ChatGPT answers, unedited

If your goal is “did AI answers about our brand get more negative after the event?”, I’d build a lightweight AI-answer sentiment drift monitor rather than rely on conventional social-listening sentiment.

Recommended solution

Use a fixed prompt panel + scheduled answer collection + aspect-level LLM grading + before/after analysis.

1. Establish a baseline

Before the launch/crisis, create 50–200 representative prompts such as:

• “What are the best products for X?”
• “Is [brand] reliable?”
• “What are the drawbacks of [brand]?”
• “How does [brand] compare with competitors?”
• “Should a company buy [product]?”
• Crisis-specific questions: “What happened with [brand]?” / “Is [brand] trustworthy after [incident]?”

Run each prompt repeatedly across the AI systems/models you care about. Repetition matters because AI responses can vary.

2. Score the answer, not just the mention

I'd use a structured grader with dimensions like:

This is preferable to simple positive/negative keyword counting because AI answers often express sentiment through hedging, confidence, comparisons and framing rather than explicit emotional language. Asky

LLM-based graders are practical for this: current evaluation tooling supports both label-based and score-based grading, including custom criteria. OpenAI Platform OpenAI Developers

3. Track the delta around the event

Define:

• T−30 to T−1: baseline
• T0: launch/crisis
• T+1 to T+7: immediate reaction
• T+8 to T+30: stabilization/recovery

For every prompt/model pair, calculate:

Sentiment shift = post-event score − baseline score

But don't collapse everything into one number. I'd show the distribution by:

• model
• prompt category
• geography/language
• product
• sentiment dimension
• narrative/theme

A useful dashboard might look like:

AI Brand Perception

Overall sentiment: +0.18 → −0.06

Trust: 4.1 → 3.2

Crisis association: 0.8 → 3.7

Negative answers: 12% → 31%

Biggest shift: “customer support” prompts

Most frequently emerging narrative: “uncertainty about reliability”

4. Preserve the actual answers

This is important for PR/crisis work. Don't just store scores.

Store:

timestamp → model → prompt → answer → cited sources → scores → extracted narratives

Then your dashboard can answer “why did sentiment move?”, rather than merely reporting that it moved.

Current AI-evaluation practice also emphasizes continuously evaluating real outputs after launch and keeping humans involved in auditing automated graders. OpenAI OpenAI

5. Add anomaly alerts

I'd trigger an alert when, for example:

• sentiment falls >10% from the rolling baseline
• negative-answer share doubles
• a new negative narrative appears in ≥10% of answers
• a competitor starts being mentioned substantially more often
• a particular model changes disproportionately
• crisis-related language persists beyond a defined recovery window

The alert should include the 5–10 answers responsible for the shift, not just “sentiment decreased.”

What I'd use

For a team building this internally, my preferred architecture would be:

Prompt registry → model/API runner → response warehouse → LLM graders → statistical drift detection → dashboard/Slack alert

You don't necessarily need a specialized “AI sentiment” SaaS product. Existing evaluation infrastructure can provide much of the grading layer; OpenAI's evaluation tooling, for example, supports reusable evaluation criteria, multiple grader types, and repeated runs. OpenAI Evals OpenAI Platform

If you want the fastest route to production, though, there are now products explicitly offering before/after AI-answer sentiment tracking and model/prompt-level drilldowns. ChatBakers Profound Help Center

My key recommendation: treat this as “AI perception monitoring” rather than ordinary sentiment analysis. The most valuable signal after a crisis isn't simply positive vs. negative; it's which narratives AI systems are adopting, how confidently they're expressing them, and whether those narratives persist across models and over time.

I’d use an LLM observability + longitudinal eval pipeline, with Arize Phoenix as a strong starting point. It supports tracing, evaluations, datasets, and experiments, which lets you compare answer behavior before/after an incident or launch. GitHub Arize AI

Track:

• Sentiment: positive/neutral/negative distribution and mean score.
• Emotion/frustration: especially useful during PR crises.
• Topic slices: brand, product, pricing, support, competitors, etc.
• Answer quality: helpfulness, factuality, and policy adherence alongside sentiment.
• Time: establish a pre-event baseline, then plot rolling post-event changes.

For each release/crisis, freeze a representative prompt set and re-run it against the old and new model/prompt. Alert on statistically meaningful distribution shifts rather than individual negative answers. Production sampling + scoring + distribution monitoring is a common pattern for detecting LLM behavior changes. oasium.ai

arize.com is particularly useful if you want an open-source starting point.

If the specific goal is “How do AI assistants’ answers about our brand change after a PR crisis or product launch?”, I’d use a dedicated AI-answer reputation monitor, rather than relying only on conventional social listening.

Recommended setup

1. Build a fixed prompt panel

Create 50–200 repeatable prompts representing what customers, journalists, and prospects might ask:

• “Is [Brand] worth buying?”
• “What are the biggest problems with [Product]?”
• “Compare [Brand] with [Competitor].”
• “Is [Brand] trustworthy after the recent controversy?”
• “What do users think of the new [Product]?”
• “Should I switch from [Competitor] to [Brand]?”

Run the exact same prompts against ChatGPT, Claude, Gemini, Perplexity, etc. on a schedule.

2. Track more than positive/negative sentiment

For each answer, capture:

• Sentiment: positive / neutral / negative
• Sentiment intensity
• Brand recommendation: recommend / mixed / avoid
• Key narratives mentioned
• Competitors mentioned
• Factual inaccuracies
• Whether the crisis/launch is mentioned
• Prominence of the negative or positive claim
• Overall answer change versus baseline

This is important because an answer can remain “neutral” while becoming substantially worse—for example, moving from “a solid product” to “a product with several concerning complaints.”

Dedicated AI-reputation products such as Astiva are explicitly built around this use case: it monitors sentiment across multiple AI platforms, captures the actual responses, and supports before/after measurement around launches and PR events. Astiva AI

3. Establish a pre-event baseline

Ideally collect 2–4 weeks of answers before the launch/crisis.

Then create an event window such as:

Your key metric could be a Sentiment Shift Score:

Post-event sentiment − baseline sentiment

But I'd also track narrative shift, because that often matters more than the aggregate score.

4. Add conventional social/media monitoring

AI answers are downstream from the information ecosystem. So pair the AI-answer monitor with social/news/review monitoring.

For example, Hootsuite currently offers crisis monitoring that tracks sentiment and media volume and can alert when conversation or sentiment shifts. Hootsuite EyeOut similarly combines web/news/social/review monitoring with sentiment and anomaly detection. EyeOut

That lets you build a useful causal-ish chain:

Event → public conversation → media coverage → AI answers → customer perception

What I'd actually implement

For a PR/product team, my preferred architecture would be:

AI answer monitor + social listening + lightweight analytics warehouse

Every day:

1. Execute the standardized prompt set across the major AI assistants.
2. Store the raw answers.
3. Score sentiment, recommendation, narratives, and factual accuracy.
4. Compare against the pre-event baseline.
5. Detect statistically/significantly unusual shifts.
6. Alert PR/product teams when a meaningful narrative changes.
7. Produce a weekly “AI perception” report with the exact answer excerpts responsible for the movement.

The most important design choice is keeping the prompts, model versions, sampling frequency, and scoring methodology stable. Otherwise you can mistake model changes or prompt variation for a genuine reputation shift.

If you want a buy-vs-build recommendation, I'd shortlist Astiva for AI-answer monitoring, then pair it with a broader social-listening/crisis platform if you also need conventional PR monitoring. Astiva AI Hootsuite

Your key metric could be a Sentiment Shift Score:

Post-event sentiment − baseline sentiment

But I'd also track narrative shift, because that often matters more than the aggregate score.

4. Add conventional social/media monitoring

AI answers are downstream from the information ecosystem. So pair the AI-answer monitor with social/news/review monitoring.

For example, Hootsuite currently offers crisis monitoring that tracks sentiment and media volume and can alert when conversation or sentiment shifts. hootsuite.com EyeOut similarly combines web/news/social/review monitoring with sentiment and anomaly detection. eyeout.com

That lets you build a useful causal-ish chain:

Event → public conversation → media coverage → AI answers → customer perception

What I'd actually implement

For a PR/product team, my preferred architecture would be:

AI answer monitor + social listening + lightweight analytics warehouse

Every day:

Yes. For this use case, I’d recommend AI-answer monitoring rather than traditional social listening alone. You want to measure not just what people say about the company, but how ChatGPT, Gemini, Perplexity, Claude, etc. subsequently describe it.

Recommended setup

1. Establish a pre-event baseline

For 2–4 weeks before the launch/crisis, run a fixed set of prompts such as:

• “What do you think of [brand]?”
• “Is [product] worth buying?”
• “What are the biggest criticisms of [brand]?”
• “Compare [brand] with [competitor].”
• “What happened with [crisis/topic]?”
• “Would you recommend [product]?”

Capture the complete answer, not just a sentiment score. Current AI-monitoring products such as Profound, Astiva, and LLM Pulse specifically support historical sentiment tracking and/or the underlying AI responses. Profound Astiva AI LLM Pulse

2. Score sentiment at several levels

I would track:

• Overall sentiment: -1 to +1
• Recommendation strength: strongly against → strongly for
• Positive/negative mention rate
• Key themes: price, quality, reliability, customer service, ethics, etc.
• Competitor sentiment gap
• Sentiment volatility, not just the average

That last metric is particularly useful during a crisis. A rising variance can indicate that AI answers are becoming polarized even before the overall average turns negative. Astiva AI

3. Create an event-centered dashboard

For a launch or PR crisis, use:

Baseline → Event → +24h → +3d → +7d → +14d → +30d

Then show something like:

Crucially, break this down by AI platform. Different engines can develop different narratives, so an aggregate score can hide an important shift. Geonimo LLM Pulse

My tool recommendation

If you want a buy rather than build solution, I'd shortlist:

• Profound — strongest fit if you're already thinking about AI/search visibility. It tracks sentiment by prompt, topic and platform and lets you drill into the sources driving positive/negative narratives. Profound
• Astiva — particularly interesting for a PR/crisis team because it explicitly supports before/after measurement around launches and crisis responses, sentiment volatility, alerts, and full AI-response capture. Astiva AI
• LLM Pulse — a simpler option focused specifically on sentiment across AI models, with five-level scoring, topic drivers and historical trends. LLM Pulse LLM Pulse

I'd pair one of those with a traditional media/social listening platform such as Meltwater if the goal is crisis intelligence rather than just AI-answer monitoring. Traditional tools remain useful for detecting the original conversation that eventually gets reflected in AI answers. Meltwater

The important part: don't treat the sentiment score as the KPI

The most valuable output is:

“AI sentiment fell 18 points because 43% of answers now mention the pricing controversy, citing these six sources.”

That lets PR answer three separate questions:

1. Did AI perception change?
2. What narrative caused the change?
3. Which sources are causing AI to adopt that narrative?

That's much more actionable than simply reporting “sentiment is down.”

If you're building this internally, I can also sketch a concrete architecture + dashboard schema for tracking this across ChatGPT, Claude, Gemini and Perplexity.

If your goal is specifically to detect how the tone of AI answers changes after a PR crisis or product launch, I’d recommend a continuous “AI answer sentiment drift” pipeline, rather than relying on ordinary social-listening sentiment alone.

Recommended stack

1. Capture a fixed prompt panel

Create 100–500 representative questions people are likely to ask AI about your company/product, for example:

• “Is [product] safe?”
• “What happened with [company]?”
• “Would you recommend [product]?”
• “What are the problems with [company]?”
• “How does [product] compare with competitors?”
• “What do customers think about it?”

Run the exact same prompts against the major AI systems you care about on a recurring schedule. Save the prompt, answer, model, timestamp, and relevant context/search conditions.

This gives you a longitudinal dataset rather than a one-off benchmark.

2. Score more than just positive/negative

For each answer, track dimensions such as:

This matters because a crisis might not make an answer overtly “negative.” It may instead shift from “good product” → “good product, but there are concerns about…”, which is often the more useful signal.

3. Use an observability/evaluation layer

My strongest recommendation would be langfuse.com as the production layer. It supports tracing, datasets, evaluations, custom scores, experiments, and production monitoring, including categorical scores such as sentiment or tone. Langfuse Langfuse

For pre-launch/regression testing, pair it with promptfoo.dev. It is designed for systematic prompt/model evaluations and can be used to compare versions and detect regressions. Promptfoo

So the architecture becomes:

Prompt library → AI answer collection → Langfuse → sentiment/factuality evaluators → time-series database → dashboard/alerts

4. Establish a pre-event baseline

This is the most important part.

For a product launch, measure at least 2–4 weeks before launch. For a crisis, preserve the most recent pre-crisis period if you already have monitoring.

Then calculate:

Sentiment shift = post-event score − pre-event baseline

I'd also calculate the rate of change, because a sudden 15-point drop over two days is qualitatively different from a gradual 15-point decline over three months.

You can visualize something like:

Brand favorability

80 ┤──────────────╮
70 ┤              │╲
60 ┤              │ ╲
50 ┤              │  ╲──────╮
40 ┤              │         ╲
30 ┤              │          ╲──
   └──────────────────────────────
       pre-event   crisis    recovery

5. Add a “narrative shift” detector

Sentiment alone won't tell your PR team why the answers changed.

Cluster the answers into themes and track their prevalence over time:

• “data privacy”
• “customer complaints”
• “pricing”
• “product reliability”
• “CEO controversy”
• “security”
• “competitor comparison”

Then you can say:

“Negative sentiment increased 18 points, primarily because references to product reliability rose from 12% to 47% of answers.”

That's much more actionable than “sentiment went down.”

Crisis-specific sentiment analysis is particularly tricky because sarcasm, ambiguous language, domain terminology, and noisy social content can cause conventional sentiment models to misread the situation. Frontiers

6. Separate AI-answer sentiment from public sentiment

I'd keep two datasets:

A. Public conversation

• Reddit
• social posts
• news
• reviews
• forums

B. AI answers

• ChatGPT
• Claude
• Gemini
• Perplexity
• other relevant assistants

Then correlate them.

For example:

Public sentiment: -8%
AI-answer sentiment: -24%
Dominant new narrative: product safety

That tells you something much more interesting: AI answers may be amplifying the crisis beyond the underlying public conversation.

Event-centric sentiment research is increasingly treating sentiment as a time series rather than a static classification, which fits this use case well. AI Models

7. Set alerts around changes, not absolute scores

I'd trigger an alert when:

• sentiment drops >10 points vs. baseline
• trust drops >10 points
• negative-theme frequency doubles
• a new crisis narrative appears in >5% of answers
• recommendation language changes materially
• factual inaccuracies about the crisis appear
• one AI platform diverges sharply from the others

And keep human review for major alerts. LLM-based sentiment/evaluation is useful at scale, but nuanced crisis language deserves calibration against human judgments. Langfuse

What I'd build

If this were an internal PR/brand intelligence product, my MVP would be:

Prompt registry → daily automated queries → raw answer store → LLM judge → Langfuse → Postgres/BigQuery → Grafana/Metabase dashboard → Slack/email alerts

The dashboard would have four primary views:

1. Sentiment over time
2. AI platform comparison
3. Narrative/theme emergence
4. Representative answer changes — showing the actual before/after answers that caused the metric to move

That last view is crucial: executives need to see “what AI is saying differently now”, not just a line chart.

If you're choosing a vendor/product rather than building this internally, I can also give you a shortlist of 5–7 existing tools for AI-answer/brand monitoring, with which ones are best for PR crisis tracking vs. product-launch measurement.

For a product launch, measure at least 2–4 weeks before launch. For a crisis, preserve the most recent pre-crisis period if you already have monitoring.

Then calculate:

Sentiment shift = post-event score − pre-event baseline

I'd also calculate the rate of change, because a sudden 15-point drop over two days is qualitatively different from a gradual 15-point decline over three months.

You can visualize something like:

Brand favorability

80 ┤──────────────╮
70 ┤              │╲
60 ┤              │ ╲
50 ┤              │  ╲──────╮
40 ┤              │         ╲
30 ┤              │          ╲──
   └──────────────────────────────
       pre-event   crisis    recovery

5. Add a “narrative shift” detector

Sentiment alone won't tell your PR team why the answers changed.

Cluster the answers into themes and track their prevalence over time:

• “data privacy”
• “customer complaints”
• “pricing”
• “product reliability”
• “CEO controversy”
• “security”
• “competitor comparison”

Then you can say:

“Negative sentiment increased 18 points, primarily because references to product reliability rose from 12% to 47% of answers.”

That's much more actionable than “sentiment went down.”

Crisis-specific sentiment analysis is particularly tricky because sarcasm, ambiguous language, domain terminology, and noisy social content can cause conventional sentiment models to misread the situation.

6. Separate AI-answer sentiment from public sentiment

I'd keep two datasets:

A. Public conversation

• Reddit
• social posts
• news
• reviews
• forums

B. AI answers

If your goal is specifically to track how AI assistants’ answers about your brand change after a PR crisis or product launch, I’d use a dedicated AI-answer sentiment monitoring layer, rather than traditional social-listening software alone.

My recommendation

Best fit: Brandi AI Sentiment Hub if you want an off-the-shelf PR/brand-intelligence product. It is explicitly designed to track how AI answer engines describe a brand, including sentiment, competitors, buyer questions, and the third-party sources influencing those answers. It also supports tracking changes over time at the source/link level. PR Newswire Brandi AI

A good alternative is Diploria AI if your priority is a simpler PR-team workflow: it tracks AI answers across engines, classifies them as recommended/neutral/negative, and identifies the sources behind the perception. Diploria AI

How I'd set it up

Create a pre-event baseline and then continuously run a fixed prompt set against the major AI answer engines.

For example:

• Brand perception: “What do you think of [Brand]?”
• Crisis-specific: “What happened with [Brand] and [incident]?”
• Trust: “Is [Brand] trustworthy?”
• Product: “What are the best alternatives to [new product]?”
• Purchase intent: “Should I buy [product]?”
• Competitive: “[Brand] vs. [Competitor]”
• Safety/reliability: “What are the biggest concerns with [Brand/product]?”

Track each answer by:

The important part is not just a single sentiment score. You want to preserve the actual answers and identify what caused the shift. A four-point sentiment decline is much less useful than knowing, for example, “negative sentiment increased because 63% of answers now mention the pricing controversy.”

Add an automated alert layer

I'd set alerts around change from baseline, rather than absolute sentiment:

• 🔴 Sentiment drops >10% over 24 hours
• 🔴 Negative answers double versus baseline
• 🟠 A new crisis-related narrative appears in >20% of answers
• 🟠 A competitor begins appearing substantially more often
• 🟡 A previously corrected claim reappears
• 🟢 Positive/recommended answers recover to baseline

For a serious crisis, sample several times per day; for a normal product launch, daily monitoring is probably sufficient.

If you want to build it yourself

A relatively clean architecture is:

Prompt registry → AI engines → response archive → LLM sentiment/aspect grader → time-series DB → dashboard/alerts

I'd use aspect-based sentiment, rather than one overall sentiment label. For example:

Brand:        -0.10
Product:      +0.25
Pricing:      -0.42
Trust:        -0.31
Customer svc: -0.05
Innovation:   +0.18

Then compare those vectors against your pre-event baseline.

For the grading layer, an LLM judge is reasonable, but keep a human audit sample. OpenAI's current evaluation guidance similarly recommends model-based graders for scalable evaluation while keeping domain experts involved to audit the graders. developer-openai-com.sitemirror.store openai.com

One important distinction: don't conflate sentiment of public coverage with sentiment of AI answers. The latter is a downstream representation of whatever information the model retrieves or has learned, so you should track the answer + cited sources + narrative together.

If you tell me whether you're looking for an off-the-shelf SaaS tool or a system you can build internally, I can give you a concrete recommended stack and dashboard schema.

For the grading layer, an LLM judge is reasonable, but keep a human audit sample. OpenAI's current evaluation guidance similarly recommends model-based graders for scalable evaluation while keeping domain experts involved to audit the graders.

Yes. For a PR crisis or product launch, I’d recommend treating this as longitudinal AI-behavior monitoring, not just generic sentiment analysis.

Recommended solution

Use an LLM evaluation/observability platform + a fixed “sentiment benchmark” + production sampling.

A strong implementation would look like:

What I'd choose

For a team that wants something relatively turnkey, I'd shortlist:

• Evidently — particularly attractive if your central requirement is custom sentiment/tone/drift monitoring. Evidently AI
• MLflow — good if you want an engineering-oriented evaluation + tracing stack and already use ML infrastructure. MLflow AI Platform
• Braintrust / Arize / W&B — worth evaluating if you need a broader enterprise LLM observability/evaluation platform connecting production traces, experiments and human feedback. Braintrust Arize AI Weights & Biases

My preferred architecture:
AI answers → trace/log → PII redaction → sentiment/tone judge → topic classifier → time-series store → dashboard → drift alerts → human review

The important design decision is to freeze the benchmark and snapshot the model/prompt version around the launch. Otherwise, six weeks later you won't know whether the sentiment shift came from the PR event, changing user questions, a prompt change, or the model provider changing the model underneath you. flowscope

If you're building this for a specific company/product, I can also sketch a concrete architecture and KPI dashboard (including suggested thresholds and an example data schema).

A good solution is to treat AI answer sentiment as a separate reputation channel from traditional social listening. After a PR crisis or product launch, you want to know not only what people are saying, but how AI systems are framing your brand when users ask about it. Promptwatch Built In

Recommended approach: AI Answer Sentiment Monitoring Stack

1. Build a prompt monitoring library

Create a fixed set of prompts that represent customer, journalist, and buyer questions:

Examples:

• “Is [brand] trustworthy?”
• “What are the problems with [product]?”
• “Should I buy [product] vs competitors?”
• “What happened with [crisis topic]?”
• “What do customers think about [feature]?”

Run these prompts daily or several times per day across major AI assistants.

Track:

• Sentiment score (positive / neutral / negative)
• Narrative themes
• Competitor mentions
• Sources cited by the AI
• Changes in wording or recommendations

Tools focused on AI answer monitoring, such as Profound and similar AI visibility platforms, are designed around tracking how brands are represented in generated answers rather than only tracking web mentions. Profound

2. Create a “sentiment shift index”

Instead of a single sentiment number, use a dashboard like:

This makes it easier for PR teams to show impact and recovery.

3. Separate short-term crisis signals from long-term perception

Monitor different time windows:

• 0–72 hours after launch/crisis

• Negative narrative spikes
• Misinformation
• New AI-generated summaries
• 1–4 weeks

• Whether corrective messaging changes AI responses
• Whether citations shift toward new sources
• Quarterly

• Brand positioning
• Competitive comparison trends

Some AI sentiment platforms specifically track prompt-level trends and connect shifts to campaigns, launches, and news events. LLM Pulse

4. Connect AI sentiment to source analysis

When sentiment drops, find the cause:

Example:

AI answer changed from “innovative and reliable” to “expensive and controversial.”

Investigate:

• Which news articles are being cited?
• Are review sites driving the shift?
• Are forums dominating retrieval?
• Did a competitor publish comparison content?

This lets communications teams act on the cause rather than just the score. Profound

5. Add alerts for meaningful changes

Avoid alerts for every negative answer. Trigger when:

• Negative sentiment increases >10 percentage points
• A new crisis narrative appears in multiple models
• Competitors replace your brand in recommendations
• A high-volume prompt changes tone

For broader reputation monitoring, social/web listening tools can complement AI-answer monitoring by catching upstream conversations that may later influence AI outputs. EyeOut Hootsuite

Practical tool choices

• Enterprise PR team: Combine an AI-answer monitoring platform with an existing media intelligence suite.
• Growth/product marketing team: Use an AI visibility platform that tracks prompts, citations, and sentiment trends.
• Engineering-heavy team: Build an internal pipeline: - Prompt scheduler
• LLM response collector
• Sentiment classifier
• Topic clustering
• Time-series dashboard

A strong internal architecture would look like:

Prompt Library
      ↓
AI Model Response Collection
      ↓
Sentiment + Narrative Extraction
      ↓
Change Detection
      ↓
PR/Product Alerts
      ↓
Response Actions

For a product launch, I would start tracking 2–4 weeks before launch to establish a baseline, then continue for at least 6–8 weeks afterward so you can distinguish launch noise from a lasting reputation shift.

Yes. I’d recommend building a “Sentiment Drift Monitor” rather than relying on ordinary conversation analytics.

The key idea is to repeatedly ask a fixed, representative set of questions about your company/product, then compare the tone and framing of AI answers before, during, and after the PR crisis or launch.

Recommended setup

The evaluation layer

For the actual scoring, I'd use an LLM-as-judge rubric supplemented by human review. Modern eval systems support label graders and numerical score graders, and can combine multiple graders into a single evaluation. OpenAI Platform OpenAI Developers

For example, every answer could be evaluated with:

sentiment: -2..+2
trust: 0..100
recommendation: 0..100
concern: 0..100
crisis_association: 0..100
dominant_narrative: string
confidence: 0..1

Keep a human-annotated validation set of perhaps 200–500 answers. That's important because automated sentiment judgments can drift themselves. OpenAI's evaluation guidance also recommends expert/human annotations as a strong basis for evaluation, with automated graders used to scale the process. OpenAI for Developers

What I'd put on the dashboard

AI Reputation Score

72 → 64 → 58 → 61 → 67

Then underneath:

• Sentiment: ↓ 14%
• Trust: ↓ 21%
• Crisis mentions: ↑ 4.8×
• Positive recommendations: ↓ 18%
• Recovery: +9 points since official response
• Most persistent negative narrative: “product reliability”

And, critically, a prompt-level drilldown showing exactly which questions changed and representative answer excerpts.

Architecture

Prompt library → model APIs → answer archive → evaluator → time-series database → dashboard/alerts

I'd also preserve the raw answers permanently so you can distinguish a genuine reputation shift from a model update or prompt/context change.

For an implementation based on OpenAI's tooling, Evals can run the same evaluation across datasets and versions, while graders can assign labels or scores to outputs. OpenAI Platform OpenAI Platform

My preferred approach: treat this as brand-perception observability for AI, with a pre-crisis baseline, fixed prompt panel, multidimensional scoring, narrative clustering, and model/version-aware time series. That gives you something much more actionable than a generic sentiment dashboard.

A strong solution is to build an AI Answer Reputation Monitoring system that treats LLM responses as a new PR channel—similar to how teams monitor news, social media, and reviews today.

The goal is not just “sentiment analysis,” but tracking how the narrative changes in AI-generated answers before, during, and after a crisis or launch. Tools in this emerging category monitor how brands are described across AI assistants and answer engines, including sentiment, citations, and framing. Promptwatch Slate

Recommended approach: AI Narrative Shift Monitor

1. Create a prompt benchmark library

Before the launch or crisis, define the questions customers, journalists, investors, and competitors are likely to ask.

Examples:

• “Is [Product] worth buying?”
• “What are the problems with [Brand]?”
• “Is [Company] trustworthy?”
• “What are alternatives to [Product]?”
• “Why are customers unhappy with [Brand]?”

Run these prompts regularly across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Other relevant AI answer engines

Track the exact answers, not just scores. AI reputation tools increasingly focus on prompt-level monitoring because an LLM’s framing can differ from traditional social sentiment. BigSentiment

2. Measure sentiment + narrative dimensions

Avoid a single positive/negative score. Track:

A useful dashboard would show:

“Before launch: 72% positive AI framing”
“7 days after launch: 41% positive, driven by battery complaints and pricing concerns.”

3. Add crisis detection rules

Set alerts for:

• Sentiment drop >15 points week-over-week
• New negative themes appearing in >20% of answers
• Competitor displacement (“users should consider X instead”)
• AI repeating a misleading claim
• Major source changes (new articles/reviews influencing answers)

4. Combine AI-answer monitoring with traditional listening

Use two layers:

Layer A: What AI says

• AI-generated answers
• Recommendations
• Summaries
• Citations

Layer B: What feeds AI

• News
• Reddit/forums
• Reviews
• Social discussions
• Customer feedback

This matters because fixing the AI narrative often requires changing the underlying information ecosystem, not just monitoring outputs. Promptwatch Diploria AI

Tool options

Enterprise PR/comms stack

Good for large brands with existing monitoring workflows:

• Meltwater — useful if you already use media monitoring and want AI-generated answer analysis layered into PR workflows. Promptwatch
• Brandwatch — strong for broad social, media, and consumer sentiment monitoring; pair it with an AI-answer tracker because traditional listening does not directly show what LLMs say. Promptwatch

AI-native monitoring

Better when the question is specifically: “What will ChatGPT tell customers?”

• Promptwatch — focused on tracking AI platform responses, sentiment, and visibility. Promptwatch
• Diploria AI — focuses on AI answers, sentiment trends, cited sources, and reputation monitoring workflows. Diploria AI

Suggested operating cadence

Pre-launch

• Establish a baseline from 100–500 representative prompts
• Capture screenshots/responses
• Identify existing negative narratives

Launch week

• Monitor daily
• Track new themes
• Compare AI answers against media/social sentiment

Post-crisis recovery

• Weekly narrative scorecard: - sentiment trend
• top negative themes
• changed citations
• competitor movement
• recommended corrective actions

For most PR teams, I’d implement a hybrid stack: an AI-answer monitoring tool for “what models say” plus a traditional listening platform for “why they say it.” That gives you early warning and a path to influence the narrative. Promptwatch BigSentiment

Yes. I’d treat this as an “AI reputation observatory” rather than conventional social sentiment monitoring.

The key distinction is that you want to measure how AI systems describe your company/product, not merely what people say about it. Tools in this category now track prompts, mentions, sentiment, citations, competitors, and answer changes across ChatGPT, Gemini, Claude, Perplexity, etc. Promptwatch BigSentiment

Recommended setup

1. Build a fixed “reputation prompt set”

Create ~100–300 questions representing what customers, journalists and prospects might ask AI after the crisis/launch:

• “Is [Company] trustworthy?”
• “What happened with [crisis]?”
• “Should I buy [Product]?”
• “What are the problems with [Product]?”
• “What alternatives to [Product] should I consider?”
• “Which is better: [Product] or [Competitor]?”
• “Is [Company] good for enterprise customers?”

Run the same prompts across the major AI platforms on a schedule.

Important: preserve the exact prompt set and methodology. Otherwise, a change in the questions can look like a sentiment shift when it isn't.

2. Capture the entire answer, not just sentiment

For every run, store:

This is important because “positive sentiment” can conceal a serious reputation problem. For example, an answer might be polite but repeatedly associate your brand with a security incident.

3. Establish a pre-crisis / pre-launch baseline

I'd calculate a baseline over at least several days or weeks, then compare every subsequent observation against it.

For example:

AI Reputation Score: 78 → 61 (-17)
Negative association with “security” +240%
Recommendation rate: 72% → 48%
Competitor substitution: +31%

Don't rely on a single sentiment score. Recent research also highlights calibration and reproducibility as persistent challenges in LLM-based sentiment analysis. Springer

4. Add an event timeline

Put PR/product events directly on the sentiment graph:

AI sentiment
100 ┤
 80 ┤───────╮
 60 ┤       ╰──────╮
 40 ┤              ╰───╮
 20 ┤                  ╰──
    └────────────────────────
       Launch   Crisis   Fix

Then annotate:

• press release
• product launch
• outage
• negative review
• CEO statement
• remediation announcement
• major media coverage
• product update

This lets you distinguish “sentiment changed” from “sentiment changed after this event.”

5. Monitor the evidence behind the answers

This is the piece I'd consider essential after a PR crisis.

Track why models are producing the sentiment:

• news articles
• Reddit/forum discussions
• reviews
• company pages
• competitor pages
• regulatory/public documents
• social posts
• product documentation

AI-answer monitoring platforms increasingly expose the sources and citations underlying model responses, which makes this much more actionable than simply watching a sentiment number. BigSentiment SignalKit

Tool recommendation

If you're primarily a PR/marketing team, I'd start with a purpose-built AI brand-monitoring platform such as Promptwatch, Brand24, or SignalKit rather than building the whole thing yourself. Current tools can track sentiment and representation across multiple AI platforms and preserve the underlying answers for investigation. Promptwatch SignalKit Brand24

If you're an engineering/AI team monitoring your own chatbot or agent, use an observability/evaluation layer such as MLflow or LangSmith and add sentiment as a custom evaluator. Production monitoring can score live traces and feed problematic examples back into the evaluation dataset. LangChain Databricks Documentation

My preferred architecture

AI platforms → scheduled prompt runner → raw answer store → sentiment/theme evaluator → time-series database → dashboard + alerts

With alerts such as:

🚨 AI reputation shift
Negative sentiment increased 18% over 24h across 4/6 models.
Primary emerging theme: “data privacy.”
73% of new negative answers cite three articles published yesterday.

That last level of detail is what makes the system useful to a crisis-response team rather than just another dashboard.

If I were implementing this today: use a commercial AI-visibility monitor for the external AI-answer layer, but maintain your own canonical prompt set, baseline, event timeline, and raw-answer archive. That gives PR/marketing a fast solution while preserving enough data to prove whether sentiment actually shifted and why.

For a PR crisis or product launch, I’d treat this as an AI reputation monitoring + LLM evaluation problem: you want to detect whether AI assistants are becoming more negative, less accurate, or less likely to recommend your brand after an event.

A strong solution is a sentiment-shift monitoring pipeline:

1. Build a “brand prompt benchmark”

Create a fixed set of prompts that represent how customers discover and evaluate you:

Examples:

• “Is [brand] trustworthy?”
• “What are the biggest problems with [product]?”
• “Should I buy [product] after the recent controversy?”
• “Compare [brand] vs competitors.”
• “What are customers saying about [launch]?”

Run these prompts across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Copilot
• Any internal AI assistant powered by your models

Store responses daily (or hourly during a crisis).

2. Score every answer on multiple dimensions

Don’t use sentiment alone. Track:

Platforms designed for LLM evaluation and observability can help automate this type of scoring, including production tracing, regression tests, and custom evaluators. ai-evals.tools Evidently AI

3. Create a “sentiment delta” dashboard

The key metric is change over time:

Sentiment Shift = Post-event AI sentiment − Baseline AI sentiment

Example:

Then segment by:

• AI model
• Prompt category
• Region/language
• Product line
• Competitor

4. Capture “why” sentiment changed

A dashboard should not just say “negative sentiment increased.” It should cluster causes:

Example clusters:

• “Battery complaints increased”
• “Privacy concerns appearing”
• “CEO comments referenced frequently”
• “Competitor X mentioned more often”
• “Incorrect launch pricing information spreading”

Use embeddings/topic clustering on AI answers to identify emerging narratives.

5. Add human review for high-impact changes

Automatically route cases like:

• Negative sentiment increase >20%
• New safety/legal allegation appears
• AI stops recommending product
• False information appears repeatedly

A PR or product team can then review examples and decide whether to:

• publish clarifications
• update documentation
• adjust prompts/system instructions
• improve public content that AI systems learn from

Recommended stack options

Lean setup

• Prompt scheduler
• API collection of AI responses
• Sentiment/topic classifier
• Dashboard (Looker, Tableau, Grafana, etc.)

More mature AI product stack

Use an LLM observability/evaluation platform:

• braintrust.dev — combines tracing, evaluations, and production monitoring workflows. Braintrust
• evidentlyai.com — open-source monitoring and evaluation framework with support for LLM quality signals including sentiment and drift monitoring. Evidently AI
• langfuse.com — useful for teams wanting open-source LLM observability and tracing. LLM Tools

For a PR crisis specifically, I’d add:

• Baseline archive: save AI answers before the crisis.
• War room dashboard: refresh every few hours.
• Narrative tracker: top negative/positive themes.
• Competitor watch: detect if AI recommendations shift elsewhere.
• Correction tracking: measure whether AI responses recover after official statements.

The most valuable output is not “AI sentiment is down 14%.” It is: “AI assistants are now 22% less likely to recommend the product because three recurring narratives about reliability and pricing appeared after the launch.” That gives PR, marketing, and product teams something actionable.

A strong solution is to build an AI Answer Sentiment Monitoring System that treats LLM responses as a new reputation channel (alongside news, social, reviews, and search). The goal is not just “is sentiment positive or negative?” but how the AI narrative about your brand changes before, during, and after an event.

Several newer tools now focus specifically on monitoring how AI assistants describe brands, including sentiment, mentions, citations, and narrative shifts across systems like ChatGPT, Gemini, Claude, and Perplexity. Promptwatch BigSentiment

Recommended approach

1. Create a baseline before the launch/crisis

Build a “normal state” dataset:

• 50–200 representative prompts customers might ask: - “Is [brand] trustworthy?”
• “What are the problems with [product]?”
• “Should I buy [product] over competitors?”
• “What happened with [company event]?”
• Run them weekly (or daily during a crisis) across: - ChatGPT
• Claude
• Gemini
• Perplexity
• AI search experiences

Capture:

• Full answer text
• Sentiment score
• Key themes
• Competitor mentions
• Sources/citations used
• Product attributes mentioned
• Whether the answer recommends, warns, or avoids the brand

2. Track narrative shifts, not just sentiment

A useful dashboard should show changes like:

The most valuable signal is often attribute migration:

Before crisis: “innovative but expensive”
After crisis: “innovative but unreliable”

That tells PR teams what message needs correction.

3. Add event correlation

Tie sentiment movements to:

• Press releases
• Product launches
• News coverage
• Executive statements
• Regulatory events
• Social spikes

Example:

Sept 1 — Product launch
Sept 3 — Negative Reddit discussion begins
Sept 5 — AI answers begin mentioning reliability concerns
Sept 8 — Negative sentiment peaks

This helps distinguish a temporary reaction from a lasting reputation change.

4. Use an AI monitoring platform or build a lightweight stack

Enterprise / PR team options

• Brandwatch — useful if you already monitor broad reputation signals across social and media.
• Similarweb — useful for broader AI search visibility and competitive analysis. Prism News
• Brand24 — useful for sentiment tracking and alerts across public mentions. Brand24

AI-answer-specific monitoring

Platforms focused on LLM responses can track how AI systems represent brands and detect shifts in generated answers. Examples include tools such as Promptwatch, LLM Pulse, and other AI reputation monitoring products. Promptwatch LLM Pulse

5. Build an alert system

Trigger alerts when:

• Negative sentiment increases >15–20 points
• A new negative theme appears
• A competitor becomes more frequently recommended
• AI answers start repeating incorrect claims
• A high-authority source enters citations

Example alert:

“AI sentiment toward Product X dropped 24 points in 72 hours. New themes: battery failures, customer complaints. 68% of negative answers cite three review sites.”

6. Add a human review loop

Automated sentiment can miss nuance. Have a communications person review:

• Top 10 changed answers
• New negative narratives
• False claims/hallucinations
• Recommended response themes

Suggested architecture

Prompt library
      ↓
AI answer collection
      ↓
Sentiment + theme extraction
      ↓
Narrative change detection
      ↓
Dashboard + alerts
      ↓
PR response workflow

For a PR crisis or major launch, I would run this daily for the first 30 days, then reduce to weekly monitoring once sentiment stabilizes. The key metric is not “AI likes us”; it is whether the story AI tells about the brand is moving in the intended direction.

Yes. I’d treat this as “AI reputation monitoring” rather than ordinary sentiment analysis: you want to measure not just whether an answer is positive/negative, but how the narrative changes across AI assistants after a specific event.

Recommended setup

1. Establish a pre-event baseline

• Create 30–100 representative prompts customers, journalists, investors, and prospects might ask.
• Run them across ChatGPT, Claude, Gemini, Perplexity, etc.
• Save the full answer, sentiment, key claims, cited sources, competitors mentioned, and position/ranking of your brand.
• Repeat the same prompt set on a schedule.

Dedicated AI-brand monitoring products now support exactly this kind of cross-model tracking, including sentiment, citations, competitors, and historical changes. Promptwatch Nightwatch

2. Track sentiment as a multidimensional score

Don't rely on a single positive/neutral/negative label. I'd capture:

This matters because an answer can remain technically “positive” while becoming substantially less favorable.

3. Create an event-specific “crisis/launch” dashboard

Have three windows:

• Baseline: 2–4 weeks before the event
• Shock: event day through ~7 days
• Recovery: weeks 2–6+

Then calculate:

Sentiment shift = post-event sentiment − baseline sentiment

Break that down by AI model, prompt category, geography/language, and narrative/theme.

I'd also alert on changes such as:

• sentiment ↓ >10 percentage points
• negative mentions ↑ 2×
• a new high-risk claim appears
• recommendation/ranking drops
• a competitor starts replacing you in answers
• an AI begins repeating a particular negative narrative

Some current monitoring products already use threshold-based alerts and theme clustering for this purpose. Sightlabs LLM Metrix

The important part: preserve the evidence

Don't give executives a dashboard saying “sentiment fell 14%.”

Make every datapoint clickable to:

Prompt → exact AI answer → sentence about your brand → sentiment judgment → cited sources → historical comparison

That lets PR immediately answer why sentiment changed rather than debating the metric. Tools in this category increasingly expose the underlying answer and citation evidence for exactly this reason. MentionsAPI MentionFlow

If you want to build it yourself

A lightweight architecture would be:

Prompt registry → LLM APIs → response store → sentiment/claim grader → time-series DB → dashboard/Slack alerts

For the grader, use a structured rubric rather than generic sentiment:

Score the brand's portrayal from -1 to +1.

Evaluate separately:
1. Overall sentiment
2. Trust/reputation
3. Product perception
4. Likelihood of recommendation
5. Crisis-related risk
6. New negative claims

Return JSON with scores, evidence sentences,
themes, and confidence.

Model-based graders are well suited to this because they can assign labels or scores to open-ended responses; OpenAI's current evaluation tooling supports both label and score model graders. OpenAI Developer OpenAI Platform

My recommendation: if you're a PR/marketing team, start with a dedicated AI-brand monitoring platform rather than building the infrastructure. If you're building this as an internal analytics product or need proprietary prompts/data, build the evaluation layer yourself and use an existing observability/dashboard stack.

The key KPI I'd put on the executive dashboard is “AI Narrative Delta”: how the average portrayal of the brand has changed versus the pre-crisis/pre-launch baseline, with the underlying answers available for every movement.

A strong solution is to build an AI Reputation Intelligence pipeline that treats AI-generated answers as a new reputation channel—similar to monitoring social media, news, and reviews, but focused on how models describe your brand after a crisis or launch. Several tools now specifically monitor LLM answers, citations, sentiment, and narrative shifts across AI platforms. BigSentiment LLM Pulse

Recommended approach: AI Answer Sentiment Watchtower

1. Create a baseline before the event

Before a PR crisis or product launch, capture a snapshot of how AI answers describe you.

Track a fixed prompt set such as:

• “Is [Brand] a trustworthy company?”
• “What are the pros and cons of [Product]?”
• “What are alternatives to [Product]?”
• “Should I buy [Product]?”
• “What are the biggest concerns about [Brand]?”

Run these prompts weekly (or daily during a launch) across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• AI search experiences

Store:

• Exact answer text
• Model/version
• Date/time
• Citations/sources used
• Sentiment score
• Key themes
• Competitors mentioned

The key is preserving the actual answers, not just a score, so communications teams can see why sentiment moved. LLM Pulse

2. Measure sentiment shifts with more than positive/negative

A useful scoring model:

Example:

Before launch

• Sentiment: +72
• Themes: innovation, ease of use, market leader

Two weeks after launch

• Sentiment: +41
• Themes: bugs, pricing confusion, customer complaints

That tells PR and product teams the narrative changed, not just that “mentions dropped.”

3. Connect AI answers to the sources driving them

AI answers are influenced by external evidence:

• News coverage
• Reviews
• Reddit/forums
• Documentation
• Product pages
• Comparison articles

A good system should answer:

“Why did ChatGPT start describing us negatively?”

Possible finding:

• 60% of negative AI responses cite recent reviews
• A competitor comparison page ranks higher
• Old pricing information is still appearing in sources

LLM monitoring platforms increasingly combine answer tracking with citation/source analysis because the answer alone does not explain the cause. BigSentiment Slate

4. Add crisis alerts

Set thresholds such as:

• Sentiment drops >15 points week-over-week
• Negative themes increase by 25%
• A new controversy keyword appears
• AI stops recommending your product
• Competitors begin appearing more often

Alerts should route to:

• PR/comms
• Product marketing
• Customer support
• Executive team

5. Tooling options

Depending on your size:

Enterprise PR team

Use an AI reputation layer alongside existing media monitoring.

Examples:

• Meltwater — useful if you already monitor earned media and social channels; its newer AI-focused capabilities extend monitoring into LLM-generated brand discussions. Promptwatch
• Brandwatch — strong for broader social, forum, and consumer sentiment signals that may influence AI narratives. Promptwatch

AI-native brand monitoring

Better if the goal is specifically “what are AI assistants saying about us?”

Examples:

• Promptwatch — focused on monitoring brand sentiment and visibility in AI-generated answers. Promptwatch
• LLM Pulse — focused on sentiment tracking, model comparisons, and answer evidence. LLM Pulse

Build internally

For companies with data teams:

Architecture:

Prompt Scheduler
        ↓
LLM Response Collector
        ↓
Sentiment + Topic Classifier
        ↓
Trend Database
        ↓
Dashboard + Alerts
        ↓
PR Response Workflow

A simple stack:

• Prompt orchestration: Airflow / Temporal
• Storage: PostgreSQL / BigQuery
• Analysis: LLM classifier + embeddings
• Dashboard: Looker / Tableau / Grafana
• Alerts: Slack / Teams / PagerDuty

Metrics I would put on the executive dashboard

AI Reputation Score

• Composite sentiment + trust + recommendation score

Narrative Velocity

• How quickly negative themes are spreading

AI Share of Voice

• How often your brand appears vs competitors

Citation Health

• Whether AI systems rely on accurate, favorable sources

Recovery Curve

• Time from crisis impact → sentiment normalization

For a PR crisis or major product launch, I would avoid relying only on traditional social listening. The best setup is a two-layer system:

1. Traditional reputation monitoring → what people and media are saying
2. AI answer monitoring → what customers will hear when they ask an AI assistant

Together they show both the public narrative and the AI-mediated narrative. Promptwatch reputation.house

Yes. I’d recommend treating this as an AI reputation monitoring + longitudinal eval problem, rather than ordinary social sentiment analysis.

Recommended solution

Build a small monitoring pipeline that repeatedly asks the major AI assistants the same set of brand/product questions, captures the complete answers, and measures how those answers change before and after the event.

A good architecture is:

Prompt library → AI models → response store → sentiment/narrative evaluator → time-series dashboard → alerts

1. Establish a pre-event baseline

For 2–4 weeks before the PR crisis or launch, run a fixed prompt set such as:

• “What do you know about [company]?”
• “What are the best alternatives to [product]?”
• “Would you recommend [product]?”
• “What are the biggest complaints about [company]?”
• “Is [product] worth buying?”
• “[Company] vs [competitor]”
• “What happened with [controversy]?” — for a crisis
• “What do you think of [new product]?” — for a launch

Run them across the models/surfaces you care about, ideally at consistent intervals.

The important thing is not to change the prompts after the event, because you want a controlled before/after comparison.

2. Track more than a single sentiment score

I'd score every answer on at least:

This matters because a shift from “recommended” → “acceptable” can be commercially meaningful even if both are technically “positive.”

Existing AI-brand-monitoring products are already moving in this direction: for example, LLM Pulse tracks sentiment and the topics driving it, while SignalKit combines sentiment with visibility, share of voice, position, and the underlying answer. LLM Pulse SignalKit

3. Use an LLM judge, but calibrate it

Rather than relying on generic positive/negative sentiment classification, create a rubric specific to your brand.

For example:

Sentiment:
-2 = strongly damaging
-1 = mildly negative
 0 = neutral/mixed
+1 = mildly positive
+2 = strongly positive

Recommendation:
0 = not recommended
1 = conditional recommendation
2 = clear recommendation

Narrative:
- product quality
- pricing/value
- customer service
- trust
- safety
- controversy
- innovation
- competitor comparison

Have the evaluator return structured JSON so you can aggregate it over time.

This is a particularly good use case for model graders: OpenAI's current evaluation tooling supports score and label model graders, as well as combining multiple graders into a composite evaluation. OpenAI Platform OpenAI Platform

I'd still manually review a sample of evaluations. Automated graders are useful for scale, but they shouldn't be treated as ground truth; OpenAI similarly notes that automated grading can be useful while expert human grading remains more reliable in some settings. OpenAI OpenAI Evals

4. Detect the shift, not just the level

Your main dashboard should show something like:

AI Reputation Score

+0.74 → +0.61 → +0.32 → -0.08 → +0.19

with annotations:

• 🔴 PR crisis announced
• 📰 Major news coverage
• 🟢 Company response published
• 🚀 Product launched
• 📈 Review coverage increased

Then break it down by model:

                 Pre-crisis    Day 7     Day 30
ChatGPT             +0.72       +0.31      +0.48
Claude              +0.68       +0.44      +0.59
Gemini              +0.75       +0.18      +0.41
Perplexity          +0.63       -0.12      +0.27

This tells you whether the event changed AI perception broadly or only affected particular answer engines.

5. Add narrative-change detection

This is arguably more valuable than sentiment.

For example, before launch:

“Affordable option with basic functionality.”

After launch:

“Newer product with strong features, but concerns about reliability and pricing.”

Your system should identify:

New negative narrative: reliability
New negative narrative: pricing
Disappearing positive narrative: affordability
New positive narrative: feature breadth

That gives PR/product teams something actionable rather than merely saying “sentiment dropped 14%.”

6. Alert on statistically meaningful changes

Don't alert every time the score moves 0.03.

I'd use alerts such as:

• Sentiment drops >0.25 versus baseline
• Negative-answer rate doubles
• A new negative narrative appears in >10% of answers
• A factual error appears repeatedly
• Competitor recommendation rate increases >20%
• Your brand disappears from previously high-visibility prompts
• A crisis-related narrative persists for 7+ days

For a major crisis, temporarily increase monitoring from daily to several times per day.

What I'd actually buy vs. build

If you want fast deployment, I'd shortlist an AI-answer monitoring platform rather than building everything from scratch. Products such as LLM Pulse, Hootsuite's LLM Insights, SignalKit, and similar tools already offer pieces of this workflow. Hootsuite, for example, explicitly tracks how LLMs describe/recommend brands, sentiment, misinformation, competitors, and share of voice. Hootsuite

If you're a larger company with engineering resources, I'd build the evaluation layer yourself and use an API/monitoring service for collection. That gives you control over your sentiment rubric and, importantly, your definition of a “reputation shift.”

My preferred setup

Collection: ChatGPT + Claude + Gemini + Perplexity
Cadence: daily normally, 4–6× daily during a crisis
Prompt set: 50–200 stable questions
Evaluation: sentiment + recommendation + narrative + accuracy + competitor position
Storage: raw answer + model + prompt + timestamp + citations
Dashboard: baseline vs current, model-by-model and narrative-by-narrative
Alerting: Slack/email when predefined shifts occur
Human review: every alert + a weekly random sample

The key principle is: never throw away the raw AI answers. Store the exact response that produced every score. That makes the dashboard auditable and lets your PR team answer “What exactly changed?” rather than arguing about an opaque sentiment number. Tools in this category are increasingly exposing the underlying answer alongside the score for precisely this reason. SignalKit

If you're evaluating this for a PR/comms team, I'd lean toward buying an existing AI-monitoring platform. If you're building it for an internal analytics/AI team, I'd build the custom eval layer and dashboard around a monitoring API.

That gives PR/product teams something actionable rather than merely saying “sentiment dropped 14%.”

6. Alert on statistically meaningful changes

Don't alert every time the score moves 0.03.

I'd use alerts such as:

• Sentiment drops >0.25 versus baseline
• Negative-answer rate doubles
• A new negative narrative appears in >10% of answers
• A factual error appears repeatedly
• Competitor recommendation rate increases >20%
• Your brand disappears from previously high-visibility prompts
• A crisis-related narrative persists for 7+ days

For a major crisis, temporarily increase monitoring from daily to several times per day.

What I'd actually buy vs. build

If you want fast deployment, I'd shortlist an AI-answer monitoring platform rather than building everything from scratch. Products such as LLM Pulse, Hootsuite's LLM Insights, SignalKit, and similar tools already offer pieces of this workflow. Hootsuite, for example, explicitly tracks how LLMs describe/recommend brands, sentiment, misinformation, competitors, and share of voice.

If you're a larger company with engineering resources, I'd build the evaluation layer yourself and use an API/monitoring service for collection. That gives you control over your sentiment rubric and, importantly, your definition of a “reputation shift.”

My preferred setup

For a PR crisis or product launch, I’d recommend an AI-answer sentiment monitoring layer rather than relying on conventional social listening alone.

Best-fit solution: LLM answer monitoring + event-based drift detection

The setup I’d use:

1. Create a fixed prompt panel

• 50–200 prompts covering brand perception, product quality, pricing, competitors, safety, customer experience, and crisis-specific questions.
• Run the same prompts against ChatGPT, Gemini, Claude, Perplexity, and other important answer engines.
• Save the complete answers, not just sentiment scores.
• Score sentiment + narrative

• Give each answer a sentiment score, e.g. -1 to +1.
• Separately classify the reason for the sentiment: pricing, reliability, safety, customer support, product features, leadership, etc.
• Embeddings are useful here because they can cluster semantically similar narratives and detect emerging themes/anomalies. OpenAI Developers OpenAI Developers
• Measure change against a pre-event baseline

I'd track:

• Overall sentiment
• % positive / neutral / negative answers
• Sentiment by AI platform
• Sentiment by topic
• Brand recommendation rate
• Competitor sentiment gap
• Sentiment volatility — potentially more important than the average score during a crisis.

The key is to detect “sentiment moved from its normal range”, rather than simply declaring an answer negative. Existing AI-monitoring products are already using this general approach. Astiva AI Profound

4. Add a crisis/launch event layer

Put the PR event on the timeline:

Crisis → media coverage → AI-answer sentiment shift → narrative change → recovery

Then calculate the lag between the event and each model's change in answers. This lets you answer a much more useful question than “Is sentiment positive?”:

“Did the crisis/launch materially change what AI tells prospective customers about us?”

5. Alert on drift, not just negativity

For example:

• 🔴 Alert: sentiment falls >2 standard deviations from 30-day baseline
• 🔴 Alert: negative-answer share increases >15 percentage points
• 🟠 Alert: a new negative topic appears in >10% of answers
• 🟠 Alert: competitor starts being recommended significantly more often
• 🟢 Recovery: sentiment returns to baseline for 3 consecutive measurements

Buy vs. build

If you want something operational now, I'd shortlist:

• astiva.ai — particularly interesting for PR teams because it explicitly tracks sentiment volatility, captures verbatim AI answers, and supports before/after measurement around launches and crisis responses. Astiva AI
• llmpulse.ai — focuses on five-level sentiment scoring, topic drivers, historical trends, and comparisons across AI platforms. LLM Pulse
• tryprofound.com — stronger if you want to connect sentiment changes to the underlying publishers/pages/themes driving the answers. Profound
• hootsuite.com — worth considering if you already use Hootsuite and want LLM perception alongside broader social/brand monitoring. Hootsuite

My recommendation: for a serious PR/crisis program, use a tool such as Astiva or Profound as the monitoring layer, but retain the underlying raw AI answers and build your own event-study dashboard on top. The raw answers are crucial: a sentiment score tells you that perception moved; the answer tells your PR team why.

If this is for an enterprise team, I’d structure the dashboard as Baseline → Crisis/Launch → 24h/72h/7d/30d impact → Recovery, with one view per AI model and a consolidated narrative-risk score.

If your goal is specifically to track how AI-generated answers about your brand change after a PR crisis or product launch, I’d recommend an LLM-answer monitoring layer paired with traditional social/listening data.

My pick: Sprinklr + an LLM-answer benchmark

sprinklr.com is particularly relevant because it now offers LLM Insights, which is designed to monitor how brands appear in AI-generated answers—including visibility, sentiment, recommendations, and competitive positioning. Sprinklr Investors

I’d implement it like this:

1. Create a fixed prompt panel

• 50–200 questions customers, journalists, and prospects might ask AI about your company/product.
• Include neutral, positive, negative, comparison, and crisis-related prompts.
• Example: “Is [Brand] trustworthy?” or “What are the biggest problems with [Product]?”
• Establish a pre-event baseline

• Run the same prompts across the major LLMs before the launch/crisis.
• Record sentiment, factual claims, recommendations, competitors mentioned, and whether your brand is cited.
• Run the panel continuously after the event

• Daily during the first 1–2 weeks.
• Then weekly once things stabilize.
• Compare each answer against the baseline rather than relying on a single sentiment score.
• Track a small set of metrics

5. Correlate AI shifts with external conversation

Use Sprinklr's social/listening data alongside the LLM panel. Its listening product can monitor sentiment, emotions, trends, and anomalies across 30+ digital/social channels, which lets you distinguish an AI-answer problem from a broader public-perception problem. Sprinklr Sprinklr

One important design choice

Don't reduce the measurement to “positive/negative sentiment.” For a PR crisis, I'd have an evaluator classify every AI answer into:

Sentiment + narrative + factuality + recommendation + severity

For example:

Sentiment: Negative
Narrative: Product safety concerns
Factuality: Partially inaccurate
Recommendation: Avoid
Severity: High

That gives your PR/product team something actionable rather than simply saying “sentiment dropped 12%.”

Alternative

brandwatch.com is a strong choice if your primary need is consumer/social sentiment and crisis monitoring. It offers real-time sentiment analysis, historical data, anomaly alerts, and AI-powered analysis across large volumes of online conversation. brandwatch.com brandwatch.com

Bottom line: for your exact use case, I'd choose Sprinklr LLM Insights + a controlled longitudinal prompt panel + social listening. The key is measuring changes in the answers to identical questions over time, not just measuring generic internet sentiment.

For a PR crisis or product launch, I’d recommend a continuous “AI reputation eval” pipeline rather than a conventional social-listening dashboard. The key is to track how AI systems actually answer questions about your brand, not just sentiment in social posts.

Recommended solution: AI Answer Sentiment Monitor

1. Build a fixed “brand question set”

Create ~50–200 prompts covering:

• Brand perception: “What do people think about X?”
• Product quality: “Is X reliable?”
• Safety/privacy concerns
• Pricing and value
• Competitor comparisons
• Crisis-specific questions
• Product-launch questions
• “Should I buy/use X?”
• Questions likely to surface the negative narrative

Keep the prompts identical across measurement periods so you can distinguish genuine shifts from changes in the questions.

2. Probe the major AI answer engines

Run the question set against the models/platforms that matter to your audience, and save:

• Exact prompt
• Full answer
• Timestamp
• Model/platform/version, when available
• Citations/sources returned
• Sentiment score
• Topics/aspects mentioned
• Positive/negative claims
• Whether the answer recommends or discourages the brand

This is essentially an eval problem: current evaluation tooling supports running the same evaluation against different models and using graders to assign labels or scores. OpenAI Developers OpenAI Developers

3. Don't use one “sentiment score”

I'd use an AI Reputation Scorecard with separate dimensions:

That lets you distinguish “sentiment fell because people are discussing the outage” from “sentiment fell because AI answers now characterize the company as unreliable.”

4. Establish a pre-crisis baseline

Before the launch/crisis, save at least 2–4 weeks of observations.

Then calculate:

Sentiment shift = current score − baseline score

But also track the rate of change and confidence interval. A drop from 0.62 → 0.58 might be noise; 0.62 → 0.21 across multiple models is a much stronger signal.

A useful operational design is to monitor the same standardized prompt set weekly, then increase cadence during an active crisis or launch. rhinegold — Insights

5. Add an “explain the shift” layer

When the score changes materially, automatically retrieve the answers responsible for the movement and classify why.

For example:

AI sentiment: −18% this week

Primary driver: “data privacy” narrative
Models affected: 4/5
New negative claim appearing: “Company X shares customer data…”
Source appearing in 63% of affected answers: [news article]
First detected: Aug. 27
Confidence: High

This is much more actionable for PR than a graph saying “sentiment = 43.”

There are already products explicitly oriented around this workflow—e.g. model-level sentiment tracking with the underlying AI conversations surfaced for investigation. ChatBakers LLM Pulse

6. Use an LLM grader, but keep humans in the loop

I'd have a grader score each answer against a rubric such as:

Sentiment:
-1 = strongly negative
 0 = neutral/mixed
+1 = strongly positive

Trust:
0–100

Does the answer:
- mention the crisis?
- repeat a disputed claim?
- recommend the product?
- cite authoritative evidence?
- characterize the company as unsafe/unreliable?

Then have humans audit a sample of the grader's judgments. This matters because automated graders can scale evaluation, but they aren't a substitute for expert review. OpenAI OpenAI

The dashboard I'd give PR leadership

I'd make the top-level dashboard extremely simple:

AI Brand Health

• 🟢 Overall AI sentiment: +0.47 ↑ 6%
• 🔴 Negative narrative rate: 18% ↑ 41%
• 🟡 Recommendation rate: 64% ↓ 8%
• 🔴 Crisis mentions: 37% ↑ 112%
• 🟢 Positive/neutral answers: 71%

Then below it:

What changed?

1. Privacy concerns — +28%
2. Product reliability — +17%
3. Customer support — +11%

And finally:

Show me the answers that caused the change.

That's the crucial part. Every alert should have the actual AI answers and their citations attached, rather than forcing the communications team to trust an opaque score.

My preferred architecture

Prompt registry → AI platforms → response store → LLM/human grading → time-series database → drift detection → Slack/email dashboard

I'd also maintain a frozen baseline eval plus a continuously refreshed sample. Evaluation guidance increasingly emphasizes testing systems under realistic conditions and continuously measuring real outputs after launch, rather than treating evaluation as a one-time pre-launch exercise. OpenAI

If you're building this internally, OpenAI Evals is a reasonable foundation for the evaluation/grading layer; its current API supports reusable evals, multiple graders, and running evaluations against different models/configurations. OpenAI Developers

Bottom line: for a crisis, don't ask “Is our brand sentiment down?” Ask “Which AI systems changed their answers about us, which narratives caused the change, what sources are driving those answers, and did the change persist?” That's the monitoring system I'd build.

For example:

AI sentiment: −18% this week

Primary driver: “data privacy” narrative
Models affected: 4/5
New negative claim appearing: “Company X shares customer data…”
Source appearing in 63% of affected answers: [news article]
First detected: Aug. 27
Confidence: High

This is much more actionable for PR than a graph saying “sentiment = 43.”

There are already products explicitly oriented around this workflow—e.g. model-level sentiment tracking with the underlying AI conversations surfaced for investigation. chatbakers.com llmpulse.ai

6. Use an LLM grader, but keep humans in the loop

I'd have a grader score each answer against a rubric such as:

Yes. I’d recommend treating this as an AI-perception monitoring layer, separate from conventional social/news sentiment monitoring.

Recommended solution: AI Answer Sentiment Monitor

The core workflow should be:

1. Build a fixed prompt panel

• 50–200 questions that customers, journalists, investors, and prospects are likely to ask about your brand.
• Include neutral queries (“What does X do?”), comparison queries (“X vs. competitor”), skeptical queries, and crisis-specific questions.
• Keep the exact prompts versioned so you can compare apples-to-apples over time.

2. Snapshot AI answers before the event
Run the panel against ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, etc., and establish a pre-launch/pre-crisis baseline.

For every answer, capture:

• Sentiment toward the company/product: -1 to +1
• Positive/negative/neutral claims
• Key topics and allegations
• Brand/product attributes
• Competitors mentioned
• Sources/citations used
• Whether the answer repeats outdated or incorrect claims
• Exact response text

This is increasingly becoming a distinct brand-intelligence category: for example, Brandi AI's Sentiment Hub and Sprinklr's LLM Insights are specifically aimed at measuring how brands are represented inside AI-generated answers. PR Newswire Sprinklr

3. Create a “sentiment shift” score

Rather than just reporting today's sentiment, calculate:

Shift = current sentiment − baseline sentiment

Then segment it by:

• AI platform
• Prompt/question
• Product
• Geography, if relevant
• Audience/use case
• Topic
• Competitor

For example:

That gives your PR team something much more actionable than “AI sentiment is down.”

4. Add change detection and alerts

I'd alert when there's a statistically meaningful change rather than every small fluctuation.

For example:

• 🔴 Sentiment drops >20% from 7-day baseline
• 🔴 A new negative narrative appears in >10% of answers
• 🔴 A previously corrected allegation returns
• 🟠 Competitor recommendations increase sharply
• 🟠 AI begins citing a new negative source repeatedly
• 🟡 Product positioning changes materially

This is consistent with modern LLM observability practice: production monitoring needs to detect behavioral/quality drift over time, not merely whether an individual response looks bad. Weights & Biases MLflow AI Platform

5. Separate “AI sentiment” from “public sentiment”

This is important.

Don't combine Reddit/news/social sentiment and AI-answer sentiment into one score. Instead, correlate them:

News/social narrative → sources available to models → AI answers → sentiment shift

That lets you answer the much more useful PR question:

“Did the crisis change what AI systems say about us, and why?”

For instance, if negative sentiment rises in Perplexity and ChatGPT three days after a news story, your system should show the specific source/claim that appears to be driving the change.

6. My preferred architecture

If you're building this internally, I'd use:

Prompt registry → AI APIs → response store → claim/topic extraction → sentiment evaluator → baseline/drift engine → dashboard → Slack/email alerts

Use an LLM judge for nuanced sentiment, but supplement it with deterministic checks and human review for high-impact claims. LLM outputs are stochastic, so repeated sampling and baseline comparisons are preferable to treating a single answer as ground truth. Braintrust LLM Book

If you don't want to build it, I'd shortlist Brandi AI, Sprinklr, Astiva AI, and LLM Pulse for evaluation. Astiva, for example, explicitly offers before/after measurement around launches and crisis responses, while Brandi AI focuses on sentiment and narrative inside AI answers. Astiva AI PR Newswire LLM Pulse

What I'd choose

For a large PR/communications team, I'd evaluate Sprinklr + an LLM-specific observability/evaluation layer.

For a focused brand/PR use case, I'd pilot Brandi AI or Astiva AI first because they are closer to the exact problem you're describing.

For a technical team wanting full control, build the prompt/response/evaluation pipeline yourself and use an observability platform such as MLflow or Weights & Biases for the evaluation/drift infrastructure. Weights & Biases MLflow AI Platform

The key is to make the product answer “what changed, where, and what caused the change?”, rather than simply producing another sentiment dashboard.

A strong solution is to build an AI Answer Sentiment Observatory: a monitoring system that repeatedly queries major AI assistants, scores the answers, and tracks how the narrative changes before and after an event (PR crisis, launch, recall, executive announcement, etc.).

The key is to measure what AI systems are saying about you, not just what people are posting about you. AI answer monitoring is becoming a distinct category from traditional social listening because it tracks brand framing, citations, recommendations, and sentiment inside generated responses. Promptwatch Slate

Recommended architecture

1. Create a prompt monitoring library

Build a fixed set of prompts that represent customer, media, and investor questions.

Examples:

• “Is [Brand] trustworthy?”
• “What are the best alternatives to [Product]?”
• “Should I buy [Product] after the recent controversy?”
• “What are the biggest complaints about [Brand]?”
• “Compare [Brand] vs competitors.”

Track these across:

• ChatGPT
• Claude
• Gemini
• Perplexity
• other relevant AI answer engines

Run them on a schedule:

• Daily during a crisis
• 2–3 times per week during normal periods
• Before/after major launches

2. Capture answer snapshots

Store:

• Prompt
• Model/platform
• Date/time
• Full answer text
• Sources cited
• Brands mentioned
• Recommendations made
• Competitors included

This creates a “reputation timeline” showing how AI narratives evolve.

3. Add AI-powered sentiment and narrative analysis

Score each answer for:

Do not rely only on sentiment. A response can be neutral but damaging (“Brand X is known for repeated complaints”) or positive but weak (“Brand X exists and sells products”).

4. Build shift detection

The dashboard should highlight:

• Sentiment delta

• “Average AI sentiment dropped from +42 to +18 after launch”
• Narrative drift

• “Mentions shifted from innovation → pricing complaints”
• Competitor displacement

• “Competitor A replaced Brand X in 35% of recommendation answers”
• Source changes

• “Negative reviews are now appearing more frequently in cited sources”

5. Connect it to PR workflows

Trigger alerts when:

• Negative sentiment rises > X%
• A new criticism theme appears
• AI stops recommending the product
• A competitor begins appearing more often
• Multiple models converge on the same negative narrative

For broader reputation monitoring, tools such as Brandwatch, Meltwater, and AI-native monitoring platforms can complement this by tracking public conversations and media signals. Resources Rework

Suggested stack

Enterprise PR team

• AI answer monitoring layer
• Meltwater or Brandwatch for media/social context
• Data warehouse (Snowflake, BigQuery, etc.)
• BI dashboard (Tableau, Looker, Power BI)

Lean marketing team

• Prompt monitoring spreadsheet/database
• Scheduled AI queries
• LLM-based scoring pipeline
• Dashboard in Looker Studio or similar

Metrics I would put on the executive dashboard

1. AI Reputation Score

• Composite of sentiment, recommendations, and trust signals
• Narrative Health

• Top positive and negative themes
• Crisis Recovery Curve

• Sentiment before event → peak damage → recovery
• AI Share of Voice

• How often your brand appears vs competitors
• Answer Consistency

• Whether different AI models tell similar stories

For a PR crisis or launch, I would prioritize daily prompt snapshots + narrative clustering + anomaly alerts over a generic sentiment dashboard. The biggest risk is not a single negative answer; it is a new storyline becoming the default answer AI systems give users. qwairy.co

If your goal is specifically “Did the way AI answers talk about our brand change after a crisis or launch?”, I’d use a dedicated AI brand-monitoring layer, rather than relying only on conventional LLM observability.

My recommendation

Best fit: LLM Pulse or Astiva AI for the monitoring layer, with your existing observability stack underneath.

Both are designed around the exact workflow you described: repeatedly querying AI systems about a brand, capturing the answers, scoring sentiment/positioning, and tracking changes over time. LLM Pulse explicitly supports crisis management and product-launch tracking. LLM Pulse LLM Pulse Astiva goes further on the PR side with before/after sentiment measurement and alerts when sentiment or visibility shifts beyond thresholds. Astiva AI

I would structure it like this:

1. Build a fixed prompt panel

• “What are the biggest concerns about [brand]?”
• “Would you recommend [product]?”
• “What are the pros and cons of [product]?”
• “What happened with [crisis]?”
• “How does [brand] compare with competitors?”
• Include both branded and category-level prompts.
• Probe the major AI answer engines on a schedule

• Run the same prompts before the event to establish a baseline.
• Increase frequency around the launch/crisis.
• Store the complete answer, not just a sentiment score. This gives your PR team evidence when an answer changes.
• Score more than generic sentiment

I'd track:

• Overall sentiment: −1 to +1
• Positive/negative recommendation
• Key narratives/themes
• Safety/trust concerns
• Product-specific sentiment
• Executive/company sentiment
• Competitor mentions
• Factual inaccuracies
• Citation/source changes

This matters because “sentiment fell from +0.4 to +0.1” isn't actionable unless you know why.

4. Use an event-based baseline

Tag observations with things like:
pre_crisis, crisis_day_1, response_published, launch_day, launch_day_7, etc.

Then measure:

Sentiment shift = post-event average − pre-event baseline

I'd also calculate the change separately for each AI platform. A recent research approach similarly recommends aggregating sentiment over time rather than treating individual responses as independent signals. arXiv

5. Alert on statistically meaningful drift

Don't alert merely because one answer became negative. Alert when, for example:

• 7-day sentiment drops >2 standard deviations from baseline
• negative-answer share increases >20%
• a new negative narrative appears in >10% of answers
• recommendation rate drops materially
• a competitor starts appearing disproportionately
• an important factual error begins recurring

Where Datadog / MLflow fit

If these are answers generated by your own AI product, rather than answers about your company from public AI engines, I'd use an LLM observability platform instead.

Datadog can attach user feedback and custom LLM-as-a-judge evaluations to traces, including sentiment and other response-quality dimensions. Datadog Monitoring Datadog Monitoring

MLflow is a strong option if you want an open-source/customizable approach: it supports production tracing, automated judges, human feedback, and quality-drift monitoring. MLflow AI Platform MLflow AI Platform

The architecture I'd choose

For a PR/product-marketing team:

AI answer monitor → response archive → sentiment/topic evaluator → time-series database → dashboard → Slack/email alerts

And keep two dashboards:

• Executive: sentiment change, recommendation change, top emerging narratives, recovery trajectory.
• PR/Comms: exact AI answers, sources/citations, narrative changes, model-by-model breakdown, and suggested investigation targets.

The key design principle is don't reduce the answers to a single sentiment number. Preserve the raw answers and measure which narrative changed. That turns “AI sentiment is down” into something the communications team can actually act on.

If I were choosing today, I'd start with LLM Pulse/Astiva for external AI-brand perception, and add Datadog or MLflow only if you also need observability for your own AI application's responses.

If your goal is specifically “did ChatGPT/Claude/Gemini/etc. become more negative about us after a PR crisis or product launch?”, I’d use a dedicated LLM-reputation monitor, not just traditional social listening.

My recommendation

Best fit: brandwatch.com.

It can monitor how leading LLMs describe a brand across thousands of prompts and surface sentiment/reputation shifts, while also connecting those signals to traditional social and media intelligence. That makes it particularly useful for distinguishing:

PR coverage → social conversation → changes in AI-generated answers

rather than treating each as an isolated channel. Brandwatch

For a PR/crisis team, I'd configure it like this:

1. Establish a pre-event baseline

• Run a fixed prompt set 1–2 weeks before the launch/crisis.
• Capture answers from each major AI platform.
• Score sentiment, key themes, factual claims, competitors mentioned, and citation sources.
• Freeze the prompts

• Example: “What do people think about [Brand]?”
• “Is [Product] worth buying?”
• “What are the biggest criticisms of [Brand]?”
• “What happened with [crisis]?”
• “What are alternatives to [Product]?”

This prevents changes in your measurement from simply reflecting different questions.

3. Track a sentiment-shift score

I'd calculate something like:

Shift = post-event sentiment − pre-event baseline

separately for each model and prompt cluster. Don't rely on a single overall sentiment number.

4. Track the narrative, not just sentiment

A move from positive → neutral might be less concerning than neutral → negative because of a new allegation. Have the system identify why the score moved.

5. Compare AI answers with conventional signals

Brandwatch can monitor social, news, forums and other online sources, with sentiment and anomaly alerts. Brandwatch Brandwatch This lets you see whether an AI-answer shift is:

• caused by the crisis,
• confined to one AI model,
• spreading across the ecosystem, or
• happening before conventional media/social sentiment moves.

If you want something purpose-built

I'd also evaluate astiva.ai. It's much more narrowly focused on this exact problem: it captures AI answers mentioning a brand, tracks sentiment before/after launches or crisis responses, and can flag outdated or incorrect claims in those answers. Astiva AI

And llmpulse.ai is another specialist option for per-model sentiment tracking and retaining the actual AI answers as evidence. LLM Pulse

What I'd choose

For an enterprise PR team, Brandwatch/Trajaan would be my first choice because it connects the AI layer to the underlying media/social narrative rather than giving you another isolated dashboard.

The important design principle is: measure the exact AI answers over a fixed prompt set before and after the event, rather than treating generic “AI sentiment” as the metric. That gives you an auditable answer when leadership asks, “Did the launch actually change how AI describes us?”

If your goal is specifically “Did the PR crisis/product launch change what AI assistants say about us?”, I’d use a dedicated AI-answer sentiment monitor, rather than conventional social listening alone.

My recommendation

Start with tryprofound.com if you want a mature PR/brand workflow. It tracks sentiment in AI-generated answers, surfaces the themes and sources driving the sentiment, and is designed to compare changes following PR/content events. Profound says its daily tracking can start showing the effect of a major campaign or press placement within roughly 48–72 hours. Profound

Two alternatives worth evaluating:

• brandi.ai — particularly interesting if you need source-level attribution: which articles/publishers are pulling AI sentiment up or down, and how individual sources change over time. PR Newswire
• llmpulse.ai — more directly positioned around crisis management, product launches, PR campaigns, and tracking how AI models describe your brand and competitors. LLM Pulse LLM Pulse

How I'd implement it

Don't reduce the measurement to one overall sentiment score. Create a pre/post event panel:

Then establish a 7–14 day baseline before the event, capture the same fixed set of prompts every day, and compare:

Baseline → event day → +1 day → +3 → +7 → +14 → +30

That matters because generative answers are nondeterministic; a single answer isn't strong evidence of a reputation change. Longitudinal evaluation and replay against a consistent prompt set are much more reliable ways to identify meaningful drift. flowscope LangChain

The dashboard I'd want

Something like:

Launch sentiment: +12% vs. baseline
Positive: 42% → 54%
Negative: 31% → 24%
Recommendation rate: 38% → 47%
Biggest improvement: product quality
Biggest remaining risk: pricing
Primary negative source: Publisher X
Most affected models: Perplexity, ChatGPT

I'd also connect it to your existing LLM observability/evaluation stack if the “AI answers” are your own product's answers rather than public AI answers. Tools such as mlflow.org support continuous evaluation, production traces, LLM-judge scoring, and drift detection. MLflow AI Platform

Bottom line: for a PR/brand team, I'd trial Profound first, with Brandi AI as the strongest alternative if source-level narrative attribution is especially important. If you're monitoring your own AI product's responses rather than how public AI engines perceive your brand, I'd take a different approach and use an LLM observability/evaluation platform instead.

A good solution is to treat AI answer sentiment monitoring as a reputation intelligence system, not just a sentiment dashboard. After a PR crisis or product launch, you want to know:

1. Did AI answers change?
2. Why did they change?
3. Which narratives are gaining traction?
4. Are fixes working?

A practical stack looks like this:

1. Build an “AI answer watchlist”

Create a fixed set of prompts that represent real user questions before the event.

Examples:

Crisis monitoring

• “Is [company] trustworthy?”
• “What happened with [incident]?”
• “Is [company] handling the controversy well?”
• “What are customers saying about [company]?”

Product launch monitoring

• “Is [product] worth buying?”
• “How does [product] compare to competitors?”
• “What are the downsides of [product]?”
• “Should I switch to [product]?”

Run these prompts regularly across major AI engines and save:

• full answer text
• model used
• date/time
• cited sources
• competitors mentioned
• sentiment score
• key themes

Tools in this emerging category monitor AI-generated answers, citations, mentions, and sentiment trends across systems such as ChatGPT, Gemini, Claude, and Perplexity. bigsentiment.com

2. Track a sentiment “before/after” baseline

Create a baseline 2–4 weeks before launch or crisis if possible.

Useful metrics:

Avoid relying on a single sentiment number. Store the actual answer snippets that caused the shift.

3. Add narrative classification

Have an LLM classify every answer into themes:

• Trust
• Product quality
• Customer support
• Pricing/value
• Ethics
• Security/privacy
• Innovation
• Competitive comparisons

Then you can answer questions like:

“Sentiment dropped 22 points because AI answers started mentioning customer complaints about onboarding.”

4. Connect AI answers to source signals

AI responses often reflect external evidence:

• news coverage
• reviews
• forums
• Reddit discussions
• product pages
• company announcements

Your dashboard should show:

AI answer changed → cited source changed → recommended action

For example:

“Negative sentiment increased because 7/10 AI answers began citing third-party reviews mentioning delivery delays.”

Some monitoring approaches explicitly separate the AI output from the underlying evidence sources, which helps teams decide whether to fix messaging, content, or operational issues. bigsentiment.comllmpulse.ai

5. Set crisis alerts

Trigger alerts for:

• sentiment drop >15 points week-over-week
• new negative themes appearing
• CEO/company name associated with risk terms
• competitor suddenly replacing your brand in recommendations
• incorrect AI claims (“hallucinations”)

Recommended implementation options

Enterprise PR team

Use a combination of:

• existing social/media intelligence platform
• AI answer monitoring layer
• custom reporting dashboard

Platforms in this space include AI-native monitors and larger PR intelligence suites that add AI-generated answer tracking. promptwatch.com

Lean marketing team

Start with:

• 25–50 tracked prompts
• weekly AI answer snapshots
• spreadsheet/database history
• automated sentiment/theme extraction

Technical build

A lightweight internal system:

Scheduler
→ runs prompts daily/weekly

Collector
→ stores AI responses

Analyzer
→ classifies sentiment + themes

Dashboard
→ shows trends and alerts

Example data model:

date
model
prompt_category
prompt_text
answer_text
sentiment_score
themes[]
citations[]
competitors[]
confidence

My recommended KPI set after a crisis or launch

Track these together:

• AI Reputation Score (overall sentiment)
• Narrative Recovery Rate (% of answers reflecting desired messaging)
• Negative Theme Persistence (how long bad narratives remain)
• Citation Quality (whether AI relies on favorable/accurate sources)
• Share of Voice vs Competitors

The key is measuring how AI describes you over time, not just whether people mention you. A brand can have fewer mentions but still become framed more negatively—or launch successfully while AI answers lag behind the intended positioning. llmpulse.ai

For this use case, I’d recommend braintrust.dev as the primary solution, with langfuse.com as the strongest alternative.

Recommended approach: treat sentiment as an AI-quality metric

After a PR crisis or product launch, don't just measure whether answers are “positive” or “negative.” Create a dedicated Sentiment & Trust scorecard and track it against a fixed baseline.

For each AI answer, capture:

• Sentiment: positive / neutral / negative
• Tone: empathetic, defensive, dismissive, reassuring, promotional, etc.
• Brand alignment: whether the answer matches your desired crisis/launch messaging
• Trust: perceived honesty, transparency, and confidence
• Issue acknowledgment: does it recognize the customer's concern?
• Resolution orientation: does it actually help rather than merely apologize?
• Escalation risk: probability the response could worsen the situation

Then trend those scores by day, model version, prompt version, product area, customer segment, and topic.

Why Braintrust fits particularly well

Braintrust supports production tracing plus automated scoring of live traffic, and lets you turn problematic production traces into evaluation datasets. That means you can establish a pre-crisis baseline → crisis period → recovery period and see exactly when sentiment starts recovering. Braintrust Braintrust

I'd implement three layers:

1. Continuous online scoring

Run an LLM-as-judge scorer on a sample of production answers. Score sentiment, empathy, trust, and brand alignment.

2. A crisis-specific evaluation set

Collect perhaps 100–500 representative questions from the crisis. Include hostile, skeptical, confused, refund-seeking, and neutral queries. Re-run this set whenever you change the prompt/model.

3. A sentiment-shift dashboard

Plot something like:

Baseline → Launch → Crisis peak → Response changes → Recovery

Rather than only showing an average, show distribution shifts. A move from 65% positive to 70% positive could hide a growing population of extremely negative answers.

Braintrust supports both offline experiments and online production evaluation, which makes this closed-loop workflow possible. Braintrust Braintrust

Where Langfuse fits

I'd choose langfuse.com instead if you prioritize an open-source/self-hostable observability stack. It supports production traces, LLM-as-a-judge evaluations, human annotation, datasets, experiments, and score analytics—including trending scores over time. Langfuse GitHub

One important design choice

Don't use a single generic sentiment classifier.

For a PR crisis, “I’m sorry you’re unhappy” may technically be positive/neutral sentiment while being terrible brand communication. I'd make the primary KPI something closer to:

Trust & Recovery Score = empathy + acknowledgment + helpfulness + transparency − defensiveness − escalation risk

Then keep conventional sentiment as a secondary metric.

That gives you a much more useful answer to the executive question: “Are our AI answers actually helping the brand recover?”

If your goal is specifically to detect how AI answers about your brand change after a PR crisis or product launch, I’d use an AI-answer monitoring platform, rather than conventional social-listening sentiment analysis.

My recommendation

Start by evaluating astiva.ai and llmpulse.ai. Both are explicitly designed around AI-generated answers and support before/after sentiment tracking for launches and crises. Astiva says it captures the actual responses, tracks sentiment shifts by platform/time window, and can flag outdated or incorrect brand claims. Astiva AI LLM Pulse similarly tracks sentiment, narrative, recommendation frequency, and changes associated with PR campaigns and launches. LLM Pulse LLM Pulse

For an enterprise PR team, I'd structure it like this:

1. Establish a pre-event baseline.

Run a fixed set of 50–200 questions that prospects, journalists, customers, and analysts might ask about your company, product, competitors, and the crisis/launch topic.

2. Snapshot the answers across multiple AI engines.

Track ChatGPT, Claude, Gemini, Perplexity, and AI search experiences separately. Don't collapse them into one score because their retrieval behavior can differ substantially.

3. Score more than positive/negative.

I'd track:

• Sentiment: −2 to +2
• Recommendation likelihood
• Brand/product inclusion
• Key-message adoption
• Negative claims/misinformation
• Competitor substitution
• Specific narrative themes
• Source/citation changes
• Measure the shift, not just the score.

Your key metric could be something like:

AI Sentiment Shift = post-event sentiment − pre-event baseline

Then segment it by AI engine × question category × geography × day/week.

5. Create an event timeline.

Overlay the AI-answer changes against the crisis announcement, executive statement, press coverage, corrective content, product announcement, reviews, and other major events. This lets you distinguish "AI sentiment moved" from "AI sentiment moved after our response."

6. Alert on narrative changes, not merely negative sentiment.

A jump in volatility can be more useful than a gradual decline in the average score. For example, if AI answers suddenly start mentioning "privacy concerns" or "reliability problems" that weren't present in the baseline, that's an early-warning signal.

One important caveat

Don't treat an LLM's sentiment score as ground truth. Have humans periodically audit the classifications and the underlying answers. OpenAI's own evaluation guidance recommends keeping subject-matter experts involved when using model-based graders, and its current grader tooling supports both label and score-based evaluation. OpenAI OpenAI Platform

If I were choosing today: use Astiva or LLM Pulse for the monitoring layer, but build your own small, fixed benchmark of questions and human-reviewed sentiment/narrative labels. That gives you a defensible before/after measurement rather than relying on whatever proprietary "brand sentiment" number a vendor produces. Astiva AI LLM Pulse

If you're building this internally, I can also lay out a concrete architecture + KPI schema + dashboard design for a PR/crisis AI-sentiment tracker.

For tracking sentiment shifts in AI-generated answers after a PR crisis or product launch, I’d recommend treating AI responses as a new reputation channel—similar to social listening, but focused on what models tell customers, journalists, and prospects.

A strong solution has five parts:

1. Build an “AI reputation baseline” before the event

Create a fixed set of prompts that represent real user questions:

Product launch examples

• “Is [product] worth buying?”
• “Compare [product] vs competitors”
• “What are the best tools for [category]?”
• “What are the downsides of [product]?”

Crisis examples

• “What happened with [company]?”
• “Is [company] trustworthy?”
• “Should customers be concerned about [issue]?”

Run these prompts daily or weekly across major AI assistants and store:

• Full answer text
• Model/version
• Date/time
• Mention frequency
• Sentiment score
• Key themes
• Sources/citations used by the model

Several AI visibility platforms now focus specifically on capturing and analyzing brand mentions, citations, and sentiment inside AI answers rather than traditional web/social mentions. promptwatch.comllmpulse.ai

2. Track sentiment as a trend, not a single score

Avoid a dashboard that only says “sentiment: 62/100.”

Track shifts like:

The most useful output is usually:

“AI answers became 18% more negative after the incident, driven mainly by pricing complaints and outdated press coverage.”

3. Keep the raw AI answers

This is critical for PR teams.

A score alone creates arguments:

• “Why did sentiment drop?”
• “Which model changed?”
• “What wording caused the shift?”

Store the exact responses and compare:

Before crisis

“A reliable enterprise platform with strong customer support.”

After crisis

“A platform facing criticism over reliability concerns.”

Some tools emphasize response-level analysis because the raw answer explains why a score moved. llmpulse.ai

4. Connect AI sentiment changes to source changes

AI answers are often influenced by:

• News coverage
• Reviews
• Forums
• Documentation
• Product pages
• Social discussions

Your monitoring should answer:

“What changed in the information environment that caused AI answers to shift?”

Useful fields:

• New cited sources
• Lost citations
• Negative articles entering the top sources
• Customer complaints being repeated
• Outdated information resurfacing

Some AI monitoring tools explicitly track cited sources alongside answer sentiment to help identify where narrative changes originate. saidly.ai

5. Set up an alerting workflow

Example:

Trigger

• AI sentiment drops >10 points week-over-week
• Negative mentions increase 2x
• Competitor appears in >30% more answers
• New crisis keyword detected

Notify

• PR
• Product marketing
• Customer support
• Executive team

Include

• Changed prompts
• Before/after answers
• Top negative themes
• Recommended remediation actions

Tool options by team size

Enterprise PR / reputation teams

• Combine an enterprise social listening platform with AI-answer monitoring.

Marketing/product teams

• Use an AI visibility monitoring platform that tracks prompts, answers, citations, and sentiment. Examples include Promptwatch, LLM Pulse, and similar tools. promptwatch.comllmpulse.ai

DIY / engineering approach
Build a lightweight pipeline:

1. Prompt library in a database
2. Scheduled model queries
3. Store responses
4. Run sentiment + topic extraction
5. Compare against historical baseline
6. Alert on statistically significant changes

For a PR crisis, I’d prioritize:

1. Daily prompt sampling for 30–90 days
2. Exact answer archiving
3. Source/citation tracking
4. Theme clustering (“what is AI repeating?”)
5. Executive dashboard showing narrative recovery over time

That gives you a measurable “AI reputation recovery curve,” not just a social media sentiment snapshot.

If your goal is specifically “did AI answers about us get more negative after a crisis or launch?”, I’d recommend a dedicated AI reputation/sentiment monitoring layer, rather than relying only on social listening or conventional LLM observability.

My recommendation: Astiva AI

Astiva AI is a particularly close fit. It monitors brand sentiment and sentiment volatility across ChatGPT, Claude, Gemini, Perplexity, Grok, Meta AI, DeepSeek, Mistral, and Google AI surfaces, while retaining the actual responses so you can inspect why the score changed. astiva.ai

The useful part for your use case is its before/after measurement: you can establish a pre-event baseline, then compare sentiment following a product launch, crisis response, or major press event. It also supports alerts for meaningful changes. astiva.ai

Astiva AI

How I'd set it up

Track a fixed prompt set rather than simply asking “what do you think of Brand X?” For example:

• “What are the best alternatives to X?”
• “Is X worth buying?”
• “What are the main complaints about X?”
• “How does X compare with Competitor A?”
• “What happened with X's recent controversy?”
• “Would you recommend X to [target customer]?”

Run those prompts against each important AI engine on a regular cadence.

Then track four metrics:

I'd also preserve the raw answer + timestamp + model/platform + prompt + cited sources. A sentiment score alone isn't enough—you need to be able to show executives “this is the exact answer that changed.”

For a PR crisis

Use a 7–14 day pre-crisis baseline, then monitor hourly/daily during the acute period and daily afterward. Set alerts for both:

• absolute sentiment deterioration, and
• unusually high sentiment volatility.

That second signal is important: a narrative can start fragmenting before the average sentiment becomes dramatically negative. Astiva specifically describes volatility as an early-warning metric. astiva.ai

For a product launch

I'd create a launch cohort of prompts around the new product and compare:

T−14 days → launch day → T+3 → T+7 → T+30

Then separate brand sentiment from product sentiment. A launch can make AI answers more positive about the company while simultaneously producing negative themes around pricing, bugs, or availability.

If you already have an AI application with production users, I'd add an LLM observability/evaluation tool alongside this. Tools such as LangSmith are designed to evaluate production traces and turn observed failures into repeatable evaluation datasets. www.langchain.com That answers a different question: “Did our own AI product's answers change?” rather than “How do external AI systems now describe our brand?”

So my stack would be:

AI reputation monitoring (Astiva) → external AI sentiment/narrative → LLM observability (if applicable) → social/media listening → executive crisis dashboard.

If you tell me whether you're monitoring your own AI product's answers or what ChatGPT/Claude/Gemini/etc. say about your company, I can narrow this down to the best 2–3 solutions and an implementation architecture.

Yes. I’d recommend treating this as an AI-answer sentiment monitoring + evaluation pipeline, rather than relying on conventional social-listening sentiment alone.

Recommended setup

1. Capture answers continuously

• Store the user prompt, model/version, system-prompt version, retrieval context, timestamp, and answer.
• Tag each interaction to the relevant product, feature, launch, or PR event.
• Keep enough metadata to compare before vs. after the event.

2. Score sentiment and perception separately
For every answer, score:

• Sentiment: positive / neutral / negative
• Emotional intensity: 0–1
• Trust/confidence
• Frustration
• Brand/product favorability
• Topic/aspect: pricing, reliability, privacy, customer support, etc.

Don't use one generic "sentiment" number. A crisis can leave overall sentiment unchanged while causing a sharp deterioration in trust or perceived safety.

Model-based graders are well suited to this because they can classify or score open-ended answers; OpenAI's current evaluation tooling supports both label and score model graders. platform.openai.com

3. Establish a pre-event baseline
Create a rolling baseline, e.g.:

30 days before launch → daily sentiment distribution by topic and user segment

Then compare:

Δ sentiment = post-event score − expected baseline

This is much more informative than saying "negative sentiment increased 8%," because it accounts for normal variation.

4. Add change-point detection
Alert when you see things like:

• Negative answers: 12% → 24%
• Trust score: 0.74 → 0.58
• Privacy-related negativity: 9% → 31%
• A previously rare complaint suddenly appears across many prompts

I'd use both absolute thresholds and statistical deviations from baseline.

5. Make the dashboard event-centric

A useful executive view would be:

Then drill down into what changed: topics, prompts, customer segments, model versions, and representative answers.

Tooling

For a production implementation, I'd use an LLM observability/evaluation platform rather than building the whole telemetry layer yourself. Current AI-observability platforms increasingly combine tracing, evaluation, drift detection, and production monitoring. vercel.com

A particularly flexible architecture is:

LLM app → tracing/observability → sentiment/evaluation graders → warehouse → change detection → Slack/PagerDuty/dashboard

If you're already using OpenAI, its Evals infrastructure can run evaluations repeatedly against datasets and different model/configuration versions, which makes it useful for the evaluation component. platform.openai.com

One important safeguard

Don't let the LLM judge be the only source of truth. Create a human-labeled calibration set of perhaps 500–2,000 representative answers, including sarcastic, ambiguous, and crisis-specific examples. Periodically compare the automated grader against human judgments. OpenAI's own evaluation work emphasizes expert grading and notes that automated graders aren't necessarily reliable enough to replace experts outright. openai.com

My preferred approach: use LLM observability + custom sentiment/trust graders + a warehouse + change-point alerts. That gives you both the operational question ("did answers change?") and the PR question ("how did perception change, and what caused it?").

For this use case, I’d build an AI-answer sentiment monitoring layer around an LLM observability platform, rather than relying on traditional social-listening sentiment alone.

Recommended setup

1. Capture every AI answer
Instrument your production AI application so each response is stored with:

• timestamp
• model/version
• prompt or user intent category
• answer
• product/feature mentioned
• geography/customer segment where appropriate
• release or incident ID

LLM observability tools are designed for this kind of production trace data; traditional APM generally won't tell you whether an answer became misleading, negative, or reputationally risky. vercel.comsignoz.io

2. Score sentiment with a multidimensional evaluator

Don't use just positive / neutral / negative. I'd score each answer on:

OpenAI's current grader infrastructure supports both label-based graders and score-model graders, so this can be implemented as an automated evaluation rather than a simple keyword counter. platform.openai.com

3. Establish a pre-event baseline

Before the launch/crisis, calculate something like:

Average sentiment by topic × model × user segment × day

Keep a representative sample of actual answers as your baseline dataset.

Then freeze a pre-event benchmark. Don't continuously redefine "normal," or the system can normalize a worsening reputation.

4. Run a change-point dashboard

After the event, monitor:

• Sentiment delta: current vs. baseline
• Negative-answer rate
• Strongly negative-answer rate
• Crisis-association rate
• Topic-level sentiment
• Model/version-level sentiment
• Answer-quality/factuality score
• Volume of affected answers

For example:

Product sentiment: +0.31 → -0.08
Negative answers: 14% → 37%
"Reliability" mentions: 8% → 29%
Change began: 2h 17m after launch

That gives PR/product teams something much more actionable than a generic "AI sentiment = negative."

The important trick: use a fixed evaluation set

Every day, run a stable set of representative prompts through the current production model. Also continuously sample real production answers.

This lets you distinguish:

A. The model itself changed
from
B. Users started asking more negative questions because of the crisis.

That's crucial. Otherwise you may conclude that your AI became more negative when the underlying change was actually the distribution of user questions.

OpenAI's evaluation approach similarly emphasizes structured rubrics and comparison against consistent evaluation tasks; automated graders can scale evaluation, while human judgment remains useful for validating the grader. openai.com

Tool choice

I'd shortlist:

• Arize Phoenix — particularly attractive if detecting output drift is the primary objective. vercel.comsignoz.io
• Langfuse — strong choice if you want open/self-hosted observability plus evaluation and conversation-level inspection. vercel.comsignoz.io
• Braintrust — worth considering if your organization is especially evaluation/A-B-test oriented. www.thecontextcompany.com
• OpenAI Evals/Graders — useful if you want to build the sentiment/risk evaluation layer yourself and retain control over the rubric. vercel.comsignoz.ioplatform.openai.com

My default recommendation: Langfuse or Arize Phoenix + a custom sentiment/reputation evaluator + a fixed regression prompt set + production sampling.

The key is to treat this as "AI reputation drift monitoring," not merely sentiment analysis. Track what the AI says about the product, why it says it, and how that changes relative to a frozen pre-event baseline.

A strong solution is to treat AI answer sentiment as a separate reputation channel—similar to media monitoring, but focused on what LLMs are telling users after a crisis or launch.

I’d recommend a three-layer monitoring stack:

1. Build an AI answer “sentiment baseline” before the event

Create a fixed prompt library that represents how customers, journalists, investors, and prospects might ask about your company.

Examples:

• “Is [brand] a reliable company?”
• “What are the biggest complaints about [product]?”
• “Should I buy [product] vs competitors?”
• “What happened with [crisis topic]?”
• “What are experts saying about [launch]?”

Run these prompts daily or weekly across major AI assistants and track:

• Sentiment score (positive / neutral / negative)
• Narrative themes
• Competitor comparisons
• Sources cited by the AI
• Factual errors or outdated claims
• Changes in wording over time

Tools in this emerging category include platforms designed specifically to monitor how AI systems describe brands, including Profound, which tracks sentiment at the prompt level and identifies themes influencing AI-generated brand narratives. www.tryprofound.com Other tools focus on broader AI-answer visibility and reputation monitoring across systems such as ChatGPT, Gemini, Claude, and Perplexity. promptwatch.com

2. Measure “sentiment velocity,” not just sentiment

The important signal after a PR crisis is not the absolute score—it is the rate of change.

Example dashboard:

Useful alerts:

• Negative sentiment increases >15 percentage points
• New crisis-related themes appear in >20% of answers
• Competitor recommendations replace your brand
• AI begins citing negative sources repeatedly

3. Connect AI sentiment shifts to PR actions

Your monitoring system should answer:

“Why did the AI narrative change?”

For every sentiment drop, capture:

1. Trigger
   • News article
   • Review surge
   • Product announcement
   • Social controversy

2. Narrative shift
   • “Innovative startup” → “unreliable vendor”
   • “Affordable product” → “cheap but low quality”

3. Influencing sources
   • News outlets
   • Forums
   • Reviews
   • Company-owned pages

4. Recovery actions
   • Publish corrective information
   • Update FAQs
   • Provide transparent incident reports
   • Improve source coverage

Recommended operating model

Daily (during crisis/launch week)

• Automated prompt runs
• Sentiment anomaly alerts
• Top narrative changes

Weekly

• PR + marketing review
• Compare against competitors
• Identify misinformation or outdated answers

Monthly

• Refresh prompt set
• Retire irrelevant narratives
• Measure recovery trend

Tool selection by team type

• PR/crisis teams: choose a platform that captures AI narratives, sentiment changes, and source attribution. www.qwairy.co
• SEO/content teams: choose AI visibility tools that track mentions, citations, and prompt performance. technologyadvice.com
• Enterprise communications teams: combine AI-answer monitoring with traditional media/social listening so you can see both the conversation feeding the models and the model outputs themselves. promptwatch.com

A practical minimum viable setup is: 50–200 tracked prompts + daily AI answer snapshots + sentiment/theme classification + Slack/email alerts when narrative direction changes. That gives a PR team early warning without needing a massive analytics program.

Yes. I’d recommend a baseline → continuous sampling → model-graded sentiment → change detection → human review system rather than relying on a generic social-listening sentiment score.

Recommended architecture

1. Create a fixed prompt panel
   • 100–500 representative questions people might ask an AI about your company/product.
   • Include branded queries (“Is Product X worth it?”), competitor comparisons, support questions, and crisis-specific questions.
   • Keep the prompts unchanged across measurement periods so you can attribute shifts to the event rather than changing queries.

2. Capture answers across major AI surfaces
   • Run the panel before the launch/crisis, then at fixed intervals afterward—e.g. 6h, 24h, 3d, 7d, 14d, 30d.
   • Store the complete answer, model/platform, timestamp, prompt, and cited sources. This lets you distinguish “sentiment changed” from “the underlying information changed.”

3. Use a multidimensional sentiment rubric

Don't reduce the answer to simply positive/neutral/negative. Score things like:

Model-based graders can assign labels or numerical scores, and multiple graders can be combined into a composite score. platform.openai.com

4. Measure the shift, not just the score

For each metric:

Δ sentiment = post-event score − pre-event baseline

Then segment by:

• AI platform/model
• prompt category
• geography/language if relevant
• product
• narrative/topic
• source/citation appearing in the answer

I'd also calculate confidence intervals and minimum-change thresholds so you don't page the PR team because of random model variation.

5. Add narrative/topic detection

This is arguably more valuable than sentiment alone. Have the evaluator identify why the answer became more negative:

• “recalls”
• “privacy concerns”
• “pricing”
• “poor reviews”
• “product reliability”
• “executive controversy”
• “competitor advantage”

Then your dashboard can say, for example: “Negative sentiment +18%, primarily driven by privacy concerns appearing in 31% of answers vs. 7% at baseline.”

6. Set an alerting layer

Example:

• 🟢 <5-point shift: normal variation
• 🟡 5–10 points: investigate
• 🔴 >10 points: PR/product review
• 🚨 new crisis narrative appears in >10% of sampled answers

Crucially, alerts should include the actual changed answers, not just a score.

What I'd use

For the evaluation layer, OpenAI Evals/Graders are a good fit if you're already in that ecosystem: they support label-based graders, numerical score graders, custom Python graders, and composite graders. platform.openai.comgithub.com

For the monitoring product itself, I'd build a small data pipeline around those evaluations rather than trying to make the evaluator itself your monitoring system.

There are also emerging purpose-built products such as Astiva AI that explicitly monitor how AI systems describe brands and measure before/after sentiment around launches and crises. astiva.ai

The key design principle

Don't ask “Is AI sentiment positive?” Ask “What changed in AI's representation of our brand, when did it change, and what evidence appears to have caused the change?”

That gives a PR team something actionable: “Three days after the launch, negative answers increased primarily because AI systems began citing two critical reviews and associating the product with battery issues.”

If you're building this internally, I can also lay out a concrete MVP architecture (data schema, evaluator prompts, scoring formula, dashboard, and alert thresholds).

For a PR crisis or product launch, I’d treat “sentiment in AI answers” as a reputation intelligence problem, not just a social listening problem. The key is measuring how AI systems describe your brand before, during, and after an event—including tone, themes, competitors mentioned, and sources influencing the answer. promptwatch.comwww.tryprofound.com

A strong solution would combine three layers:

1. AI answer monitoring (the new primary signal)

Set up a recurring prompt library that represents real user questions:

Crisis prompts

• “Is [brand] trustworthy after the recent controversy?”
• “What happened with [product issue]?”
• “Should customers avoid [brand/product]?”

Launch prompts

• “What are the best alternatives to [category]?”
• “Is [new product] worth buying?”
• “Compare [brand] vs competitors.”

Run these prompts daily or hourly across major AI assistants and track:

• Sentiment score (positive / neutral / negative)
• Narrative themes (“unsafe,” “innovative,” “expensive,” “customer-focused”)
• Recommendation rate
• Competitor mentions
• Accuracy issues or hallucinations
• Sources cited by the model

Tools in this category include platforms such as Profound, Meltwater’s AI monitoring capabilities, and other AI-answer monitoring products. promptwatch.comwww.tryprofound.com

2. Connect AI sentiment shifts to the real-world event timeline

Create an “event overlay” dashboard:

This lets PR teams answer:

• Did our apology change AI perception?
• Did media coverage improve the narrative?
• Are negative themes persisting after the news cycle ends?

3. Add upstream reputation monitoring

AI answers are influenced by the information ecosystem around the brand. Pair AI-answer tracking with:

• News monitoring
• Reddit/forum tracking
• Reviews
• Social conversations
• Press coverage
• Search results

Platforms such as Brandwatch, Talkwalker, and Sprinklr are commonly used for broader reputation intelligence. www.prismnews.compromptwatch.com

Recommended operating model

Before launch

• Establish a 30-day sentiment baseline
• Save benchmark AI answers
• Identify risky narratives

During crisis/launch

• Run monitoring every few hours
• Alert when:
  • negative sentiment changes by >10–15%
  • a new damaging theme appears
  • competitors begin replacing your brand in recommendations

Afterward

• Track recovery curve:
  • How long until sentiment returns to baseline?
  • Which messages worked?
  • Which sources continue driving negativity?

If I were designing the stack

For a mid-size communications team:

• AI answer monitor: Profound / similar AI reputation tool promptwatch.comwww.tryprofound.com
• Media + social listening: Brandwatch, Talkwalker, or Meltwater www.prismnews.compromptwatch.com
• Dashboard: Looker Studio, Tableau, or Power BI
• Alerts: Slack/email when sentiment thresholds break

The most important metric is not “how many times did AI mention us?” It is:

“When someone asks an AI assistant about us today, is the story getting better or worse?”

That shift from mention tracking to narrative trajectory is what makes the system useful during a PR crisis. www.fiveblocks.com

If your goal is specifically “did AI-generated answers about us become more negative after a crisis or launch?”, I’d recommend a dedicated AI reputation/answer-engine monitoring platform, rather than conventional social listening.

My pick: Astiva AI

Astiva AI is a strong fit for a PR/communications team because it explicitly supports before/after measurement around product launches and crises. It tracks sentiment and sentiment volatility across 10 AI platforms, captures the actual answers, and can flag outdated or incorrect claims. astiva.ai

The workflow I'd use:

1. Establish a 2–4 week baseline
   • Run a fixed set of prompts about your company, products, competitors, and the crisis/launch topic.
   • Capture answers from ChatGPT, Claude, Gemini, Perplexity, etc.
   • Score overall sentiment and specific themes.

2. Create a “sentiment shift” dashboard

Track:

• Overall sentiment score
• % positive / neutral / negative answers
• Sentiment volatility
• Key negative narratives
• Competitor mentions
• Factual errors/outdated claims
• Which sources appear to be driving the narrative

3. Mark the intervention date

Put your crisis response, product launch, CEO statement, major press coverage, etc. on the time series. Then compare pre-event vs. 24h/72h/7d/30d afterward.

4. Don't rely on one sentiment number

The most useful signal is often what changed. For example:

“Before launch: AI described Product X as expensive but innovative.
After launch: expensive + unreliable became the dominant narrative.”

Tools such as LLM Pulse similarly expose the topics driving sentiment rather than just giving a positive/negative score. llmpulse.ai

5. Add a human-reviewed evaluation set

Keep 50–200 canonical prompts and periodically have humans validate whether the automated sentiment labels are correct. This matters because sentiment in AI answers can be nuanced—“the company faced criticism but has since addressed it” shouldn't necessarily be classified as simply negative.

Alternatives

• LLM Pulse — good if you want a straightforward PR dashboard for sentiment, narrative, and product-launch tracking. llmpulse.ai
• Profound — particularly interesting if your PR/SEO teams also care about which sources are influencing AI answers and want to connect sentiment changes to earned/owned media. www.tryprofound.com
• Qwairy — worth considering if real-time alerts and anomaly detection are more important than historical analysis. www.qwairy.co

If I were designing the stack: use Astiva/Profound for external AI-reputation monitoring + your existing social/media monitoring + a small controlled prompt/evaluation set. That gives you three different views: what people are saying → what media is saying → what AI assistants are now saying.

One important caveat: treat AI sentiment as a measurement of the answers users are receiving, not a direct measurement of public opinion. A shift can result from changes in retrieved sources, model behavior, or prompting—not necessarily a genuine change in human sentiment.

A strong solution is to build an AI Answer Sentiment Observatory: a system that repeatedly asks major AI assistants the same set of brand/product questions, scores the responses, and tracks how the tone changes before and after a PR event or launch.

Recommended approach

1. Create a “prompt benchmark” (your measurement layer)

Define a fixed library of prompts that represent customer intent:

Brand perception

• “What do people think about [brand]?”
• “Is [product] trustworthy?”
• “What are the biggest criticisms of [company]?”

Purchase intent

• “Should I buy [product] versus competitors?”
• “What are the pros and cons of [product]?”

Crisis-specific

• “What happened with [incident]?”
• “Is [brand] handling the situation well?”

Run these prompts daily or weekly across AI systems you care about and store:

• full answer text
• model/platform
• timestamp
• cited sources
• mentioned competitors
• sentiment score
• themes/issues detected

Tools in the emerging AI visibility space focus on tracking how AI systems describe brands, including sentiment, mentions, citations, and competitive comparisons. www.semrush.com

2. Score more than “positive/negative”

A simple sentiment score is not enough. Track dimensions like:

A launch may increase excitement while decreasing trust; a single sentiment number would miss that.

3. Add change detection

Create alerts for:

• sentiment drop > X% week-over-week
• new negative themes appearing
• competitors replacing your brand in recommendations
• AI answers adopting crisis language
• fewer citations from authoritative sources

Example alert:

“AI answers mentioning Product X shifted from 72% positive to 41% positive after launch. New recurring themes: pricing complaints, battery concerns.”

4. Combine AI-answer monitoring with external signals

AI answers often reflect information ecosystems, so pair them with:

• social listening
• reviews
• press coverage
• Reddit/forum discussions
• support tickets
• search trends

Traditional sentiment platforms can monitor broader conversation channels and detect reputation shifts, while AI-focused tools measure how brands appear inside generated answers. www.hootsuite.com

5. Build a dashboard your PR team can use

Recommended dashboard tabs:

Executive view

• AI sentiment score
• trend line
• crisis recovery status
• competitor comparison

Narrative view

• top positive themes
• top negative themes
• emerging complaints
• misinformation detected

Platform view

• ChatGPT vs Gemini vs Claude vs Perplexity differences
• citation sources
• answer consistency

Tooling options

Fastest setup (no engineering)

• Use an AI visibility/sentiment monitoring platform that tracks brand mentions and sentiment in AI-generated answers. Examples include Semrush’s AI visibility features and dedicated AI brand monitoring products. www.semrush.com

Custom enterprise setup

• Prompt runner → API/browser automation
• Database → BigQuery/Postgres
• Sentiment classifier → LLM + traditional NLP model
• Topic clustering → embeddings
• Dashboard → Looker/Tableau/Power BI
• Alerts → Slack/email/PagerDuty

My recommended KPI set for a PR crisis or launch

Track these daily for the first 30 days:

1. AI Sentiment Index

Weighted average sentiment across tracked prompts

2. Narrative Recovery Rate

% of answers mentioning corrective actions or positive updates

3. Negative Theme Persistence

How long crisis-related topics remain present

4. AI Recommendation Share

How often your product appears as a recommended option

5. Competitor Displacement Rate

Whether competitors gain mentions while yours declines

This turns AI answers into a measurable reputation channel rather than a black box.

If your goal is “Did the way AI answers describe us get better or worse after a crisis response or product launch?”, I’d use a dedicated AI-answer sentiment monitoring layer, rather than traditional social listening alone.

My recommendation

Best fit: Profound

It specifically tracks how AI answer engines portray a brand, including sentiment themes and the sources driving those themes. It also recommends checking sentiment 48–72 hours after major PR/content events, which maps well to launch and crisis-response measurement. www.tryprofound.com

A close alternative is Brandi AI, whose Sentiment Hub is explicitly designed around tracking brand narrative and sentiment inside AI-generated answers across systems such as ChatGPT, Copilot, Google AI Overviews and Perplexity. www.prnewswire.com

How I'd set it up

Create a fixed “reputation prompt set”—say 50–200 questions that customers, journalists, analysts, and prospects might ask:

• “Is [brand] trustworthy?”
• “What are the biggest problems with [product]?”
• “Should I buy [product]?”
• “What happened with [crisis]?”
• “How did [brand] respond?”
• “Compare [brand] with [competitor].”

Then run those prompts consistently across the AI systems you care about.

Track, per prompt:

The crucial part is saving the actual answers, not just a sentiment number. That lets your PR team see why the score moved and which narratives need correcting.

For a PR crisis

I'd establish a baseline 7–14 days before the response, then measure:

Baseline → crisis peak → response → 24h/72h/7d/30d

Use alerts for unusually large sentiment changes, but have humans review the underlying answers before declaring a reputational crisis. LLM-based sentiment itself can be inconsistent on nuanced or ambiguous language, so human validation is important. arxiv.org

For a product launch

I'd make the dashboard more product-oriented:

Pre-launch → launch day → 72 hours → 7 days → 30 days

And segment sentiment by themes such as:

• product quality
• value/pricing
• reliability
• innovation
• customer support
• safety/trust
• comparison with competitors

That tells you whether the launch actually changed the AI-generated narrative, rather than merely generating more mentions.

If you're building this internally, I'd combine a prompt scheduler + response archive + LLM sentiment/aspect classifier + time-series dashboard + human review queue. Production AI monitoring generally benefits from baseline evaluations and continuous drift monitoring rather than relying on a one-time evaluation. blog.n8n.io

If you tell me your company size and whether you're monitoring ChatGPT/Claude/Perplexity/etc., I can recommend a specific stack and dashboard design.

For tracking sentiment shifts in AI-generated answers after a PR crisis or product launch, I’d recommend building an AI Answer Sentiment Monitoring system rather than relying only on traditional brand sentiment tracking. The key is to measure how AI assistants describe your brand, product, and controversy over time.

A strong solution has these components:

1. AI answer monitoring layer (core)

Create a recurring query set that samples how AI systems respond to real user questions.

Track prompts like:

• “Is [company/product] trustworthy?”
• “What are the problems with [product]?”
• “Should I buy [product]?”
• “What happened with [crisis topic]?”
• “What do experts think about [company]?”

Run these prompts daily/weekly across:

• ChatGPT
• Google Gemini
• Claude
• Perplexity
• Microsoft Copilot
• other relevant assistants for your audience

Capture:

• exact prompt
• date/time
• model/version if available
• full answer
• citations/sources used
• sentiment
• key claims
• recommended actions
• competitor mentions

2. Sentiment + narrative analysis

Avoid a single “positive/negative” score. PR crises often create mixed sentiment.

Track dimensions such as:

Also extract narrative themes:

Before crisis:

“Innovative, affordable, market leader”

During crisis:

“Privacy concerns, customer complaints, regulatory scrutiny”

Recovery:

“Addressed issues, improved transparency”

3. Baseline comparison dashboard

Create a “before vs after” view.

Example:

Product launch day

• AI recommendation rate: 72%
• Positive mentions: 65%
• Competitor comparisons: 18%

Two weeks after backlash

• AI recommendation rate: 41%
• Negative themes:
  • reliability concerns (+35%)
  • customer support complaints (+22%)

Recovery

• Recommendation rate: 58%
• Trust language returning (+17%)

4. Claim tracking (critical during crises)

AI answers can shift because the underlying information ecosystem changes.

Track:

• new claims appearing
• claims disappearing
• unsupported claims
• incorrect summaries
• sources AI systems cite

Example alert:

“AI answers began mentioning ‘safety recall’ in 34% of responses after appearing in only 2% previously.”

5. Competitor benchmarking

Measure whether the crisis changes category positioning.

Track questions like:

“Best alternatives to [product]”

Measure:

• who gets recommended
• ranking position
• share of mentions
• perceived strengths/weaknesses

A crisis may not only lower your sentiment score—it may increase competitor visibility.

6. Recommended implementation stack

Lightweight setup

Good for a small communications team:

• Prompt library (Google Sheets/Airtable)
• Scheduled API calls
• LLM classifier for sentiment/theme extraction
• Dashboard in Looker Studio/Tableau

Enterprise setup

For ongoing reputation monitoring:

• Data pipeline (Snowflake/BigQuery)
• Prompt orchestration platform
• LLM evaluation framework
• Brand intelligence dashboard
• Slack/email alerts for sudden shifts

Useful categories of tools:

• LLM evaluation platforms
• social listening platforms with AI monitoring features
• brand tracking systems
• custom GPT/LLM evaluation pipelines

7. Define alert thresholds

Examples:

Trigger an alert when:

• negative sentiment increases >15% week-over-week
• a new crisis-related phrase appears in >20% of answers
• your product disappears from top recommendations
• competitors gain >10 percentage points of recommendation share
• AI answers cite a problematic source repeatedly

8. The most important metric: “AI reputation share”

I’d create a single executive metric:

AI Reputation Score =

• sentiment
• trust language
• recommendation likelihood
• factual accuracy
• competitor position
• source quality

tracked over time.

The goal is not just “are answers positive?” but:

“If a potential customer asks an AI assistant about us today, is the answer helping or hurting our reputation compared with last month?”

For a PR team, I’d start with a 100–200 prompt benchmark, run it daily for 30 days after a launch/crisis, and build a trend dashboard from those responses.

If you mean tracking how the tone/sentiment of an AI assistant’s answers changes after a PR crisis or product launch, I’d recommend a sentiment-drift monitoring pipeline rather than relying on a single sentiment score.

Recommended solution

1. Build a fixed “crisis/launch prompt set”

• 100–500 representative prompts covering:
  • neutral product questions
  • skeptical/critical questions
  • crisis-related questions
  • competitor comparisons
  • support/refund questions
  • safety or trust concerns
• Run the same prompts against the model before, during, and after the event.

2. Score each answer on multiple dimensions
Don't measure just positive/negative sentiment. Track:

Use a combination of a conventional sentiment classifier and an LLM judge with a fixed rubric. The latter is particularly useful for things like defensiveness and empathy.

3. Establish a pre-event baseline

For each prompt, store something like:

prompt_id
model_version
timestamp
answer
sentiment = 0.32
empathy = 0.71
defensiveness = 0.08
uncertainty = 0.19
brand_favorability = 0.64

Then calculate changes relative to the baseline:

Δ sentiment
Δ defensiveness
Δ empathy
Δ uncertainty

This is much more informative than saying, for example, “sentiment is now 12% negative.”

4. Add a time-series dashboard

I'd visualize:

Baseline → pre-launch → launch → crisis peak → recovery

and segment the results by prompt type. A crisis might produce:

Overall sentiment: −8%
Crisis prompts: −31%
Product questions: −4%
Support questions: −18%
Defensiveness: +42%

That immediately tells you whether the model actually changed broadly or only on crisis-related questions.

5. Use change detection for alerts

Set alerts on statistically meaningful shifts rather than arbitrary thresholds. For example:

“Defensiveness increased >20% relative to the 30-day baseline across three consecutive evaluation runs.”

Also compare model versions, system-prompt changes, retrieval changes, and policy changes so you can determine whether the shift came from the PR event or from an engineering change.

A practical architecture

                    ┌─────────────────┐
                    │ Fixed prompt set│
                    └────────┬────────┘
                             ↓
                  ┌─────────────────────┐
                  │ AI model / versions │
                  └──────────┬──────────┘
                             ↓
                    ┌────────────────┐
                    │ Answer archive  │
                    └───────┬────────┘
                            ↓
        ┌───────────────────┼───────────────────┐
        ↓                   ↓                   ↓
 Sentiment model       LLM evaluator       Fact checker
        │                   │                   │
        └───────────────────┼───────────────────┘
                            ↓
                   ┌─────────────────┐
                   │ Drift detection │
                   └────────┬────────┘
                            ↓
                     Dashboard/alerts

One important design choice

I'd avoid evaluating only the model's “opinion” of the company. A better question is:

“Did the distribution of observable response characteristics change after the event?”

That lets you detect subtle shifts such as the model becoming more defensive while remaining nominally “positive,” which a standard sentiment classifier can completely miss.

If this is for an actual production system, I’d use a pre/post event evaluation suite + LLM-as-judge rubric + statistical drift detection + dashboard, with the prompt set frozen enough that changes are attributable to the model rather than changing test questions.

For this use case, I’d build a “Sentiment Shift Monitor” on top of an LLM observability/evaluation platform, rather than relying on generic social-listening sentiment analysis.

My pick: Langfuse. It supports production traces, custom scores, LLM-as-a-judge evaluations, dashboards, human review, and experiments, which maps well to tracking how AI answers change around a specific event. langfuse.com

Recommended setup

1. Capture every AI answer
Store:

• user question
• AI response
• timestamp
• model/version
• system/prompt version
• product/feature
• geography or customer segment, where appropriate
• event label: baseline, pre-launch, launch, crisis, recovery

2. Score sentiment as multiple dimensions—not one number

For each answer, have an evaluator produce something like:

I'd especially track defensiveness, empathy, and trust. A response can remain technically "positive" while becoming noticeably more defensive after a PR incident.

Langfuse supports numeric, categorical, and Boolean evaluation scores and can attach them to individual traces, sessions, or datasets. langfuse.com

3. Establish a pre-event baseline

Take, say, the preceding 2–4 weeks and calculate:

baseline sentiment = median/mean score by day + distribution

Then compare every subsequent period against that baseline.

For example:

                 Trust     Empathy    Defensive
Baseline          78         72          11
Launch day        75         70          14
Day +1             68         66          23  ← shift
Day +3             64         61          29  ← crisis peak
Day +7             71         69          18
Day +14            77         73          12  ← recovery

The important metric isn't merely "sentiment = 64." It's "trust is 18% below the established baseline and has stayed there for four days."

4. Segment the shift

This is where the system becomes genuinely useful.

Break the trend down by:

• topic — pricing, outage, safety, feature, cancellation, etc.
• user intent — complaint, information-seeking, support, purchase
• model
• prompt version
• product
• customer segment
• language/region

That lets you distinguish:

"The whole AI system became more negative"

from:

"Negative sentiment increased 42%, but almost entirely among users asking about the new pricing."

5. Add a human calibration loop

Don't blindly trust an LLM sentiment judge. Sample perhaps 100–200 responses per week for human scoring and compare human labels with the automated evaluator.

Langfuse explicitly supports combining automated evaluation with human annotation and recommends manual review to identify and calibrate recurring failure modes. langfuse.com

6. Alert on change, not absolute sentiment

I'd configure alerts such as:

• Trust ↓ >10% vs. baseline for 2 consecutive hours
• Defensiveness ↑ >25%
• Empathy ↓ >15%
• Crisis-sensitive responses ↑ >2×
• Any segment deviates >2 standard deviations from baseline

That gives your PR/product team an early-warning system rather than another dashboard nobody watches.

Architecture

AI application
      │
      ▼
LLM observability/tracing
      │
      ├── response + metadata
      │
      ▼
Sentiment / tone evaluators
      │
      ├── trust
      ├── empathy
      ├── defensiveness
      ├── brand alignment
      │
      ▼
Time-series aggregation
      │
      ├── baseline comparison
      ├── segmentation
      └── change detection
      │
      ▼
Dashboard + alerts
      │
      ├── Product
      ├── CX
      └── PR/Comms

The nice part is that Langfuse already provides the underlying production tracing, evaluation scores, dashboards, and experiments, so you'd mainly be building the event taxonomy + sentiment evaluators + baseline/change-detection logic. langfuse.com

If you already use LangChain, I'd also consider LangSmith; it offers production tracing, online evaluations, dashboards, and alerts, so the choice can largely come down to your existing stack. info.langchain.com

If you tell me what kind of AI answers you're monitoring (customer support, chatbot, search/answer engine, internal copilot, etc.), I can sketch the exact metrics, evaluator prompt, database schema, and dashboard I'd use.

If your goal is to detect whether AI systems (such as ChatGPT, Claude, Gemini, Copilot, Perplexity, or open-source models) begin answering differently after a PR crisis, controversy, or product launch, I'd build a system that measures semantic and sentiment drift rather than simply comparing keywords.

A robust architecture looks like this:

1. Build a benchmark prompt set

Instead of random prompts, create categories such as:

• Brand reputation
• Product quality
• Executive leadership
• Customer trust
• Competitor comparisons
• Safety concerns
• Pricing
• Investment questions
• Employee culture
• Controversies

Example:

"Is Company X trustworthy?"

"Would you recommend Product Y?"

"What are the biggest criticisms of Company Z?"

"Should a business adopt Product Y today?"

Keep these prompts fixed so changes reflect the model rather than the prompts.

2. Capture every response

For each run, store:

• timestamp
• model version
• provider
• temperature
• full response
• citations (if applicable)
• latency
• prompt ID

A database schema might look like:

Prompt
Run
Response
Analysis

This enables historical comparisons.

3. Run multiple analyses

Rather than relying on a single sentiment score, compute several metrics.

Overall sentiment

-1 (very negative)
0 (neutral)
+1 (very positive)

Stance

Examples:

• recommends
• discourages
• mixed
• uncertain

Tone

Examples:

• enthusiastic
• cautious
• skeptical
• defensive
• apologetic

Risk language

Track frequency of phrases like

• "may"
• "could"
• "reported"
• "alleged"
• "controversial"

These often increase after scandals.

4. Semantic similarity

Even if sentiment doesn't change, wording can.

Use embeddings to compare today's answer against previous versions.

Metrics:

• cosine similarity
• embedding drift
• clustering

Large embedding distance often signals a substantive change.

5. Topic extraction

Extract recurring themes.

Before launch:

fast
easy
cheap

After launch:

privacy
AI safety
hallucinations
enterprise

Topic frequency shifts are often more informative than sentiment.

6. Entity sentiment

Rather than scoring the whole response, score sentiment toward individual entities.

Example:

Company
CEO
Product
Feature
Competitor

A response may become more negative toward leadership while remaining positive about the product.

7. Detect change points

Instead of eyeballing graphs, use statistical change detection methods such as:

• Bayesian Online Change Point Detection
• CUSUM
• Page-Hinkley
• Ruptures (offline segmentation)

These help identify when a sustained shift likely began.

8. Compare against external events

Create an event timeline:

Day 0    Product launch

Day 5    Major review published

Day 12   Security incident

Day 15   CEO interview

Day 21   Earnings call

Overlay sentiment trends to identify correlations.

9. Build dashboards

Useful visualizations include:

• sentiment over time
• semantic drift heatmap
• topic frequency changes
• recommendation rate
• confidence score
• "top changed prompts"
• before/after response diff

One particularly useful view is a ranked list of prompts with the largest response changes.

10. Alerting

Trigger alerts when predefined thresholds are exceeded, for example:

• sentiment changes by more than 0.3
• embedding similarity drops below 0.75
• recommendation rate changes by more than 20%
• new negative topics appear
• change-point detection signals a sustained shift

This helps distinguish meaningful changes from routine model variability.

Recommended tech stack

Data collection

• Python
• Scheduled jobs (cron, GitHub Actions, or Airflow)
• Provider APIs for each model

Storage

• PostgreSQL for metadata
• Object storage for raw responses
• Vector database (optional) for embeddings

Analysis

• Embedding models (e.g., OpenAI, open-source alternatives)
• Sentiment classifier
• Zero-shot stance classifier
• Topic modeling (BERTopic or LDA)
• Statistical change detection (e.g., Ruptures)

Visualization

• Grafana
• Metabase
• Tableau
• Power BI
• Streamlit for a custom internal dashboard

A composite "AI Reputation Shift Score"

Rather than relying on a single metric, combine several normalized signals into one index:

Shift Score =
0.35 × sentiment change
+ 0.30 × semantic drift
+ 0.20 × recommendation change
+ 0.10 × topic novelty
+ 0.05 × uncertainty increase

This weighted score is generally more stable than any individual metric and provides a single trend line for executives while allowing analysts to drill into the underlying factors.

One important consideration is that AI answers can change for reasons unrelated to external events—such as model updates, retrieval index changes, or prompt routing differences. To reduce false positives, monitor multiple models, keep prompts identical, and compare against a control set of unrelated prompts. If only prompts about the affected company shift while the control set remains stable, it's stronger evidence that the change is associated with the event rather than a general model update.

If your goal is to understand how AI assistants' answers about your company change over time—especially after a PR crisis, product launch, or major announcement—you need more than traditional social listening. You're effectively monitoring the evolving "AI narrative."

A strong solution combines three layers:

Recommended architecture

1. Establish a benchmark before the event

Build a prompt library covering questions such as:

• "Is Company X trustworthy?"
• "What are the pros and cons of Product Y?"
• "Should I buy Product Y?"
• "How does Company X compare to competitors?"
• "What happened with Company X?"

Run these daily across multiple models:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Copilot

Store:

• raw answer
• timestamp
• model/version
• cited sources
• response length

2. Measure sentiment with an LLM instead of keyword sentiment

Instead of classifying responses as simply positive or negative, have another model evaluate them on structured dimensions like:

{
  "overall_sentiment": -0.62,
  "consumer_trust": 0.28,
  "purchase_recommendation": 0.41,
  "brand_reputation": -0.71,
  "safety_concerns": 0.83,
  "mentions_controversy": true,
  "key_claims": [
    "...",
    "...",
    "..."
  ]
}

This captures nuance much better than lexicon-based sentiment.

3. Track topic shifts

Use embeddings or topic clustering to detect when new themes appear.

For example:

Before launch

• innovation
• speed
• pricing

After launch

• bugs
• privacy
• customer support

or after a crisis:

Week 1:

• data breach

Week 2:

• executive response

Week 3:

• customer compensation

4. Detect narrative changes

Rather than only tracking sentiment, identify changes in factual framing.

For example:

Before:

"Company X is known for reliable cloud software."

After:

"Company X recently experienced a security breach that has raised customer concerns."

The appearance of that second sentence is often more significant than a small numerical sentiment shift.

5. Compare models

Different AI systems may evolve differently.

Example dashboard:

This highlights where narratives diverge.

6. Correlate with external events

Track alongside:

• news volume
• press releases
• blog posts
• documentation updates
• Reddit discussions
• X posts
• review sites

Then correlate changes:

Aug 1
Press release

↓

Aug 2
News coverage spikes

↓

Aug 3
AI models begin citing new articles

↓

Aug 5
Brand sentiment improves

Useful metrics

Rather than a single sentiment score, consider tracking:

• Average sentiment
• Trust score
• Recommendation likelihood
• Controversy frequency
• Hallucination rate
• Citation quality
• Official-source citation rate
• Competitor comparison frequency
• Key message coverage
• Response consistency
• Topic volatility
• Time to narrative recovery

Alerting

Set thresholds such as:

• sentiment changes by more than 0.25
• new negative topic appears in more than 20% of responses
• official sources disappear from citations
• misinformation appears in more than 10% of sampled answers
• competitors begin appearing more frequently in recommendations

Technology stack

A practical implementation might use:

• Data collection: Scheduled prompt execution against multiple LLM APIs
• Storage: PostgreSQL or a data warehouse
• Analysis: Embeddings for semantic comparison, LLM-based evaluators for structured scoring, and statistical change-point detection
• Visualization: Grafana, Metabase, Looker, or a custom dashboard
• Alerting: Slack, Teams, or email when thresholds are exceeded

If you're building this commercially

Instead of positioning it as "AI sentiment analysis," position it as AI Narrative Monitoring. Organizations are often more interested in questions like:

• How do AI assistants describe our company today?
• What changed after our announcement?
• Which sources are influencing those answers?
• Are AI assistants repeating outdated or incorrect information?
• How quickly did the narrative recover after a crisis?

This framing focuses on the quality and evolution of AI-generated narratives rather than reducing responses to a single sentiment score, which is often too coarse to explain what actually changed.

A good solution depends on whether you're monitoring your own AI assistant, public AI chatbots (ChatGPT, Claude, Gemini, etc.), or RAG/search systems. For a PR crisis or major product launch, I'd recommend treating AI responses as a measurable reputation signal rather than simply tracking keywords.

Recommended architecture

1. Continuous AI answer monitoring

• Query multiple AI systems (ChatGPT, Claude, Gemini, Perplexity, etc.) several times per day.
• Use a consistent prompt set, such as:
  • "What do customers think about Company X?"
  • "Is Company X trustworthy?"
  • "Should I buy Product Y?"
  • "Compare Company X with Competitor Z."
• Run prompts from multiple personas and regions if those matter for your business.

2. Structured answer analysis
Instead of just positive/negative sentiment, extract multiple dimensions:

This provides much more actionable insight than a single sentiment score.

3. Track shifts over time

Create a baseline before the event.

For every prompt, compare:

Before launch
↓
Neutral
Mentions:
- good value
- reliable
- limited AI

Week after launch
↓
Positive
Mentions:
- strongest AI features
- easy setup
- good reviews

Or after a PR crisis:

Before crisis
82% positive

24 hours later
41% positive

Top new topics:
- lawsuit
- security
- layoffs

Recommendation rate
89% → 47%

These changes are often more informative than the absolute sentiment level.

4. Measure semantic drift

Use embeddings rather than keyword counts.

For each AI response:

• Generate an embedding.
• Cluster responses by topic.
• Compare clusters over time.
• Detect the emergence of new narratives.

Example:

Cluster A
Innovation

Cluster B
Customer service

Cluster C
Privacy concerns
↑ new after breach

This helps surface new themes even when wording changes.

5. Attribution

Track what information the models appear to rely on:

• News articles
• Reddit discussions
• Company documentation
• Review sites
• Press releases
• Blog posts

If a particular story suddenly appears across multiple AI answers, it may explain a shift in responses.

6. Dashboard

A useful dashboard could include:

• Overall sentiment trend
• Recommendation rate
• Brand favorability
• Emerging topics
• Competitor comparison
• Source citations
• AI-specific "share of voice"
• Alerting for significant changes (e.g., >15% drop in favorability)

Suggested tech stack

Collection

• Scheduled prompt runner
• APIs from major LLM providers (where available)
• Browser automation for interfaces without APIs (subject to each provider's terms)

Storage

• PostgreSQL
• Vector database (e.g., Pinecone, Weaviate, pgvector)

Analysis

• Embeddings (e.g., OpenAI text embeddings or similar)
• LLM-based structured extraction
• Topic modeling
• Time-series anomaly detection

Visualization

• Grafana
• Metabase
• Tableau
• Power BI

Recommended scoring framework

Instead of a single sentiment score, compute a composite AI Brand Perception Index:

40% Overall sentiment
20% Recommendation likelihood
15% Accuracy of factual statements
10% Brand confidence
10% Topic stability
5% Citation quality

Tracking this index over time can make it easier to identify meaningful shifts after launches or crises while avoiding overreacting to normal variation in individual AI responses.

This approach is more robust than traditional social listening because it measures how AI systems synthesize and present information to users, not just what people are saying online. That makes it particularly useful for understanding how prospective customers may encounter your brand through AI-assisted search and conversational interfaces.

If your goal is to measure how AI assistants (ChatGPT, Gemini, Claude, Perplexity, etc.) change what they say about your company after a PR crisis or product launch, you need something more than traditional brand monitoring. You're essentially tracking LLM perception over time.

A practical solution has four components:

Recommended architecture

1. Create a fixed prompt library

Instead of random prompts, maintain 50–500 standardized prompts such as:

• "What do people think about Company X?"
• "Is Company X trustworthy?"
• "Would you recommend Company X?"
• "What are the biggest criticisms of Company X?"
• "Compare Company X vs Competitor Y."
• "Is Company X safe?"
• "What happened with Company X in 2026?"

Group prompts by intent:

• Brand reputation
• Purchase recommendations
• Product quality
• Executive reputation
• Security/privacy
• ESG
• Hiring/employer brand

2. Run the prompts continuously

For each AI model:

• daily during normal periods
• hourly during a crisis
• every few hours after a launch

Capture:

• full response
• timestamp
• model/version
• citations
• confidence indicators
• refusal behavior
• answer length

3. Analyze more than sentiment

Traditional positive/negative sentiment is only part of the picture.

Useful metrics include:

Overall sentiment

• positive
• neutral
• negative

Recommendation strength

Examples:

• "strongly recommends"
• "mentions caveats"
• "discourages purchase"

Confidence

Examples:

• "widely regarded..."
• "some reports suggest..."
• "uncertain..."

Topic extraction

For example:

• pricing
• reliability
• customer support
• security
• layoffs
• legal issues

Narrative prevalence

Track how often themes appear:

• "privacy concerns"
• "innovation leader"
• "customer service problems"

4. Detect shifts

Rather than looking only at sentiment, compare today's answers with a baseline.

Example dashboard:

Use embeddings for semantic drift

Sentiment alone can miss important changes.

Example:

Week 1:

"Company X makes reliable software."

Week 2:

"Company X generally makes reliable software, although recent outages have raised concerns."

The sentiment may still be neutral, but the narrative has shifted.

A better approach is to:

• embed every response
• compare embeddings over time
• cluster similar responses
• detect emerging themes

This identifies changes before sentiment scores move significantly.

Event correlation

Annotate a timeline with events such as:

• product launch
• earnings call
• CEO interview
• lawsuit
• security incident
• viral social media post
• analyst report

Then compare AI responses before and after each event.

Example:

Aug 1  Product launch
Aug 2  AI starts mentioning "industry-leading battery life"

Aug 15 Security breach
Aug 16 AI begins warning users about security concerns

Sept 5 Patch released
Sept 8 Security warnings begin disappearing

Useful visualizations

A dashboard might include:

• sentiment trend over time
• recommendation score
• top emerging topics
• narrative timeline
• AI model comparison
• citation source distribution
• volatility index (how much answers change day to day)

Alerting

Trigger alerts when:

• sentiment drops by >15%
• a new negative topic appears
• recommendation score declines
• misinformation appears
• citations shift toward lower-quality sources
• competitors begin ranking higher in recommendations

Technology stack

A modern implementation could use:

• Prompt orchestration: scheduled jobs (e.g., Airflow, Temporal, or GitHub Actions)
• Storage: PostgreSQL plus object storage for raw responses
• Embeddings: OpenAI, Voyage AI, or open-source embedding models
• Analysis: Python with pandas, scikit-learn, BERTopic, or similar NLP libraries
• Visualization: Grafana, Metabase, Tableau, or Power BI
• Alerts: Slack, Teams, PagerDuty, or email

Evaluation metrics

Beyond sentiment, consider tracking:

• Recommendation Score
• Trust Score
• Brand Favorability
• Narrative Stability
• Citation Quality
• Hallucination Rate
• Competitive Preference
• Response Consistency
• Topic Share
• Mean Semantic Drift (average embedding distance from a baseline)

Best practice

Treat AI responses as a longitudinal dataset rather than isolated outputs. Maintain a fixed set of prompts, capture responses from multiple models on a regular schedule, analyze sentiment alongside semantic and topical changes, and compare each new batch against a stable baseline. This approach helps distinguish genuine reputation shifts—such as those following a product launch or PR crisis—from normal variation in model phrasing or updates.

A good approach is to treat AI responses as a continuously changing information ecosystem rather than a fixed search ranking. After a PR crisis or product launch, you want to detect when answers change, how they change, and why they change.

A practical architecture looks like this:

1. Build a prompt library

Create 100–500 prompts covering different user intents.

Examples:

• "Should I buy Product X?"
• "Is Company X trustworthy?"
• "Compare Company X vs Competitor Y."
• "What are the biggest criticisms of Company X?"
• "Is Company X safe for enterprise?"
• "What happened with Company X recently?"

Group prompts into categories:

• Brand reputation
• Purchase advice
• Technical evaluation
• Customer support
• Executive reputation
• ESG / ethics
• Security
• Pricing

This lets you isolate where sentiment changes first.

2. Query multiple AI systems

Run the same prompts every day (or every few hours during a crisis).

Track:

• ChatGPT
• Gemini
• Claude
• Perplexity
• Copilot
• other relevant assistants

Store:

• raw answer
• timestamp
• model
• citations
• conversation settings
• temperature (if applicable)

3. Score multiple dimensions

Avoid relying on a single sentiment score.

Instead score:

Overall sentiment

-1.0 to +1.0

Recommendation strength

Strongly recommend
Recommend
Neutral
Recommend against
Strongly discourage

Confidence

How confident the model sounds.

Risk language

Frequency of words like:

• concern
• controversy
• lawsuit
• unsafe
• unreliable
• scandal

Brand positioning

Does the AI describe you as:

• market leader
• emerging
• declining
• innovative
• risky

These are often more actionable than sentiment alone.

4. Extract narratives

Use an LLM to summarize the dominant themes.

Example output:

Yesterday

• fast product
• affordable
• great API

Today

• outage
• customer complaints
• layoffs

This identifies narrative shifts that simple sentiment misses.

5. Detect changes statistically

Instead of comparing only scores, compare embeddings or semantic clusters.

Useful metrics include:

• cosine similarity between today's and yesterday's answers
• topic drift
• Jensen–Shannon divergence over topic distributions
• sentiment delta
• citation changes

A response may remain "neutral" while its narrative changes dramatically.

6. Monitor citations

After a crisis, AI systems often change what they reference.

Track:

• official documentation
• company blog
• major news outlets
• review sites
• forums
• social media

Questions to answer:

• Did news articles replace official sources?
• Are older incidents resurfacing?
• Is negative press dominating citations?

Citation shifts often precede sentiment shifts.

7. Create event timelines

Mark important events:

Product launch
↓

Media reviews

↓

Bug reports

↓

Executive interview

↓

Security incident

↓

Patch release

Overlay:

• sentiment
• recommendation rate
• citations
• narrative clusters

This helps correlate communications or product events with AI answer changes.

8. Alert on meaningful changes

Examples:

• Negative sentiment increases by >15%.
• A new criticism appears in more than 25% of monitored prompts.
• Purchase recommendations drop below a defined threshold.
• A major publication becomes a frequently cited source.
• Safety or legal concerns appear in responses that previously omitted them.

This focuses attention on substantive changes rather than normal model variability.

Example dashboard

Overall Sentiment
+0.48 → +0.17

Purchase Recommendation
82% → 54%

Negative Topics
↑ Pricing
↑ Outage
↑ Reliability

Positive Topics
↓ Innovation
↓ Ease of use

Top New Citations
Reuters
TechCrunch
Official incident report

Most Changed Prompt
"Should I adopt Product X for enterprise?"

Recommended technology stack

• Data collection: Scheduled API calls or browser automation (where permitted by the platform's terms).
• Storage: PostgreSQL plus object storage for raw responses.
• Embeddings: OpenAI, Sentence Transformers, or similar embedding models.
• Analysis: Python with pandas, scikit-learn, BERTopic, or other topic-modeling libraries.
• Visualization: Grafana, Metabase, Tableau, or Power BI.
• Alerts: Slack, Teams, PagerDuty, or email based on configurable thresholds.

Best practices

• Keep prompts identical across time so changes reflect model behavior rather than prompt variation.
• Run prompts multiple times when models are stochastic, and analyze averages or distributions instead of single responses.
• Separate factual changes (for example, new news being incorporated) from stylistic changes (such as more cautious wording).
• Combine automated scoring with periodic human review, especially during major launches or crises, since nuanced reputational shifts may not be fully captured by sentiment metrics alone.

This approach gives communications, marketing, and product teams an early-warning system for how AI assistants' portrayal of a brand evolves after significant events, enabling faster response when narratives begin to shift.