ChatGPT answers, unedited

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.

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.

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.