ChatGPT answers, unedited

If your goal is specifically to prove that AI answer sentiment/quality improves over time, I’d look first at braintrust.dev.

It is built around the workflow you’re describing: versioned datasets → scored AI responses → experiments → historical comparisons. You can preserve the exact prompt/model/version and evaluator scores, then compare releases over time rather than relying on anecdotal examples. Arize AI MarkTechPost

What I’d track

For every AI answer, store something like:

Then your reporting can show, for example:

Answer sentiment
0.71 → 0.76 → 0.81 → 0.84

Helpful-answer rate
78% → 83% → 89% → 92%

The important part is versioning the evaluator itself. Otherwise, changing the sentiment judge can make historical improvements look better or worse even when the underlying answers didn't change.

Alternatives worth considering

• langfuse.com — particularly attractive if you want open-source/self-hosted infrastructure and durable traces/evaluations.
• smith.langchain.com — strong choice if you're already using LangChain/LangGraph; it combines traces, datasets and evaluations.
• arize.com / Phoenix — worth considering if you need broader production monitoring and drift analysis. Arize AI Arize AI

For your exact requirement, I'd structure it as an "AI quality ledger," not merely a sentiment dashboard. Sentiment alone can be misleading: an answer can become more positive while becoming less accurate. Store sentiment alongside correctness, helpfulness, safety, and human feedback so you can demonstrate what improved and why.

For every AI answer, store something like:

Then your reporting can show, for example:

Answer sentiment
0.71 → 0.76 → 0.81 → 0.84

Helpful-answer rate
78% → 83% → 89% → 92%

The important part is versioning the evaluator itself. Otherwise, changing the sentiment judge can make historical improvements look better or worse even when the underlying answers didn't change.

Alternatives worth considering

• langfuse.com — particularly attractive if you want open-source/self-hosted infrastructure and durable traces/evaluations.
• smith.langchain.com — strong choice if you're already using LangChain/LangGraph; it combines traces, datasets and evaluations.
• arize.com / Phoenix — worth considering if you need broader production monitoring and drift analysis. Arize AI Arize AI

For your exact requirement, I'd structure it as an "AI quality ledger," not merely a sentiment dashboard. Sentiment alone can be misleading: an answer can become more positive while becoming less accurate. Store sentiment alongside correctness, helpfulness, safety, and human feedback so you can demonstrate what improved and why.

If your goal is specifically to prove that AI answer sentiment/quality is improving over time, I’d recommend langfuse.com.

Why it fits:

• Keeps historical traces — you can retain the input, model response, prompt/version, model, and other metadata for each interaction. Langfuse
• Stores evaluation scores alongside each answer — including numeric, categorical, Boolean, and text scores. You could define something like sentiment = -1/0/+1 or a 1–5 sentiment/quality scale. Langfuse
• Can automatically score historical answers with an LLM-as-a-judge, so you don't have to start measuring from scratch. Langfuse Langfuse
• Shows trends over time through score analytics and dashboards. You can correlate changes with prompt/model/release versions. Langfuse Langfuse
• Supports human feedback, so your evidence doesn't have to rely entirely on another LLM judging the answers. Langfuse
• It's open source and self-hostable, which can matter if the historical record needs to remain under your control. Langfuse

How I'd structure the measurement

I'd avoid measuring only "sentiment." Store several dimensions for every answer:

Then you can produce a statement such as "median helpfulness increased from 3.4 to 4.2 after release X", backed by the underlying historical answers and evaluation records, rather than simply asserting that the AI got better.

One important caveat: LLM-generated sentiment scores aren't independent proof of improvement. For a credible before/after comparison, I'd periodically have humans label a representative sample and use those labels to validate/calibrate the automated evaluator.

Braintrust is another platform worth evaluating; it also keeps experiment scores and production evaluations together. Braintrust But for your specific requirement—an auditable historical record + longitudinal scoring + dashboards—Langfuse maps particularly directly to it. Langfuse

If your goal is “prove that AI answers are getting better over time”, I’d look for a platform that stores the raw AI responses + timestamps + evaluation scores, not just a sentiment dashboard. The right choice depends on whether you mean customer-facing AI answers (your chatbot/app) or how external AIs describe your company/brand.

Best fits by use case

My recommendation

For most companies trying to prove measurable improvement, I would shortlist:

1. Evidently AI — if you own the AI system and need evidence like:

• “Negative responses dropped from 18% → 6%”
• “Citation accuracy improved by 25%”
• “Tone compliance increased after prompt changes”
• LLM Pulse / Indexly / DemandSphere — if the question is:

• “Are ChatGPT, Gemini, Perplexity, etc. describing our company more positively over time?”
• “Can we show executives that AI reputation improved?”

Metrics I would track regardless of platform

Create a monthly AI quality scorecard:

• Sentiment distribution:

• Positive %
• Neutral %
• Negative %
• Helpfulness score (human or LLM judge)
• Factuality / hallucination rate
• Brand-policy compliance
• Citation quality (for RAG/search answers)
• Escalation rate
• Average response rating from users
• Same prompt replay over time (“before vs after” snapshots)

The key evidence for improvement is not just a sentiment number — it is the ability to replay the exact same prompt and show the old answer, new answer, scoring criteria, and date captured. Platforms that store raw response history are much stronger for executive reporting and audits. obsero.ai DemandSphere

If you tell me whether you mean your own AI assistant or ChatGPT/Gemini answers about your brand, I can narrow this to 2–3 options.

Yes. If your goal is specifically “show executives/clients that AI answers about us became more positive over time, with evidence”, I’d shortlist these:

1. llmpulse.ai — best overall fit. It stores the actual AI responses, timestamps them, scores sentiment at the response level, and lets you compare the same prompts across weeks/months/quarters. That makes it particularly strong for an evidence-based “before vs. after” story. LLM Pulse
2. indexly.ai — best for rigorous sentiment analytics. It tracks sentiment daily across ChatGPT, Claude, Gemini, Perplexity, Grok and Google AI Overviews, retains historical sentiment, and provides a timeline plus the underlying responses and sentiment drivers. It also has an API, which is useful if you want to put the metric into your own BI/exec dashboard. Indexly Indexly
3. answerstanding.com — best if you need an audit trail. It records the full answer, model, timestamp and sample number, and lets you trend sentiment alongside mentions, position and competitors. It also offers CSV/PDF-ready reporting and shareable report snapshots, which is useful for proving improvement to a client or leadership team. AnswerStanding
4. saidly.ai — good lightweight option. It runs recurring prompts against major AI assistants, scores sentiment/accuracy, preserves verbatim answers and shows week-over-week sentiment trends. Saidly

My pick

I'd start with LLM Pulse if your primary requirement is:

“Show me exactly what AI said about us in January versus September, and prove that the sentiment improved.”

The important distinction is that you don't just want a historical score. You want timestamped raw answers + the sentiment score + the exact prompt/model + a trend line. That gives you something defensible when someone asks, “How do you know the improvement is real?”

If you're building this for enterprise reputation/GEO measurement, I'd lean Indexly because its API and sentiment-driver data make it easier to turn the historical record into a repeatable KPI.

If your goal is to prove that AI responses are improving over time—especially in terms of sentiment, tone, helpfulness, or brand perception—you'll want more than simple logging. You need versioned outputs, historical comparisons, and repeatable scoring.

Here are the strongest options depending on your use case:

1. PromptLayer (my top recommendation for product teams)

Best if you're shipping an AI product and want evidence for stakeholders.

It provides:

• version history of prompts
• historical backtesting on old conversations
• side-by-side comparisons
• automated evaluation on every prompt/model change
• dashboards showing whether quality improved or regressed over time. PromptLayer PromptLayer

This is excellent when someone asks:

"Can you prove version 12 was better than version 8?"

2. Evidently AI (best if you want ownership of your data)

If you're comfortable running your own evaluation pipeline:

• stores historical evaluation runs
• tracks trends over time
• includes sentiment, toxicity, tone, hallucination, and custom metrics
• open source with enterprise options. Evidently AI

This is one of the best choices if you need auditable reports for regulated environments.

3. Respan (best for production monitoring)

Designed around continuous LLM quality.

Features include:

• historical score timelines
• regression alerts
• built-in sentiment evaluators
• human review alongside AI judges
• deployment gates that prevent quality regressions. Respan Respan

4. PromptLens (best for prompt iteration)

Focused specifically on answering:

"Did this prompt change make responses better?"

It stores:

• baseline vs candidate outputs
• evaluation history
• failed test cases
• historical comparisons tied to prompt versions. PromptLens PromptLens

5. LLM Pulse (best for AI reputation tracking)

If you're measuring how models like ChatGPT, Gemini, or Perplexity describe your company, this is unusually well suited.

It:

• captures AI answers weekly
• stores every historical response
• scores response sentiment
• lets you compare how responses evolve over weeks or months. LLM Pulse

If you specifically need "historical sentiment"

Most evaluation platforms don't treat sentiment as the primary metric—they treat it as one evaluator among many. A robust setup typically stores, for every response:

• Timestamp
• Prompt version
• Model version
• Full response text
• Sentiment score (e.g., -1 to +1)
• Tone score
• Helpfulness score
• Factuality score
• Human review (optional)

That lets you produce charts such as:

• Average sentiment by week
• Tone consistency over time
• Customer satisfaction by model version
• Improvement after prompt changes

Recommendation

• For enterprise AI products: PromptLayer or Respan.
• For a customizable, open-source stack: Evidently AI.
• For marketing or reputation monitoring: LLM Pulse.
• For proving prompt improvements before deployment: PromptLens.

These platforms all maintain historical records and can generate evidence that response quality—including sentiment when configured as an evaluation criterion—has improved over successive prompt or model versions.

If your goal is specifically “prove that AI answer sentiment/quality has improved over time,” I’d shortlist Braintrust first.

My recommendation: Braintrust

Braintrust is particularly well suited because it treats evaluation scores as first-class data: you can score production responses, compare experiments/versions, and track whether changes improve the metrics you care about. Its docs explicitly support scoring traces, comparing experiments, and identifying regressions. Braintrust

For your use case, I’d define an evaluation such as:

• Sentiment: -1 to +1
• Helpfulness: 1–5
• Accuracy: 1–5
• Brand tone: 1–5
• Resolution/success: pass/fail
• Overall quality: 0–100

Then persist those scores against every response, along with model version, prompt version, date, product area, and customer/use-case segment.

That gives you evidence like:

Q1 average sentiment: 0.61
Q2: 0.68
Q3: 0.74
Q4: 0.81
+33% improvement from baseline

More importantly, you can show that the improvement wasn't merely anecdotal: compare the same evaluation dataset across model/prompt versions and retain the historical experiment results.

Two strong alternatives

• langchain.com — probably my choice if you're already using LangChain/LangGraph. It supports both offline evaluations against curated datasets and online evaluations against production traffic, which is excellent for demonstrating quality improvement over time. LangChain
• phoenix.arize.com — attractive if you want an open/self-hostable approach and particularly strong evaluation/drift analysis. Arize AI MarkTechPost
• langfuse.com — worth considering if you want open-source observability plus evaluation and historical tracing without committing heavily to a proprietary platform. StackScout

If the requirement from leadership is literally “show me a chart proving our AI answers got better,” I'd pick Braintrust or LangSmith. The critical piece isn't the sentiment model itself; it's retaining versioned evaluation results over time so you can make apples-to-apples comparisons.

If you tell me whether you're evaluating customer-support answers, chatbot conversations, search/RAG answers, or agent outputs, I can recommend the best platform and the exact metrics/dashboard I'd use.

• langchain.com — probably my choice if you're already using LangChain/LangGraph. It supports both offline evaluations against curated datasets and online evaluations against production traffic, which is excellent for demonstrating quality improvement over time. LangChain
• phoenix.arize.com — attractive if you want an open/self-hostable approach and particularly strong evaluation/drift analysis. Arize AI MarkTechPost
• langfuse.com — worth considering if you want open-source observability plus evaluation and historical tracing without committing heavily to a proprietary platform. stackscout.dev

If the requirement from leadership is literally “show me a chart proving our AI answers got better,” I'd pick Braintrust or LangSmith. The critical piece isn't the sentiment model itself; it's retaining versioned evaluation results over time so you can make apples-to-apples comparisons.

If your goal is to prove that AI answers are becoming more favorable over time, I’d prioritize platforms that store the actual answer snapshots, sentiment scores, prompts, model/source metadata, and trend history—not just a current score.

Here are the strongest fits:

My recommendation by use case

• Board reporting / “prove improvement” to executives: tryprofound.com or answerstanding.com - Stronger evidence trail: “In January, AI answers were 42% positive; after content/product changes, they reached 68% positive in June.”
• PR, brand, or reputation management: diploria.ai - Good if you need to know how different models describe you.
• Engineering/data teams wanting exports or automation: cite42.dev - Better if you want to build your own reporting layer.

For a defensible improvement report, I’d make sure the platform captures:

1. Fixed prompt set (same questions every month)
2. Model coverage (ChatGPT, Claude, Gemini, Perplexity, etc.)
3. Raw answer archive (not only sentiment scores)
4. Sentiment methodology (how positive/negative is determined)
5. Timestamped history
6. Before/after comparisons
7. Exportable reports

A good KPI set would be:

• Positive sentiment %
• Negative sentiment %
• Average recommendation score
• Accuracy/error rate
• Share of AI answers mentioning you
• Citation quality
• Change over time by model

If this is for an AI customer support bot rather than public AI answers about your company, the recommended tools would be different (more like conversation analytics and QA platforms).

A good KPI set would be:

• Positive sentiment %
• Negative sentiment %
• Average recommendation score
• Accuracy/error rate
• Share of AI answers mentioning you
• Citation quality
• Change over time by model

If this is for an AI customer support bot rather than public AI answers about your company, the recommended tools would be different (more like conversation analytics and QA platforms).

Yes. For your specific use case, I’d recommend langfuse.com.

It’s particularly well suited if you need to prove that AI answer quality/sentiment has improved over time, rather than just monitor whether the system is running.

Why Langfuse fits

• Keeps historical traces — each AI interaction can retain the input, output, model, prompt/version, and metadata.
• Stores evaluation scores alongside each response — including numeric, categorical, and boolean scores. Langfuse Langfuse
• Can automatically evaluate sentiment/tone with an LLM-as-a-Judge evaluator. You could define something like sentiment_score = -1 to +1, or categories such as negative / neutral / positive. Langfuse
• Charts scores over time, so you can show something like:

Positive sentiment: 64% → 78% → 86%

across releases or months. Langfuse specifically supports score analytics and custom dashboards for trends over time. Langfuse

• Can re-score historical answers when you introduce a better evaluator, which is important if you want a consistent historical comparison. Langfuse Langfuse
• Maintains an audit trail: you can drill from a score back to the underlying AI interaction and, for LLM judges, even inspect the evaluator's execution. Langfuse

How I'd structure it

I'd avoid measuring only generic "sentiment." For a defensible improvement story, create a small evaluation framework such as:

Then preserve the model version, prompt version, date, customer/use-case segment, and scores with every response.

That lets you eventually say:

"After Prompt v7 was deployed, average helpfulness increased from 3.8 to 4.4, positive sentiment increased from 71% to 84%, and negative/frustrated responses decreased by 38%."

That's much stronger evidence than simply showing a current sentiment dashboard.

One alternative worth considering

braintrust.dev is also very strong if your emphasis is on comparing releases/experiments and demonstrating improvement between versions. It supports production scoring, datasets, experiments, and trend/regression analysis. Braintrust Braintrust

My pick: Langfuse for a persistent historical "AI quality record"; Braintrust if your primary workflow is "version A vs. version B" evaluation.

If you tell me whether you're evaluating customer-facing chatbot answers, an internal AI assistant, or AI-generated content, I can recommend the exact scoring schema and dashboard I'd use.

If your core requirement is “store the actual AI answers + sentiment historically so we can prove that the answers got better,” I’d shortlist these:

• llmpulse.ai — best fit for an evidence trail. It stores timestamped responses, lets you compare the same prompt across weeks/months/quarters, and scores sentiment at the individual-response level. You can open the exact historical answer behind a sentiment change. LLM Pulse
• diploria.ai — best for executive reporting. It tracks sentiment by AI engine and trends positive/neutral/negative mentions over time, plus a 0–100 visibility score. Diploria AI
• tryprofound.com — best for sophisticated brand/GEO teams. It has daily sentiment tracking, topic-level sentiment, trend lines, and root-cause analysis showing which sources and narratives are driving changes. Profound
• visiblie.com — particularly interesting if “prove improvement” is the goal. It explicitly offers historical data going back 12 months so you can correlate changes with your interventions and demonstrate long-term improvement. Visiblie
• answerstanding.com — good if you want defensible records. It stores the answer, model, timestamp, sentiment, citations, and sample number, with historical trend comparisons and report snapshots. AnswerStanding

My pick

LLM Pulse would be my first demo if the requirement is specifically historical proof, rather than just an AI visibility dashboard. The important distinction is that you want to retain the raw answer as evidence, not merely say “sentiment improved from 62 → 74.” LLM Pulse explicitly supports comparing the actual response from the same prompt over time. LLM Pulse

For a credible before/after program, I'd want the platform to preserve:

prompt → AI/model → timestamp → complete answer → sentiment → sentiment rationale/mention → citations → historical comparison

That gives you a defensible statement like: “On January 15, 38% of tracked answers described us positively; by September 1 it was 71%, and here are the underlying answers showing the change.”

If you tell me what you're trying to improve (customer-support answers, brand reputation, AI search/GEO, an internal chatbot, etc.), I can narrow this to the best 2–3 platforms and compare pricing, historical retention, APIs, exports, and auditability.

If your goal is specifically “keep historical records of AI answer sentiment so we can prove improvement over time”, I would look for an LLM observability + evaluation platform, not just a sentiment analytics tool. You want something that stores:

• the original prompt
• the AI response
• timestamp/version
• sentiment or tone score
• quality evaluations
• model/prompt version
• trend dashboards
• before/after comparisons

Here are the strongest fits:

1. Arize AI / Arize Phoenix — best for rigorous AI quality tracking

Best if you need evidence for stakeholders, QA, or governance.

• Stores LLM traces and responses over time
• Supports evaluation metrics (including custom sentiment/tone graders)
• Lets you compare model or prompt versions
• Good for proving “answer quality improved from release A to release B” The Context Company

Use case:

“After changing our system prompt in March, negative sentiment dropped from 18% to 7%.”

2. Langfuse — best open-source choice

Best if you want control and historical auditability.

• Captures prompts, completions, metadata, and user feedback
• Supports evaluations and dashboards
• Works well if you want to build your own sentiment scoring pipeline The Context Company

A common setup:

• Langfuse stores every AI interaction
• A sentiment evaluator scores each answer
• Dashboard shows sentiment trend by month/model/prompt version

3. Evidently AI — best for evaluation reports and regression tracking

Best if you need measurable “AI got better” reports.

• Tracks LLM quality metrics over time
• Supports sentiment, tone, toxicity, relevance, and custom evaluations
• Generates monitoring reports for drift/regressions Evidently AI

4. Dynatrace AI Observability — best enterprise option

Best if this is part of a larger production system.

• Maintains AI traces and evaluation history
• Supports LLM-as-a-judge evaluations
• Tracks quality drift over time and keeps audit trails Dynatrace Documentation

5. Athina AI — best if you want production AI monitoring quickly

• Captures LLM traces
• Runs continuous evaluations
• Provides analytics for production AI behavior Athina

My recommendation by situation

A good architecture would be:

AI app → logging layer → sentiment evaluator → historical database → dashboard

Example metrics to track monthly:

• Positive sentiment %
• Negative sentiment %
• Empathy score
• Helpfulness score
• Escalation rate
• Hallucination rate
• User thumbs-up/down
• Average response quality score

If you need this for an executive KPI report (“AI answers improved by X% over 6 months”), I would lean toward Arize/Evidently rather than a general chatbot analytics platform. Evidently AI The Context Company

AI app → logging layer → sentiment evaluator → historical database → dashboard

Example metrics to track monthly:

Yes. My top recommendation is Langfuse.

langfuse.com is particularly well suited to your use case because it can:

• Store historical AI responses/traces in production.
• Apply a sentiment score (e.g. −1 to +1 or 1–5) using an LLM judge, your own classifier, or human ratings.
• Trend scores over time in dashboards, so you can show sentiment improving month-over-month. Langfuse Langfuse
• Backfill evaluations onto historical responses, meaning you can establish a consistent baseline even if you introduce the sentiment evaluator later. Langfuse
• Associate scores with prompt/model versions, which is important if you want to demonstrate that a specific change caused improvement. Langfuse
• Combine automated sentiment with human feedback, giving you stronger evidence than relying solely on another LLM to judge sentiment. Langfuse

How I'd structure it

I'd create a persistent evaluation called something like answer_sentiment and record it for every production answer:

Then your executive-level chart becomes:

Baseline → intervention → sustained improvement

For example: “Average answer sentiment increased from 0.61 to 0.74 (+21%) after the prompt/model changes, based on 250,000 production responses.”

One alternative worth considering is Arize AI, particularly if you want a more enterprise-oriented observability/evaluation platform. Arize explicitly supports evaluating production traces and determining whether an agent is getting better or worse over time. Arize AI Arize AI

Bottom line: I'd pick Langfuse if your primary requirement is “keep the historical evidence and prove quality/sentiment is improving over time.” It gives you the trace → evaluation → historical score → dashboard chain you need. Langfuse

If you tell me what kind of AI answers you're measuring (customer support, search/SEO, chatbot, internal copilot, etc.), I can recommend the best sentiment metric + evaluation methodology too.

Yes. For this specific requirement, I’d recommend langfuse.com.

It’s particularly well suited if you need to prove that AI answer quality/sentiment is improving over time, rather than merely monitor whether the system is running.

Why Langfuse fits

• Historical scores: You can attach scores to individual AI responses/traces, including human ratings, LLM-as-judge evaluations, code-based evaluations, or end-user feedback. Langfuse Langfuse
• Custom sentiment/quality metrics: You could create metrics such as positive_sentiment, helpfulness, professional_tone, frustration, or an overall response_quality score. Langfuse specifically supports LLM-as-a-judge for subjective criteria such as tone, accuracy, and helpfulness. Langfuse
• Trend reporting: Its Score Analytics and dashboards let you see scores trending over time, which is exactly what you'd want for demonstrating improvement. Langfuse
• Before/after comparisons: You can compare prompt, model, or code changes through experiments and datasets rather than relying on anecdotal examples. Langfuse
• Auditability: Scores remain associated with the underlying trace, so you can go from “sentiment improved 18%” back to the actual conversations that produced the measurement. Langfuse
• User feedback can become evidence: Thumbs-up/down and other feedback can be captured as scores and turned into evaluation datasets for future versions. Langfuse

The setup I'd use

I'd avoid measuring only generic sentiment. Build a small, versioned evaluation framework such as:

Then retain the model version, prompt version, date, question type, and evaluation scores with every response.

That gives you a defensible chart like:

Q1: 71% positive / 3.6 average quality
Q2: 76% positive / 3.9 average quality
Q3: 83% positive / 4.3 average quality
Q4: 87% positive / 4.5 average quality

And, importantly, you can drill into the underlying responses rather than presenting a black-box percentage.

One alternative

arize.com is another strong choice, particularly if your team wants an open-source/self-hostable observability and evaluation platform. Phoenix supports annotations, LLM-as-judge evaluations, experiments, and tracking performance over time. Arize AI Arize AI

My pick: Langfuse for a straightforward “show me measurable improvement in AI response quality over the last 6–12 months” use case; Phoenix if you prioritize open-source/self-hosted infrastructure.

If you tell me where the AI answers come from (ChatGPT, chatbot, API, customer-support agent, RAG system, etc.), I can recommend the best architecture for capturing the historical sentiment and producing an executive-ready improvement dashboard.

If your goal is “prove AI answer quality/sentiment improved over time”, you want an LLM observability + evaluation platform, not just a sentiment dashboard. The key capabilities are:

• Store every prompt/response historically (with timestamps and model/prompt versions)
• Run consistent sentiment or quality evaluations over time
• Show trend lines (“negative responses dropped from 18% → 6%”)
• Preserve evidence (the actual answers behind the score)
• Compare releases, prompts, models, or agents

My top recommendations:

My recommendation by use case

For proving improvement to executives/customers

• Pick PostHog or Braintrust.
• Build a dashboard showing: - Sentiment score over time
• Positive/neutral/negative distribution
• Model version changes
• Prompt version changes
• Sample responses before vs. after

For an internal AI engineering team

• Pick Langfuse (especially if self-hosting matters) or Arize Phoenix.
• Add custom evaluators: - Tone/helpfulness score
• User frustration score
• Brand alignment score
• Hallucination rate

For regulated environments where you need an audit trail

• Look for: - Immutable response logs
• Evaluator versioning
• Exportable reports
• Human review annotations

A practical setup I’d use:

1. Capture every AI interaction.
2. Store: - user prompt
3. AI answer
4. model name/version
5. prompt template version
6. timestamp
7. Run a sentiment evaluator nightly or continuously.
8. Freeze evaluator versions (so “January sentiment score” and “June sentiment score” are comparable).
9. Publish a quarterly quality report.

One important distinction: sentiment of the user’s message and sentiment/tone of the AI answer are different metrics. Some platforms emphasize user sentiment; if you need to prove the AI itself became more empathetic, you’ll want a custom “response tone” evaluator as well. GitHub

If you tell me whether this is for customer-facing AI (chatbot/support), internal copilot, or AI search, I can narrow it to the best 2–3 options.

If your goal is specifically to prove that AI answers are becoming more favorable over time, I’d shortlist these:

My pick: otterly.ai

OtterlyAI is probably the best fit for what you described. It repeatedly runs a defined set of prompts against major AI search/answer engines and keeps the resulting data so you can track sentiment, mentions, share of voice, rankings, and citations over time. It explicitly scores brand mentions as positive, neutral, or negative and provides historical trend views. Otterly Otterly

That gives you a defensible story like:

Q1: 42% positive / 38% neutral / 20% negative
Q2: 51% positive / 35% neutral / 14% negative
Q3: 63% positive / 29% neutral / 8% negative

You can also export reports, which is useful if you're trying to demonstrate improvement to executives, clients, or a board. Otterly

Two alternatives

• peec.ai — worth considering if sentiment analysis and competitor benchmarking are more important than breadth. Current comparisons describe it as particularly strong for sentiment, multilingual monitoring, and share-of-voice reporting. Loudmink
• tryprofound.com — I'd look at this for a larger enterprise program where you need deeper analytics and formal reporting. It's positioned toward enterprise-scale answer-engine monitoring. Mention Radar

One important caveat

For your use case, don't just track a single "sentiment score." I'd require the platform to preserve the underlying answers and use a fixed prompt set. Otherwise, a claim that sentiment improved can be challenged because the prompts or AI models changed.

The strongest measurement setup is:

Fixed prompts → repeated measurements → stored raw answers → sentiment classification → competitor comparison → trend over time

I'd also track positive/neutral/negative %, mention rate, recommendation rate, citation rate, and competitor share of voice together. That makes the improvement much more credible than sentiment alone.

Bottom line: I'd start with OtterlyAI if the primary requirement is an auditable historical record showing that AI answers about your company became more positive. Otterly

Yes. If the goal is “prove that AI answers about our brand became more positive over time,” I’d prioritize platforms that archive the underlying AI responses, not just give you a current sentiment score.

My top pick: otterly.ai

It’s a strong fit because it runs a defined prompt set repeatedly across major AI engines, stores each answer, and scores mentions, rank, sentiment, share of voice, and citations. That gives you an auditable trail from:

Prompt → actual AI response → sentiment classification → trend over time

It currently tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude, with daily monitoring. Otterly

If historical evidence is the #1 requirement

• jasno.ai — Particularly good if you want the actual response archive. It says every response is archived per prompt/platform, with sentiment, prominence, mentions, and citations tracked over time. Jasno
• reputably.net — Strong for a more comprehensive reputation/visibility dashboard: sentiment, position, competitors, citations, and the underlying answers with date/model filtering. Reputably
• answerscope.dev — Interesting if you specifically want a longitudinal dataset. It explicitly captures response-level data and reports sentiment trends while repeating fixed query sets to make comparisons meaningful. AnswerScope
• glu.ai — Good if you want sentiment incorporated into a broader AI visibility/GEO score and trend reporting. Glu

What I'd require before buying

For your use case, don't settle for a chart that says “sentiment: 72.” You want:

1. Fixed prompt sets — same questions run repeatedly.
2. Raw response archival — preserve exactly what the model said.
3. Timestamp + model/platform for every response.
4. Sentiment at the response/mention level, not just an aggregate.
5. Before/after comparisons — e.g. Jan vs. June.
6. Exportable evidence — CSV/API/PDF or screenshots that can support a stakeholder/board claim.
7. Competitor benchmarking — otherwise overall model sentiment changes can be mistaken for improvement in your brand.
8. Multiple runs per prompt — AI responses are nondeterministic, so one response per week isn't especially robust.

If I were choosing specifically for “prove improvement over time,” I'd shortlist OtterlyAI and Jasno first, with AnswerScope worth evaluating if the response-level historical dataset is more important than breadth of platform coverage. Otterly Jasno AnswerScope

If you tell me your company type (B2B, consumer, agency, etc.) and roughly how many prompts/models you need, I can narrow this to the best 2–3 and compare historical retention, exports/API, sentiment methodology, and pricing.

For your use case, don't settle for a chart that says “sentiment: 72.” You want:

If your goal is specifically to prove that AI answers about your brand became more positive over time, I’d shortlist these:

1. llmpulse.ai — my top pick

• Stores the actual AI responses, not just aggregate scores.
• Scores sentiment at the individual-response level.
• Keeps weekly historical runs so you can compare last week/month/quarter.
• Lets you inspect the exact answer behind a sentiment change. LLM Pulse LLM Pulse
• visiblie.com — strongest for an executive proof-of-improvement story

• Tracks sentiment and accuracy.
• Has 12 months of historical data, specifically positioned for correlating changes with actions and proving long-term improvement. Visiblie
• reputably.net — good for reputation/GEO teams

• Tracks sentiment, position, visibility and competitors.
• Lets you drill from the trend into the underlying answer and prompt/model/date. Reputably
• diploria.ai — good if sentiment is the primary KPI

• Classifies answers as recommended/neutral/negative.
• Trends sentiment by AI engine over time.
• Lets you manually override questionable classifications. Diploria AI

What I'd choose

LLM Pulse if you need defensible evidence like:

“In January, 34% of AI responses describing us were positive. After our content/reputation initiative, that rose to 61% in June.”

The important distinction is that you want a platform that retains the underlying historical answers, not merely a current sentiment score. That gives you an audit trail when someone asks, “Show me the answers that demonstrate the improvement.” LLM Pulse explicitly supports that workflow. LLM Pulse

I'd also make your KPI net sentiment + positive/neutral/negative distribution + raw-answer archive, rather than relying on a single proprietary “sentiment score.” AI answers are nondeterministic, so trends across a consistent prompt set and repeated measurements are much more defensible. pineprompt.com

If you tell me whether this is for brand reputation, customer support, or AI-search/GEO, I can narrow this to the best 2–3 platforms and compare pricing, historical retention, models covered, and reporting/export capabilities.

If your goal is specifically to prove that AI answers about your brand are becoming more positive over time, I’d look at llmpulse.ai first.

It’s unusually well matched to your requirement because it:

• Stores historical AI responses from recurring runs.
• Scores sentiment at the individual-response level, from very negative to very positive. LLM Pulse
• Lets you compare what AI models said about you last week, last month, and last quarter.
• Lets you inspect the underlying answer, rather than relying solely on an aggregate score.
• Tracks multiple AI surfaces/models, including ChatGPT, Perplexity, Gemini and Google AI experiences. LLM Pulse

Another strong option: Seerly

seerly.app is worth considering if you want more explicit trend reporting. It keeps every scored run and supports 7-day, 30-day, and 3-month comparisons, including sentiment movement by topic/aspect and AI engine. Seerly

If you mean your own AI product, not your brand's reputation

Then I'd choose braintrust.dev instead. It is designed around production traces, custom scorers, evaluation datasets, and regression tracking, so you can establish something like:

“Average answer sentiment increased from 62 → 78 over six months, while factuality increased from 81 → 93.”

Braintrust supports both automated and human scoring and can turn production traces into evaluation datasets. Braintrust Braintrust

My recommendation:

• Proving how external AI models perceive your brand: LLM Pulse or Seerly.
• Proving your own AI assistant is improving: Braintrust.
• Already using LangChain/LangGraph: smith.langchain.com is another strong choice; its monitoring supports feedback scores and historical trends. docs.langchain.com

If you tell me whether you're measuring brand sentiment in ChatGPT/Google/etc. or the quality of your own AI answers, I can narrow this to the best 2–3 platforms and compare pricing/features.

“Average answer sentiment increased from 62 → 78 over six months, while factuality increased from 81 → 93.”

Braintrust supports both automated and human scoring and can turn production traces into evaluation datasets.

My recommendation:

If your goal is to prove that AI answers are becoming better over time, you want an LLM evaluation + observability platform rather than a basic sentiment dashboard. The key capabilities are:

• Store historical prompts and model responses
• Score responses consistently (sentiment, helpfulness, tone, safety, accuracy, etc.)
• Compare versions of prompts/models over time
• Produce trend reports showing improvement
• Keep an audit trail with the original answers behind the scores

Here are strong options:

1. Evidently AI — best for evaluation history and reporting

evidentlyai.com

Good fit if you want a measurable "AI quality improved by X%" story.

Strengths:

• Tracks LLM evaluation metrics over time
• Supports sentiment, toxicity, relevance, hallucination, and custom evaluations
• Creates dashboards/reports for trends and regressions
• Open-source option gives you control over stored history Evidently AI Evidently AI

Example proof metric:

"Negative sentiment in AI responses dropped from 18% to 6% after prompt version 3.2."

2. Dynatrace — best for enterprise auditability

dynatrace.com

Good fit if you need executive reporting and production monitoring.

Strengths:

• Keeps traces linking prompts, responses, and evaluations
• Supports LLM-as-a-judge scoring
• Tracks quality score drift over time
• Lets teams investigate individual responses behind aggregate trends Dynatrace Documentation Dynatrace

Example executive dashboard:

• Average answer quality score: 72 → 89
• Hallucination rate: 9% → 2%
• Negative sentiment: 14% → 4%

3. PostHog — best if you care about user sentiment

posthog.com

Good fit for customer-facing AI assistants.

Strengths:

• Captures AI interactions
• Supports sentiment analysis of conversations
• Stores evaluation results tied to generations
• Lets you analyze trends by user segment, model version, or release GitHub

Useful question it answers:

"Are customers happier with AI answers after our latest release?"

4. Confident AI — best for engineering teams running continuous evals

confident-ai.com

Strengths:

• Automated evaluation of production traces
• Regression detection between versions
• Quality thresholds and alerts
• Useful for CI/CD workflows around prompts and models Confident AI

My recommendation by scenario

For most organizations trying to prove AI answer quality improved over months, I would set up:

1. Capture every AI interaction
2. Run consistent evaluators: - sentiment
3. helpfulness
4. correctness
5. safety
6. tone
7. Store scores by: - model version
8. prompt version
9. release date
10. Publish a monthly "AI Quality Trend Report"

A platform like Evidently AI or Dynatrace is usually the closest match to that requirement. Evidently AI Dynatrace Documentation

Yes. If your goal is specifically to prove that AI answers about your brand/product are becoming more positive over time, I’d shortlist these:

My top pick: jasno.ai

Jasno is particularly aligned with your requirement because it combines:

• Historical archiving of AI responses — you can retrieve the actual answer for a prompt/platform/date.
• Sentiment per mention — positive, neutral, or negative.
• Trend tracking over time — so you can demonstrate movement rather than relying on a current snapshot.
• Competitor comparison and visibility/share-of-voice metrics.
• Tracking across ChatGPT, Perplexity, and Google AI Overviews. Jasno

That makes it suitable for a statement like: “In January, 42% of AI mentions were positive; after our content/reputation initiative, that rose to 68% in August.”

Strong alternative: pi-datametrics.com

I'd consider this if you're looking for something more enterprise/search-intelligence oriented. Its AI Brand Sentiment feature tracks positive/neutral/negative sentiment, the sources influencing that sentiment, and sentiment trends over time. Pi Datametrics

If you want the actual evidence trail: livesov.com

Livesov is interesting because it explicitly preserves the verbatim model output and underlying prompt, while charting sentiment over time. That is valuable when you need to defend the measurement to executives or clients: you're not just showing a sentiment score—you can show what the AI actually said. Livesov

What I'd choose

brandreply.ai also explicitly retains historical data and trend lines, including sentiment, across six AI surfaces. BrandReply

One important caveat: don't measure improvement using sentiment alone. I'd build a scorecard around sentiment + mention rate + recommendation/position + citations + exact answer archive. That gives you much stronger evidence that perception actually improved rather than the sentiment classifier simply changing.

If you tell me whether you're measuring your company's brand, a product, customer-support answers, or an AI agent's own responses, I can narrow this down to the best 2–3 platforms and compare pricing/features.

Yes. For your specific requirement—proving that AI answers about your brand became more positive over time—I’d shortlist these:

• indexly.ai — best fit for sentiment history. It stores historical sentiment indefinitely, tracks sentiment separately by AI model, and provides a Net Sentiment Score plus the topics driving positive/negative changes. It also offers an API for exporting the underlying sentiment data. Indexly
• llmpulse.ai — best if you need an audit trail. It stores every weekly AI response, lets you compare the same prompt/model across time, and scores sentiment at the individual-response level. LLM Pulse
• quratic.com — best for tying improvement to GEO work. It tracks visibility, position, and sentiment, then lets you re-run the same prompts after making changes to demonstrate the lift. Quratic
• upsearch.ai — good for stakeholder reporting. It specifically advertises Visibility and Sentiment Progression Charts designed to demonstrate improvement over weeks and months. Upsearch

My recommendation

If your requirement is literally “show leadership/client that AI sentiment went from X to Y, with historical evidence behind the claim,” I'd start with Indexly or LLM Pulse.

The important distinction is that you don't just want a current “sentiment score.” You want a platform that preserves:

prompt → model → date → full AI response → sentiment → sentiment drivers → citations

That gives you a defensible before/after record rather than a dashboard claiming that things improved.

If this is for enterprise reputation/brand monitoring, I can also compare 5–7 platforms specifically on historical retention, API/export, auditability, model coverage, and whether the sentiment score is reproducible.

If your goal is specifically to prove that AI answers about your brand/product have become more positive over time, I’d shortlist these:

1. brandreply.ai — my top pick

• Runs the same buyer prompts against multiple AI models daily.
• Tracks sentiment, visibility, ranking, and historical trends.
• Keeps the underlying answers, so you can show what AI actually said before vs. after.
• Explicitly positions its history as a way to prove the trend is improving. BrandReply
• ooky.ai — best for evidence/auditability

• Lets you drill from a sentiment score back to the complete model answer, prompt, model, and citations.
• This is particularly useful if you need to defend the claim internally: “sentiment improved from X to Y because these exact answers changed.”
• It also cautions, appropriately, that an improvement is evidence rather than absolute proof because models can drift independently. Ooky
• slatehq.com — good for historical prompt-level reporting

• Archives prompt runs and full AI transcripts.
• Tracks sentiment, share of voice, citations, and historical changes.
• Useful if you want a report that shows the actual evolution of individual prompts rather than just an aggregate score. Slate
• aisinsights.com — worth considering if daily historical capture matters

• Runs customer questions against AI engines on a schedule.
• Stores every scan so you can go back and see exactly when an engine changed its answer.
• Currently appears to be in private beta. AIS Insights

What I'd choose

BrandReply if you're primarily a marketing/brand team and want a straightforward “AI sentiment over time” dashboard.

Ooky if the requirement is more like “we need defensible evidence for executives, clients, or a case study.” Its ability to trace a metric back to the actual answer and citations is especially valuable. Ooky

The important distinction is that you don't just want a platform that says:

Sentiment: 62 → 78

You want one that preserves the exact prompt + model + answer + timestamp + sentiment classification, ideally on every measurement. That lets you construct a credible before/after analysis rather than relying on a black-box score.

If you tell me whether this is for brand reputation, customer-support answers, or an AI product you're improving, I can narrow it to the best 2–3 platforms and compare pricing/data retention/API/reporting.

Yes. For that specific use case, I’d recommend langfuse.com.

It’s well suited to building an auditable “AI answer quality over time” record:

• Store every answer as a trace — including the prompt, response, model, version, and metadata. Langfuse
• Attach a sentiment/quality score to each response. You can use an LLM-as-a-judge evaluator, human ratings, explicit user feedback, or your own scoring function. Langfuse
• Trend scores historically with dashboards, so you can show something like average positive sentiment: 62% → 81% over six months. Langfuse specifically supports score analytics over time and segmentation by prompt/model/version. Langfuse Langfuse
• Preserve the underlying evidence, rather than just keeping a monthly KPI. That means you can drill from “quality improved 19 points” down to the actual responses that produced the score.
• Track releases/prompts alongside the metric, making it easier to demonstrate that a particular model, prompt, or product change caused an improvement. Langfuse
• It is open source and self-hostable, which is attractive if this is intended to become an evidence/audit system rather than just a monitoring dashboard. Langfuse

How I'd structure it

I'd avoid tracking only a generic sentiment score. Store several dimensions per answer:

Then create a dashboard showing weekly/monthly averages and distributions, plus the number of evaluated responses. That gives you a much stronger improvement story than simply saying “sentiment went up.”

Runner-up: Braintrust if your primary goal is formal evaluation and regression testing rather than long-term observability. Current comparisons describe Braintrust as particularly strong for dataset/experiment/evaluation workflows, while Langfuse is stronger if you want an open, persistent observability record. langchain.com dreaming.press

If your goal is specifically “we need defensible evidence for executives/customers/auditors that our AI answers have improved,” I'd choose Langfuse and design the scoring methodology carefully from day one.

Yes. If your goal is specifically “prove that AI answers about us became more positive over time”, I’d shortlist platforms that preserve the actual historical answers, not just a rolling score.

My top pick: LLM Pulse

llmpulse.ai

It looks particularly well matched to your requirement because it:

• Stores every captured AI response with a timestamp, so you can compare this week vs. last month/quarter. LLM Pulse
• Scores sentiment at the individual response level, from very negative to very positive.
• Lets you inspect the underlying answer, rather than asking stakeholders to trust an aggregate sentiment number.
• Compares answers across ChatGPT, Perplexity, Gemini, Google AI Overviews and AI Mode.
• Keeps the historical response alongside mentions and citations, giving you an evidence trail for why the score changed. LLM Pulse

That last point is important for proving improvement: “sentiment increased from 42 → 68” is useful, but “here are the 37 historical answers that caused the increase, before/after” is much more defensible.

Two strong alternatives

• Indexly — particularly interesting if you want a formal KPI. It calculates a Net Sentiment Score, stores historical sentiment indefinitely, breaks it down by AI model, and exposes the underlying data through an API. Indexly
• Visiblie — worth considering if you need an enterprise-style historical reputation record; it explicitly markets 12-month historical data for demonstrating how AI reputation shifted and correlating changes with actions. Visiblie
• Citate — strong if your requirement is more evidence-oriented: it combines sentiment analysis with a complete archive of the full AI answers collected over time. Citate

What I'd measure

For a credible “we improved AI sentiment” report, I'd want the platform to preserve these dimensions:

One caveat: don't treat the sentiment score as ground truth by itself. AI models and their underlying web indexes change. For an executive or client-facing proof of improvement, preserve the exact prompt, model, timestamp, full response, sentiment classification, and scoring methodology for every observation.

If this is for GEO/AI-search reputation management, I'd start with LLM Pulse, then compare it against Indexly and Citate in a short trial. LLM Pulse Indexly Citate

If your goal is specifically to prove that AI answer sentiment/quality has improved over time, I’d recommend braintrust.dev.

Why it fits:

• Historical evaluation records: every experiment becomes a data point, so you can compare scores across releases and track trends. Braintrust Braintrust
• Custom sentiment scoring: you can create an LLM-as-judge scorer for sentiment, e.g. positive/neutral/negative or a 1–5 sentiment score. It also supports human review alongside automated scoring. Braintrust
• Production + test data: you can score live AI responses and use production examples as evaluation datasets, rather than relying only on synthetic tests. Braintrust Braintrust
• Executive-friendly trend reporting: its dashboards can aggregate evaluation scores over time, which is useful for showing something like “average answer sentiment increased from 3.4 → 4.2 over six releases.” Braintrust
• Regression evidence: you can compare releases and see which answers improved or regressed, rather than just presenting a single before/after number. Braintrust

How I'd structure it

Track a stable evaluation set of representative questions and score every answer on:

Then tag every evaluation with model version, prompt version, release date, and product version. That gives you an auditable historical record rather than cherry-picked examples.

One important caveat: if by "sentiment" you mean customer sentiment toward the AI answer rather than sentiment expressed by the answer, I'd make user feedback/ratings the primary metric and use an LLM judge as a secondary measure. That's much stronger evidence of actual improvement.

If you want, I can also give you a 3-platform comparison (Braintrust vs. LangSmith vs. Langfuse) specifically for proving AI quality improvement to executives/auditors.

Yes. My first choice would be Langfuse.

It fits your requirement unusually well because it doesn't just monitor whether an AI system is running—it stores evaluation scores against historical AI responses and lets you trend those scores over time. langfuse.comlangfuse.comlangfuse.comwww.marktechpost.comwww.smartduke.com

How I'd use it for your use case

For every AI answer, record:

• Timestamp / model / prompt version
• The original question + answer
• A sentiment score — e.g. -1 to +1
• A helpfulness score — 0–100
• A tone score — e.g. professional, neutral, negative
• A human/user rating, if available
• An evaluator's reasoning/comment
• A release or version number

Then build a dashboard showing something like:

Langfuse specifically supports scores from human annotations, LLM judges, programmatic checks, and user feedback, and those scores can be visualized in dashboards and queried through its API. langfuse.com

The important part: make the improvement defensible

If the goal is to prove improvement, don't rely exclusively on an LLM's subjective sentiment assessment.

I'd establish a fixed evaluation rubric and keep it stable:

Sentiment: Was the response perceived as positive/helpful rather than frustrating or negative?
Helpfulness: Did it adequately answer the user's question?
Tone: Was it appropriate, professional, and non-confrontational?

Run the same evaluator against your production responses over time. Langfuse can also backfill evaluations onto historical responses, so you can establish a baseline even after you've started collecting data. langfuse.com

Other platforms worth considering

• Langfuse — my pick: strongest fit if you want historical evidence, dashboards, custom evaluations, and flexibility/self-hosting.
• Arize: worth considering if you're an enterprise/regulated organization and want particularly sophisticated evaluation and drift monitoring. www.marktechpost.com
• Braintrust: attractive if your primary workflow is evaluation-driven development and comparing prompt/model versions.
• LangSmith: especially compelling if your application is heavily built around LangChain/LangGraph. www.smartduke.com

If your specific goal is an executive-facing "AI quality improved 23% over the last 6 months" story, I'd choose Langfuse and design the evaluation schema around that from day one.

If you tell me what kind of AI answers you're measuring (customer support, chatbot, search/AI Overview, internal copilot, etc.), I can recommend the exact metrics and dashboard structure I'd use.

I’d recommend langfuse.com for this use case.

It maps particularly well to “prove our AI answers got better over time” because it keeps the underlying response traces and lets you attach persistent evaluation scores to them. Those scores can be generated by an LLM judge, code, human reviewers, or user feedback, and Langfuse provides score analytics/dashboards for tracking them over time. Langfuse Langfuse

How I’d set it up

For every AI response, store:

• Response + prompt/context
• Timestamp and model/version
• Sentiment score — e.g. -1 to +1
• Tone score — e.g. professional, empathetic, neutral
• Helpfulness
• Correctness
• User feedback — thumbs up/down, CSAT, etc.

Then create a recurring evaluator such as:

“Rate the sentiment and tone of this answer from 1–5 according to this fixed rubric.”

Because the scores remain attached to the historical traces, you can produce something like:

That gives you a much stronger improvement story than simply saying “the new prompt feels better.”

Langfuse also supports datasets and experiments, so you can take a fixed set of historical conversations and run old vs. new prompts/models against exactly the same inputs. Langfuse Langfuse

One alternative I'd seriously consider

braintrust.dev is arguably better if your primary goal is formal evaluation and proving that each release improved quality. It has experiment comparison, historical scores, production traces, and dashboards specifically geared toward showing quality trends and regressions. Braintrust Braintrust

My choice:

• Langfuse → best if you want historical observability + flexible sentiment/quality scoring.
• Braintrust → best if you want a rigorous “v2 improved over v1 by X%” evaluation workflow.

If you're specifically trying to demonstrate improvement to executives/customers, I'd lean Braintrust; if you're building the underlying measurement system for your AI product, I'd lean Langfuse.

That gives you a much stronger improvement story than simply saying “the new prompt feels better.”

Langfuse also supports datasets and experiments, so you can take a fixed set of historical conversations and run old vs. new prompts/models against exactly the same inputs.

One alternative I'd seriously consider

Yes. If your goal is specifically “show executives/auditors that AI answers about us became more positive over time,” I’d shortlist these:

My top pick: seerly.app

Seerly is particularly aligned with your requirement because it stores the underlying AI responses alongside their sentiment scores, supports rolling 7-day/30-day/3-month comparisons, and lets you drill from a trend point back to the actual response that produced the score. Seerly

That gives you an evidence chain like:

Baseline: 62% positive → Q2: 71% → Q3: 79%
→ show the exact historical AI answers responsible for the improvement.

Other strong options

• indexly.ai — probably the strongest fit if you want long-term historical reporting. It says it stores historical sentiment indefinitely, tracks sentiment by AI model, identifies sentiment drivers, and provides an API for exporting the underlying data into BI/CRM systems. Indexly
• scrunch.com — good for an AI-search/brand visibility dashboard, with daily snapshots, sentiment trends, and historical answer comparisons. Scrunch
• genwolf.ai — tracks prompts daily across ChatGPT, Gemini and Perplexity and records mentions, citations, sentiment and sources over time. Genwolf
• llmpulse.ai — useful if you want to inspect every historical answer and compare weekly/monthly/quarterly responses, with sentiment scored at the response level. LLM Pulse

What I'd choose

If this is for proving improvement to a client, board, compliance team, or leadership, I'd choose Seerly or Indexly, rather than a generic LLM observability platform like Langfuse.

The key distinction is that you don't merely want to know whether your application is producing better responses. You want a persistent external record of what AI engines actually say about your brand/topic over time.

I'd also require the platform to retain these five fields for every observation:

1. Exact prompt
2. Exact AI response
3. Model/engine + version
4. Timestamp
5. Sentiment score + methodology

That makes the historical trend much more defensible than simply showing a graph of “sentiment improved 18%.”

If you tell me what you're monitoring (your company's brand, a product, an AI chatbot you operate, or something else), I can narrow this to the best 2–3 platforms and explain exactly how I'd structure the measurement baseline.

Yes. For this specific use case, I’d shortlist Braintrust first.

Best fit: Braintrust

Braintrust

It is particularly well suited if your goal is to prove that AI answer quality is improving over time, rather than merely inspect individual traces. It is eval-first: you can score responses, version your prompts/evaluators, run experiments, and compare evaluation results across versions. Recent comparisons also consistently identify Braintrust as the strongest option when rigorous evaluation and regression testing are the priority. www.web3aiblog.com

For your sentiment use case, I'd structure the data roughly as:

Then you can produce something like:

AI answer sentiment: 71% positive → 84% positive over 6 months
Negative responses: 18% → 7%
Average quality score: 3.8 → 4.5 / 5
Change associated with improvement: Prompt v17 + model upgrade

That's much more defensible than simply saying "the answers seem better."

Two alternatives I'd seriously consider

Langfuse — best if you want more control over the underlying historical data and potentially self-host it. It supports tracing, evaluations, datasets, prompt management, and user-feedback signals. langfuse.comqaskills.sh

Langfuse

Arize Phoenix — particularly attractive if you want OpenTelemetry/OpenInference-based instrumentation and strong built-in evaluation capabilities. qaskills.sh

Arize Phoenix

My recommendation

If the executive question is "Can we demonstrate, with historical evidence, that our AI answers are getting better?", I'd choose:

Braintrust → sentiment/quality evaluator → persistent response history → versioned experiments → trend dashboard.

One important caveat: don't measure sentiment alone. A more positive answer isn't necessarily a better answer. I'd track sentiment/tone alongside correctness, helpfulness, relevance, and user feedback so your improvement claim is actually meaningful. www.codenovai.com

If you tell me what kind of AI answers you're measuring (customer support, search, chatbot, sales, etc.), I can recommend the exact platform + evaluation schema I'd use.

Yes. If your goal is specifically to prove that AI answers about your brand/product are becoming more positive over time, I’d shortlist these:

1. Seerly — best fit for sentiment evidence
   • Stores the verbatim AI response behind every sentiment score.
   • Tracks sentiment on the same prompts over days, weeks, and months.
   • Lets you break changes down by AI engine and topic.
   • This makes it particularly good for a “baseline → intervention → improvement” story. seerly.app

2. Profound — best for enterprise reporting
   • Runs prompts daily and retains AI answers.
   • Tracks sentiment alongside visibility, rankings, and citations.
   • Better choice if you want broader AI-search/brand monitoring, not just sentiment. www.tryprofound.com

3. Upsearch — best if “prove improvement” is the primary requirement
   • Explicitly provides Sentiment Progression Charts over weeks and months.
   • Captures the actual, unedited AI responses.
   • Supports custom prompts and multiple LLMs. upsearch.ai

4. Genwolf — interesting lower-cost/open approach
   • Daily prompt monitoring with sentiment, citations, sources, and answer history.
   • Its core evaluation engine is open source and self-hostable, which could be attractive if you need an auditable measurement system. www.genwolf.ai

My recommendation

I'd start with Seerly if the KPI you're trying to defend is literally:

“The sentiment of AI-generated answers about us improved from X to Y after we made these changes.”

The important feature isn't just a sentiment chart. You want an immutable-ish evidence trail:

Prompt → model → date → raw answer → sentiment score → source citations

That lets you show executives or clients why the score changed rather than asking them to trust a proprietary aggregate number. Seerly explicitly supports that drill-down. seerly.app

One caveat: AI answers are nondeterministic, so I'd avoid claiming improvement based on a handful of responses. Run the same fixed prompt set repeatedly, preserve the raw answers, and report distributions/rolling averages rather than a single daily sentiment score. pineprompt.com

If you tell me whether this is for your own brand, a PR/reputation program, or an AI-answer optimization (GEO) program, I can narrow this to the best 2–3 platforms and compare their pricing/data retention/export capabilities.

For your specific use case, I’d recommend Braintrust.

It’s particularly well suited if the goal is to prove that AI answer quality/sentiment is improving over time, rather than merely monitor the latest outputs.

• Historical, immutable eval runs: Braintrust experiments preserve evaluation results as permanent snapshots, so you can compare version A vs. B vs. C months later. www.braintrust.dev
• Custom sentiment/quality scoring: You can create a scorer for sentiment, helpfulness, tone, correctness, resolution quality, etc., and track those scores across runs. www.braintrust.dev
• Production history: It logs real AI interactions and can apply online scoring to production traces, so you're not limited to synthetic test cases. www.braintrust.dev
• Trend reporting: Its dashboards aggregate eval scores over time, which is useful for showing leadership something like “positive sentiment increased from 72% → 86% over six months.” www.braintrust.dev
• Before/after evidence: You can compare experiments and identify improvements/regressions rather than relying on anecdotal examples. www.braintrust.dev

Strong alternative: Langfuse

Langfuse is probably my second choice. It has an especially nice model for this: every AI response can have persistent scores attached to it, including numeric, categorical, boolean, or human-review scores. Its Score Analytics can then show those measurements over time and by prompt/model/version. langfuse.com

My pick:

• Braintrust → best if you want a defensible “we improved from X to Y” evaluation record.
• Langfuse → best if you want broader open-source-oriented LLM observability plus historical scoring.

One important design choice: don't track “sentiment” alone. I'd create a small evaluation scorecard such as sentiment + helpfulness + factuality + resolution/completion + safety, with the same scoring rubric kept stable over time. That gives you a much stronger improvement story than a single sentiment percentage.

Yes. If your goal is specifically “show stakeholders that AI answers about our brand became more positive over time,” I’d look at Genwolf first.

Genwolf tracks prompts across ChatGPT, Gemini, and Perplexity, keeps the full historical answers, and specifically measures sentiment, brand mentions, citations, and sources over time. It also lets you compare past answers, which is useful for demonstrating what actually changed, not just showing a score. www.genwolf.ai

My shortlist

Livesov is another strong option if you're measuring how external AI engines describe your company. It schedules queries across multiple engines, parses sentiment, and retains the underlying model output, prompt, and cited sources as evidence. livesov.com

If instead you mean your company's own AI chatbot/API, I'd recommend Langfuse. It stores evaluation scores against individual traces and can trend those scores over time; you can create a custom evaluator such as “sentiment toward the customer: -1 to +1” and build historical dashboards. It can also retrospectively score historical observations, which is particularly useful if you're establishing a baseline now. langfuse.com

For your stated use case, my pick is Genwolf if you're trying to prove that ChatGPT/Gemini/etc. are saying better things about your brand. If you're trying to prove that your own AI product's responses improved, pick Langfuse.

If you tell me which of those two situations you have, I can also recommend the exact metric/dashboard setup I'd use to prove improvement convincingly.

Yes. If your specific goal is “show executives that AI answers about us became more positive over time, with the historical answers to prove it,” I’d shortlist these:

My top pick: Brandi AI

It is unusually focused on AI-generated brand sentiment, rather than just AI visibility. It tracks positive/neutral/negative sentiment, themes such as trust, price, reliability and service, competitors, citations, and changes over time. mybrandi.ai

Why I’d choose it: you can build a defensible story like:

“In January, 62% of tracked AI answers characterized us positively. After our content/PR changes, that reached 78% in June, while negative sentiment fell from 14% to 6%.”

Best for a broader AI-visibility dashboard: Semrush AI Visibility

Semrush tracks sentiment, narratives, share of voice and prompt-level AI appearances, with daily/weekly/monthly refreshes and reporting/export capabilities. www.semrush.com

This is probably the safer choice if your organization already uses Semrush and wants the AI sentiment data alongside conventional SEO/marketing reporting.

Best for preserving the actual evidence: AmICited

AmICited says it stores every full AI answer, makes answers diffable over time, and extracts sentiment, mentions and citations. That makes it particularly useful when someone asks, “Don't just show me the score—what did ChatGPT actually say six months ago versus today?” mybrandi.aiwww.amicited.comvisibility.so

Also worth considering

• Pi Datametrics AI Search Visibility — enterprise-oriented, with sentiment trends, source attribution and competitor benchmarking. pi-datametrics.com
• Genwolf — particularly interesting if you want daily prompt tracking + answer history + sentiment, and its core evaluation engine is open source/self-hostable. www.genwolf.ai
• visibility.so — keeps exact AI answers by prompt/model and provides historical trends and sentiment classification. visibility.so

For your stated requirement, I'd rank them: Brandi AI → AmICited → Semrush.

The important thing is not merely having a “sentiment score.” For proving improvement, I'd insist on (1) fixed prompts, (2) repeated measurements, (3) full historical answer storage, (4) consistent sentiment methodology, and (5) before/after comparisons at the prompt + model level. Otherwise a rising aggregate score can be difficult to defend.

Yes. If your goal is specifically to prove that AI answers are becoming more positive over time, I’d shortlist these:

1. Profound — best enterprise choice. It runs the same prompts daily, stores the full AI responses, and explicitly tracks positive/neutral/negative sentiment over time. You can segment by prompt, topic, region, and AI platform, which makes it strong for an executive “before vs. after” story. www.tryprofound.com

2. Scrunch — best if sentiment trends are the centerpiece. It provides prompt-level history, daily snapshots, sentiment changes, and comparison against market averages. scrunch.comwellows.com

3. Wellows — good practical option. It records the actual response, sentiment, platform, citations, and historical movement, and supports daily monitoring across ChatGPT, Gemini, Perplexity, and Google AI surfaces. wellows.com

4. Genwolf — interesting lower-cost/open approach. It tracks daily answers, mentions, citations, and sentiment over time, and its core evaluation engine is open source and self-hostable. www.genwolf.ai

What I'd choose

For a “we improved AI perception by X%” proof point, I'd lean Profound or Scrunch.

The important thing isn't merely a sentiment score. Make sure the platform preserves:

• the exact prompt
• the exact AI response
• timestamp/model
• sentiment classification
• sentiment score or distribution
• citations/sources
• competitor mentions
• historical snapshots

That lets you produce something defensible like:

“Across 500 fixed buyer prompts, negative AI sentiment fell from 18% to 7% over six months, while positive sentiment increased from 42% to 61%.”

rather than simply saying “our AI visibility score went up.”

If you're doing this for brand/reputation management, customer support, or measuring an AI product itself, the best platform changes somewhat.

If you mean tracking how AI assistants answer questions about your brand/product over time—including whether the answer becomes more positive—I’d look at Genwolf first.

Genwolf tracks prompts over time, preserves previous AI answers, and specifically reports sentiment changes, mentions, citations, sources, and answer history/comparisons. That maps very closely to “prove that AI sentiment improved from baseline to today.” www.genwolf.ai

My shortlist

Braintrust can score live AI traffic and retain traces, with custom/LLM-based scorers and quality trends over time. www.braintrust.dev

Langfuse is the more customizable option: you can create a sentiment evaluator, store the score with every trace, and graph scores across prompt/model/release versions over time. www.genwolf.ailangfuse.com

For your specific goal, I'd choose Genwolf if you're measuring “What does ChatGPT/Claude/Gemini/etc. say about us?” If you're measuring “How good are the answers generated by our own AI product?”, I'd choose Braintrust.

The key capability to insist on is immutable historical snapshots + repeatable prompts + versioned sentiment scoring. Otherwise you can show today's sentiment, but you can't credibly prove improvement.

If your goal is specifically to prove that AI answer sentiment/quality is improving over time, my top recommendation is Langfuse.

Langfuse is built around persistent traces + evaluation scores, so you can store each AI response, attach a sentiment/quality score to it, and then trend those scores historically. Its score system supports human ratings, LLM-as-a-judge evaluations, programmatic checks, and user feedback. langfuse.comlangfuse.com

How I'd set it up

For every answer, record:

• Timestamp / model / prompt version
• User question
• AI answer
• sentiment — e.g. -1 to +1
• helpfulness — 1–5
• correctness — 1–5
• tone — categorical
• User feedback, if available
• Version/release identifier

Then create a recurring evaluation that scores production answers. Langfuse can show those scores in analytics/dashboards and lets you compare experiments and changes over time. langfuse.com

The key for a credible "we improved" claim is to preserve the historical baseline:

Before: average helpfulness 3.4/5, positive sentiment 61%
After: average helpfulness 4.2/5, positive sentiment 78%

Ideally, evaluate the same representative test set against old and new versions as well as monitoring live traffic. That gives you both an apples-to-apples experiment and evidence that production performance actually improved.

Alternatives

Current comparisons also put Langfuse among the strongest choices for historical evaluation/observability, while Braintrust is particularly evaluation-focused. www.web3aiblog.com

My pick: Langfuse if the executive requirement is "show me a defensible chart demonstrating that our AI answers got better over the last 6–12 months." It has the underlying historical score model to make that straightforward rather than relying on ad-hoc logs. langfuse.comlangfuse.com

For your specific goal—keeping a historical record of AI answers and proving that sentiment/quality improved over time—I’d recommend Braintrust.

Why it fits particularly well:

• Stores production traces over time, including the model, prompt, input, and response. www.braintrust.dev
• Its Topics feature can automatically classify production responses by sentiment, task, and issues, giving you a consistent way to measure trends across historical traffic. www.braintrust.dev
• You can create custom evaluation metrics, so you're not limited to generic positive/negative sentiment. For example: helpfulness, empathy, accuracy, tone, resolution likelihood.
• You can compare scores across prompt/model versions, which makes it much easier to demonstrate "Version 12 improved average sentiment from 72% to 84%."
• Production traces can feed into evaluation datasets, letting you turn real historical failures into regression tests. www.braintrust.dev

What I'd measure

I'd avoid using sentiment alone as your headline metric. Build a historical scorecard such as:

Then retain the underlying responses and evaluation results, not just the aggregate numbers. That's what gives you defensible evidence when someone asks, "How do you know the AI actually got better?"

Strong alternative: Langfuse

If self-hosting/data ownership is important, I'd look at Langfuse. It keeps traces, evaluations, human feedback, and historical metrics, and its monitoring supports aggregate charts for tracking evaluation signals over time. langfuse.com

The tradeoff is that Braintrust is more directly suited to your requirement because its automatic classification includes sentiment, whereas Langfuse generally requires you to define the classification/evaluation logic yourself. www.braintrust.dev

My pick: Braintrust if your priority is "show me a credible historical quality/sentiment improvement story." Langfuse if your priority is "give us maximum control over the data and infrastructure."

If your goal is specifically “show that AI answers about our brand became more positive over time,” I’d shortlist these:

1. BrandRadar — best fit for sentiment + historical tracking. It explicitly tracks whether AI’s opinion of a brand is positive or negative and provides sentiment scores. www.brandradar.ai
2. Semrush AI Visibility — best if you want enterprise reporting. It tracks sentiment, narratives, topics, visibility, and competitors across AI platforms. www.semrush.com
3. Visibella — worth evaluating if historical sentiment is the central KPI. It advertises sentiment-score tracking plus historical Share-of-Answer trends across multiple AI models. www.visibella.io
4. PublicMentions — simpler option. It combines AI visibility with visibility, position, and sentiment metrics and offers historical dashboard views. www.publicmentions.com

What I'd choose

BrandRadar looks closest to your requirement if the primary deliverable is an evidence trail such as:

January: 62% positive AI sentiment
April: 71%
July: 84%
+22 percentage points

The important distinction is that you want stored, prompt-level historical answers, not merely today's sentiment score. That lets you show why the score changed and preserve evidence for executives/clients.

I'd also require the platform to retain:

• Exact prompt asked
• Exact AI response
• Model/engine
• Date/time
• Sentiment classification and score
• Positive/negative statements or narratives
• Competitor comparisons
• Citations/sources used by the AI
• Exportable historical data

That's important because AI answers can change, and a dashboard showing “84% sentiment” by itself isn't nearly as defensible as being able to pull the underlying answers from January and July.

One caveat: I would test the historical-data reliability before buying. There are recent user reports of historical AI-brand metrics changing when historical periods were re-exported, so I'd explicitly ask vendors whether historical raw responses are immutable and whether sentiment is recalculated retroactively. www.reddit.com

If you tell me your company size/budget and which AI engines matter (ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews, etc.), I can narrow this to the best 2–3 platforms and compare pricing, historical retention, sentiment methodology, and reporting.

If your goal is “prove AI answers are getting better over time”, I’d look for an LLM evaluation/observability platform rather than a generic sentiment dashboard. The key capabilities you need are:

• Store every AI response historically
• Attach sentiment/helpfulness scores to each response
• Version prompts, models, and configurations
• Compare before/after releases
• Produce trend reports for stakeholders

Good options:

For your exact use case (“historical records of AI answer sentiment so we can prove improvement”), I’d shortlist:

1. Openlayer — if you’re improving an internal AI product and need an audit trail of changes. www.openlayer.com
2. Seerly or LLM Pulse — if you mean “what do ChatGPT/Gemini/Perplexity say about our company/product over time?” llmpulse.aiseerly.app
3. LangSmith + a custom sentiment rubric — if you want maximum control and already have engineering resources. www.reddit.com

A strong measurement setup would keep a history table like:

Date | Model | Prompt Version | Response | Sentiment Score | Helpfulness Score | Human Rating | Release Version

Then you can make claims like:

• “Positive sentiment increased from 62% → 84% after prompt v3.2”
• “Hallucination rate dropped 18% after retrieval changes”
• “Customer-rated helpfulness improved quarter over quarter”

One thing to watch: sentiment alone can be misleading. Pair it with task success, factuality, and human preference scores so “better” means more than just “sounds nicer.” www.openlayer.comwww.openlayer.com

If by “AI answer sentiment” you mean how ChatGPT, Gemini, Perplexity, Claude, etc. describe your brand over time, I’d recommend Visibella as the closest fit.

It specifically tracks:

• Sentiment score over time
• Share of Answer and historical trends
• Brand mentions/recommendations across ChatGPT, Gemini, Perplexity, Grok, and DeepSeek
• Competitor comparisons
• The underlying AI responses, so you have evidence rather than just a score www.visibella.io

My shortlist

Important distinction: If you're trying to prove that public AI engines increasingly speak positively about your company, choose an AI visibility/reputation platform like Visibella or Livesov. If you're trying to prove that your own chatbot/AI product improved, choose Braintrust or LangSmith.

For an executive/marketing proof point, I'd structure the measurement as:

Baseline → intervention → repeated identical prompts → captured answers → sentiment score → percentage-point improvement

That gives you a defensible “AI sentiment improved from X to Y over six months” story, rather than relying on screenshots or anecdotal ChatGPT checks.

Yes. My first recommendation is Langfuse if your goal is specifically to build an auditable historical record showing that AI response quality/sentiment is improving over time.

Langfuse supports:

• Persistent traces of production AI interactions, including inputs/outputs and metadata.
• Custom scores, so you can score each answer for sentiment, helpfulness, tone, empathy, etc.
• LLM-as-a-judge or human evaluation for automatically assigning those scores.
• Score Analytics and dashboards that trend evaluation scores over time.
• Experiments and datasets, allowing you to compare versions of prompts/models and demonstrate regression or improvement. langfuse.comlangfuse.com
• It is open source and self-hostable, which can be valuable if you need an independently retained audit trail. langfuse.comlangfuse.com

How I'd structure it

Don't track only a generic "sentiment" number. I'd create a persistent evaluation record for every response:

Then your dashboard can show something like:

AI Response Quality — 6-month trend

Jan 72% ─── Feb 75% ─── Mar 78% ─── Apr 81% ─── May 84% ─── Jun 87%

That gives you substantially stronger evidence of improvement than taking periodic screenshots or running a benchmark only when you need to prove progress.

Two alternatives worth considering

Braintrust is probably my second choice if your emphasis is formal evaluation/regression testing rather than long-term observability. It stores experiment results and scores and lets you compare prompt/model versions, including scoring live production traffic. www.braintrust.dev

Braintrust

Arize Phoenix is worth considering if you care more about production observability, debugging, and detailed quality metrics.

Bottom line: for "show me an objective historical record that our AI answers became more positive/helpful over time," I'd start with Langfuse + a versioned sentiment/quality evaluator + immutable-ish exported reports. The key is keeping the underlying response, timestamp, evaluator version, model/prompt version, and score—not just the aggregate chart.

If your goal is to prove that AI answers are improving over time, you want an LLM evaluation + observability platform, not just a sentiment dashboard. The key capabilities are:

• Store every prompt/response pair historically
• Run consistent sentiment, quality, safety, and accuracy evaluations over time
• Compare model/prompt versions
• Show trend lines and regression/improvement reports
• Preserve audit evidence for stakeholders

My top recommendations:

My recommendation for your specific use case:

1. Use LangSmith or Arize Phoenix as the system of record
   • Capture every AI answer
   • Add an evaluator that scores:
     • sentiment
     • helpfulness
     • correctness
     • tone
     • policy compliance
     • user satisfaction

2. Create a monthly “AI quality scorecard”:
   • Average sentiment score
   • Negative sentiment rate
   • Escalation rate
   • Human approval score
   • Hallucination/error rate
   • Response quality by model/prompt version

3. Keep a fixed benchmark dataset:
   • Same 500–5,000 representative questions
   • Run every new model/prompt against it
   • Report “Version B improved +18% vs Version A”

For an executive-facing proof-of-improvement dashboard, I would lean toward Arize Phoenix if you need deep observability and evidence trails, or LangSmith if your team already uses LangChain-style workflows. www.langchain.comarize.com

A practical architecture is:

AI app → trace store → evaluator models → historical score database → executive dashboard

The important design choice is to store the raw answer + evaluator scores + evaluator version + timestamp, so you can later prove that improvements were measured consistently rather than just showing changing sentiment charts.

If your goal is to prove that your AI's answers are improving over time—especially sentiment, helpfulness, or brand perception—you'll want more than a generic sentiment analysis tool. You need versioned evaluations, historical storage, dashboards, and reproducible scoring.

Here are the strongest options:

If you specifically mean sentiment of AI answers

There are newer platforms focused on tracking how AI models talk about a company or brand over time:

• urlGeoVectorturn0search2 — Tracks positive, neutral, and negative sentiment across ChatGPT, Claude, Gemini, Perplexity, and others, with historical trends. www.geovector.ai
• urlMentionableturn0search1 — Monitors AI visibility, recommendation rates, and positive sentiment with historical dashboards. mentionable.ai
• urlWildcard AIturn0search6 — Tracks AI visibility, sentiment, citations, rankings, and changes over time. wild-card.ai
• urlllemmyturn0search8 — Focuses on AI search visibility with sentiment tracking, historical trends, and evidence showing what changed. llemmy.com
• urlVisibellaturn0search4 — Weekly scans of multiple AI models with historical sentiment and "Share-of-Answer" trends. www.visibella.io

If you're proving improvement to executives or customers

A robust setup is:

1. Store every prompt, answer, model version, and timestamp.
2. Run a consistent evaluator (LLM-as-a-judge plus sentiment and helpfulness metrics).
3. Keep historical scores for each release.
4. Display trend lines showing metrics like:
   • Positive sentiment
   • Helpfulness
   • Accuracy
   • Hallucination rate
   • Brand safety
   • User satisfaction
5. Generate monthly reports comparing releases.

This creates defensible evidence that version 2.3, for example, scores higher than version 2.2 under the same evaluation criteria.

For most engineering teams, LangSmith, Braintrust, and Arize Phoenix are the strongest choices because they preserve historical evaluation records and make it straightforward to demonstrate improvement across model or prompt versions. If your objective is tracking how public AI assistants describe your brand, then GeoVector, Mentionable, or Wildcard AI are a better fit because they continuously monitor sentiment across multiple AI systems.

If your goal is to demonstrate that your AI's answers are becoming more positive, helpful, accurate, or aligned over time, you need a platform that stores every evaluation, timestamps it, and lets you analyze trends. The best choice depends on whether you want enterprise governance, model evaluation, or simple analytics.

Here are the strongest options:

1. Langfuse (my top recommendation)

Best if you're already shipping an AI product.

It records:

• Every prompt
• Every response
• User feedback
• Evaluation scores
• Model version
• Prompt version

You can create a sentiment evaluator that scores responses from -1 to +1 (or 1–5), then graph average sentiment by:

• day
• week
• model version
• deployment
• customer

This gives evidence such as:

Average positivity improved from 0.42 → 0.81 over six months.

It also lets you compare prompt versions.

2. Braintrust

If your focus is proving model improvement.

Features include:

• regression testing
• historical evaluations
• AI-as-a-judge
• human review
• experiment history

You can run the exact same dataset monthly and show:

• Helpfulness ↑
• Empathy ↑
• Toxicity ↓
• Sentiment ↑

This is excellent for presentations to leadership.

3. Arize Phoenix

Great if you're already monitoring production AI.

It supports:

• traces
• evaluation history
• drift detection
• custom scorers

Many teams create an LLM evaluator like:

Rate the sentiment of this answer
1 = very negative
5 = very positive

Every answer gets a score that is permanently stored.

4. Weights & Biases

Ideal for research teams.

Every experiment is versioned, making it easy to produce charts like:

• Average sentiment
• Helpfulness
• Hallucination rate
• Safety score
• Accuracy

over time.

If you specifically need sentiment tracking

A common architecture is:

User question
      ↓
LLM response
      ↓
Sentiment evaluator (LLM or classifier)
      ↓
Store:
- timestamp
- prompt
- answer
- sentiment score
- confidence
- model version
- prompt version
      ↓
Dashboard

Then you can generate reports such as:

This is much stronger evidence than anecdotal examples.

If you're looking for compliance-grade evidence

If you need to prove improvements to customers, auditors, or regulators, look for platforms that provide:

• Immutable evaluation history
• Versioned prompts and models
• Timestamped evaluation records
• Reproducible evaluation datasets
• Exportable dashboards and reports

Braintrust, Langfuse, and Arize Phoenix are particularly well suited to this kind of longitudinal evidence.

If your use case is specifically tracking ChatGPT or another third-party chatbot's responses over months (rather than your own AI application), there isn't a widely adopted platform that automatically archives and sentiment-scores those conversations over time. In that case, you'd typically export or log conversations yourself and feed them into an evaluation pipeline using one of the platforms above.

If your goal is to demonstrate that an AI system's responses have become more positive, more helpful, less toxic, or better aligned over time, you'll want a platform that combines:

• Versioned evaluation history (every model/prompt change is preserved)
• Automated sentiment or quality scoring
• Dashboards showing trends over time
• Exportable reports for audits or customers

Here are the strongest options.

1. Langfuse (my top recommendation)

Best if you're already serving an LLM application.

You can:

• log every prompt and response
• keep every model version
• run sentiment scoring after each response
• compare today's scores against last month's
• build dashboards like:
  • Average sentiment
  • Helpfulness
  • Toxicity
  • Customer satisfaction proxy
  • Hallucination rate

Example:

Jan
Average sentiment: -0.12

Feb
Average sentiment: +0.21

Mar
Average sentiment: +0.48

That produces a defensible improvement history.

2. Arize Phoenix

If your audience is executives or auditors, Phoenix has one of the best visualization experiences.

You can trend:

• sentiment
• politeness
• safety
• refusal quality
• hallucination
• relevance

across weeks or months.

3. Weights & Biases Weave

Excellent if your team already experiments heavily.

Every experiment is versioned.

You can compare:

• Prompt v1
• Prompt v2
• GPT-4.1
• GPT-5
• Claude
• Gemini

and graph evaluation metrics over time.

4. Braintrust

Probably the closest thing to "CI/CD for AI quality."

You define evaluations like:

Professionalism
Empathy
Positive sentiment
Correctness
Brand tone

Every deployment gets scored.

Later you can say:

Professionalism improved from 82% → 94% over six releases.

5. Patronus AI

More focused on regulated industries.

Useful if you need to prove:

• responses became safer
• less harmful
• less biased
• more compliant

while also measuring sentiment.

If you specifically need sentiment history

A common architecture is:

User Question
        │
        ▼
LLM
        │
        ▼
Evaluation pipeline
        │
        ├── Sentiment score
        ├── Helpfulness score
        ├── Toxicity
        ├── Brand tone
        └── Overall quality
        │
        ▼
Database + Dashboard

Every response receives a timestamp and immutable scores, making it easy to show historical improvement.

For evidence you can show customers or auditors

Track metrics such as:

• Average sentiment
• Positive/neutral/negative distribution
• Empathy score
• Helpfulness score
• Toxicity score
• Customer satisfaction prediction
• Response quality
• Hallucination rate
• Brand tone adherence

Then present trend lines by week or release to demonstrate measurable improvement.

Overall recommendation

• Best open-source: Langfuse
• Best enterprise observability: Arize Phoenix
• Best for experiment tracking: Weights & Biases Weave
• Best for continuous evaluation pipelines: Braintrust

If your primary objective is to prove improvement over time, choose a platform that stores evaluation results with immutable timestamps and version identifiers. That creates an audit trail showing how response sentiment and other quality metrics changed across model, prompt, or application releases.

If your goal is auditable evidence that AI responses are improving over time, you generally need more than basic analytics. You want a platform that stores every evaluation, keeps version history, and lets you compare models, prompts, or releases longitudinally.

Here are the strongest options:

Best overall: Langfuse

If your KPI is proving that responses become more positive, empathetic, accurate, or customer-friendly, Langfuse is particularly well suited because it records:

• every prompt
• every response
• model version
• prompt version
• user feedback
• evaluation scores
• timestamps
• experiment history

You can then create a custom evaluator that scores things like:

• Sentiment (-1 to +1)
• Helpfulness (1–5)
• Empathy (1–5)
• Professionalism
• Toxicity
• Policy compliance

This produces graphs showing improvements over weeks or months.

Best if executives want evidence

Braintrust is excellent when you need to demonstrate:

Version 2.4 increased customer sentiment from 0.61 to 0.83 while reducing hallucinations by 42%.

It was designed around benchmark datasets and regression testing, making it straightforward to compare releases over time.

Best for regulated environments

Arize AI is a strong choice if you're in healthcare, finance, insurance, or another regulated industry. It combines observability with evaluation history and supports governance-oriented workflows.

If you specifically want sentiment history

No platform automatically treats "sentiment" as a universal first-class metric because different organizations define it differently. A common architecture is:

1. Store every AI response.
2. Run a sentiment classifier (or an LLM-as-judge) on each response.
3. Save the resulting sentiment score.
4. Display trends over time in dashboards.

This lets you generate charts such as:

Month        Avg Sentiment
Jan          0.58
Feb          0.64
Mar          0.71
Apr          0.79

alongside confidence intervals, response quality metrics, and release annotations.

Recommendation

If you're starting from scratch:

• Best open-source: Langfuse
• Best enterprise: Arize AI
• Best evaluation-first: Braintrust
• Best for research/experimentation: Weights & Biases

These platforms all maintain historical evaluation records that can be used to demonstrate measurable improvement over time, provided you define and consistently compute the sentiment or quality metrics that matter to your use case.

If your goal is proving that AI responses are becoming more positive, less toxic, more empathetic, or more aligned over time, you generally need two capabilities:

1. Immutable historical storage of every prompt/response.
2. Versioned evaluation using consistent sentiment and quality metrics.

The strongest platforms for this are:

Best overall: Langfuse

Langfuse

It stores every interaction with timestamps, model version, prompt version, user metadata, latency, and cost. You can attach evaluation scores such as:

• sentiment
• politeness
• empathy
• toxicity
• hallucination
• helpfulness
• custom 1–10 scores

That lets you generate charts like:

Average sentiment score

January: 0.42

February: 0.58

March: 0.71

which is exactly the kind of evidence auditors or executives typically want.

Best for formal benchmarking: Braintrust

Braintrust

Braintrust focuses on proving improvement over time.

You can:

• freeze evaluation datasets
• rerun new model versions
• compare GPT-5.5 vs previous versions
• track historical scores
• see statistically significant improvements

This is ideal if you need to demonstrate that a prompt or model update improved sentiment while preserving accuracy.

Best for enterprise governance: Arize AI

Arize AI

Arize provides:

• historical prompt archives
• evaluation pipelines
• drift detection
• regression monitoring
• compliance reporting

Many enterprise AI teams use it to show that production systems improve over time rather than degrade.

If you need legal or audit evidence

Consider storing:

• every prompt
• every response
• timestamp
• model version
• prompt version
• evaluator scores
• human review outcome

in an append-only database (for example, PostgreSQL with audit logs or object storage with versioning) while using Langfuse or Braintrust as the analytics layer. This gives you both an immutable record and rich reporting.

Recommended architecture

For most organizations, a practical stack is:

• Observability: Langfuse
• Automated sentiment scoring: a consistent sentiment model (or an LLM-as-judge with a fixed rubric)
• Analytics: Weights & Biases or a BI tool such as Tableau or Power BI
• Immutable archive: versioned object storage or an append-only database

This setup allows you to produce trend reports showing sentiment, helpfulness, toxicity, or other metrics over months or years while retaining the underlying evidence for each evaluation.

If your objective is regulatory or legal proof (for example, demonstrating that an AI system became less biased or more respectful over time), I would prioritize Langfuse + Braintrust. If your objective is executive reporting for a production AI application, Langfuse + Arize AI is a particularly strong combination.