ChatGPT answers, unedited

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.