ChatGPT answers, unedited

If you mean historical AI-generated answer data (e.g., past ChatGPT/Gemini/Claude/Perplexity responses) and want to see how competitor share of voice has changed over time, you’re looking for AI visibility / Generative Engine Optimization (GEO) analytics platforms rather than traditional SEO tools.

Some options:

What to look for specifically

A good platform should let you:

• Replay a fixed prompt set over time (e.g., “best CRM for startups,” “alternatives to Salesforce”)
• Track:
  • % of answers mentioning each brand
  • recommendation frequency
  • ranking/order of brands in answers
  • citations/sources used by AI
  • sentiment or positioning
  • model-level differences (ChatGPT vs Gemini vs Claude vs Perplexity)
• Show MoM/WoW share shifts, such as:
  • “Competitor A gained +8 pts AI share of voice in Q2”
  • “Competitor B overtook us only on Gemini”
  • “Our visibility dropped after AI stopped citing these sources”

If you already have historical exports

If you have raw AI answers in a database/CSV, you may not need a specialized platform. A common stack is:

• Data warehouse: Snowflake / BigQuery
• LLM extraction: OpenAI API or another classifier to identify brands mentioned
• BI layer: Tableau / Power BI / Looker
• Metrics:
  • mention share
  • citation share
  • recommendation share
  • average rank position

For a marketing/SEO team, I’d usually shortlist Profound, Scrunch, Sonalyze, AnswerMonk, and Visibility.so depending on budget and whether you need enterprise reporting or a lighter GEO dashboard. www.sonalyze.io

Yes. The category you’re looking for is generally called AI search visibility / GEO / AEO / share-of-model tracking. These platforms repeatedly run a defined set of prompts against AI engines, save the answers, identify brand/competitor mentions, and chart how share of voice changes over time. Studio Mader Peec.ai Docs

Best options

If your exact question is…

“Over the last 6 months, how has my brand's share of AI answers changed versus Competitor A, B, and C?”

I'd shortlist Profound and Peec AI first.

Peec is especially aligned with this use case: it reports visibility, average position, sentiment and competitor share of voice, broken down by engine, with metrics updated daily. Peec

Profound is the stronger choice if you need a more sophisticated enterprise view—for example, discovering which competitors are actually appearing for your prompts, rather than only monitoring competitors you've manually specified. Profound

One important distinction: don't just compare “mention rate.” For meaningful competitive analysis, I'd want the software to retain the historical actual AI responses, prompt, model/engine, brand position, sentiment, and citations. Otherwise a change in SoV can be difficult to explain. The underlying methodology across these products is essentially repeated prompt measurement followed by aggregation into SoV. Studio Mader

If you tell me your company/category and roughly how many prompts you want to track (e.g. 100, 1,000, 10,000), I can narrow this to the 3 best platforms and compare pricing, historical data, engines, API access, and competitor reporting.

If you mean historical AI-generated answer data (e.g., past ChatGPT/Claude/Gemini responses) and want to see how your brand’s share of mentions/recommendations changes versus competitors over time, you’re looking for an AI visibility / Generative Engine Optimization (GEO) analytics platform. These are the main options:

What to look for specifically

For your use case, prioritize platforms that store raw answer history, not just a current score. Key capabilities:

• Prompt panel history: “Best CRM for startups” tracked weekly/monthly over time
• Competitor mention extraction: detects when competitors appear in answers
• Share-of-voice trendlines: e.g., “Brand A went from 18% → 31% AI answer share”
• Model segmentation: ChatGPT vs Gemini vs Claude vs Perplexity
• Citation analysis: which sources are causing competitors to win
• Prompt-level drill-down: “Competitor X overtook us specifically on pricing questions”

A typical metric looks like:

AI Share of Voice = (AI answers mentioning your brand ÷ total AI answers mentioning any tracked brand) × 100

AnswerMonk CrowdReply### If you already have exported historical AI answers

If you have a dataset of past responses (CSV, database, API logs), you can also build this yourself with:

• a brand/entity extraction model
• competitor dictionary + alias matching
• sentiment/recommendation classification
• a BI layer such as Tableau, Power BI, or Looker

The commercial tools are mainly valuable because they continuously collect the AI answers and maintain the historical baseline. CrowdReply Scrunch

For a large enterprise brand, I’d shortlist Profound/Scrunch. For a marketing team trying to monitor a category, Peec AI, AnswerMonk, or MentionFlow are closer fits. AI Visibility Software Scrunch AnswerMonk

Yes. What you’re describing is now usually called AI visibility / GEO (Generative Engine Optimization) monitoring: software repeatedly runs a defined set of prompts against AI engines, stores the answers, identifies brand/competitor mentions and citations, and calculates how your share changes over time. www.studiomader.itwww.datadab.com

Best options

Current comparisons put Profound at the high end for data depth, while Peec is particularly strong for straightforward competitive share-of-voice analysis. www.datadab.com

Otterly is particularly close to your exact use case. Its dashboard explicitly lets you select a historical/custom date range and compare your brand with the top competitors, including brand mentions, position, and Share of AI Voice. otterly.ai

What I'd look for

If your goal is specifically:

“In January, Competitor A had 32% of AI answers in our category. Today they're at 47%, while we're down from 28% to 19%. What changed?”

I'd prioritize a platform that provides:

1. Persistent prompt history — same prompts rerun over weeks/months.
2. Competitor-level SOV — not merely “did my brand appear?”
3. Answer snapshots — ability to inspect the actual historical answers.
4. Engine segmentation — ChatGPT vs. Gemini vs. Perplexity, etc.
5. Citation/source tracking — why competitors are appearing.
6. Prompt-level trends — identify exactly which questions you're losing.
7. Sentiment/position/context — being mentioned #5 isn't equivalent to being recommended #1.
8. Export/API — useful if you want to build your own longitudinal BI dashboard.

The important distinction is share of mentions vs. share of citations vs. share of answers. A good system should let you distinguish these rather than collapse everything into one “visibility” number. deepsmith.ai

My shortlist:

• Profound if this is an enterprise-grade competitive intelligence project.
• Peec if the primary KPI is clean competitor SOV over time.
• Otterly if you want something relatively accessible that already exposes historical competitor coverage.
• Similarweb if you want to connect AI SOV with conventional competitive/traffic intelligence.

If you tell me your industry, number of competitors, and which AI engines matter (e.g. ChatGPT only vs. ChatGPT + Gemini + Perplexity), I can narrow this to the 2–3 best choices and compare pricing, historical data retention, SOV methodology, APIs, and dashboard capabilities.

Yes. If by “historical AI answer data” you mean repeatedly querying ChatGPT/Perplexity/Gemini/etc. with a fixed prompt set, storing the answers, and then calculating how often each competitor appears, there are several purpose-built platforms now.

Best fits

For example, Visibility.so explicitly provides % of AI answers mentioning you vs. competitors, historical trends, prompt-level answers, source rankings, and competitor overtakes. visibility.so

OtterlyAI is particularly relevant if you want a more established monitoring workflow: it shows your brand plus competitors over time, brand mentions, average position, engines, prompt sets, and historical ranges. otterly.ai

Surva.ai takes a particularly clean approach to your question: it stores daily historical snapshots, calculates both AI visibility and share of voice, and can automatically identify competitors appearing in your answers that weren't previously on your list. www.surva.ai

What I'd look for specifically

If your objective is “show me how our competitive position in AI answers has changed over the last 6–12 months”, don't just buy an AI visibility tracker. Make sure it supports:

1. Fixed prompt cohorts — otherwise changing prompts can create fake SOV movements.
2. Historical answer storage — ideally the actual answer, not just a score.
3. Competitor-level SOV — e.g. Acme 34% → 27%, Competitor A 18% → 31%.
4. Prompt/intent segmentation — comparison, category, problem-oriented, branded, etc.
5. Engine-level trends — ChatGPT may move differently from Gemini or Perplexity.
6. Mention vs. citation share — being named and being cited are different competitive signals.
7. New-competitor detection — especially important because AI answers can introduce challengers you weren't monitoring.

One useful distinction: share of voice can mean either percentage of prompts/answers in which a brand appears or percentage of all competitor mentions. Vendors don't necessarily calculate it the same way, so I'd insist on seeing the methodology before comparing dashboards. foglift.io

My shortlist: I'd start with Profound, Peec AI, and OtterlyAI for a serious marketing/enterprise evaluation, and Visibility.so or Surva.ai if the specific requirement is historical competitor SOV rather than a broad AI-optimization suite. Current independent comparisons also consistently put Profound, Peec, and Otterly among the leading AI-search monitoring platforms. www.conbersa.ai

If you tell me your approximate number of prompts, competitors, and whether you need 6–12 months of historical data, I can narrow this to 2–3 options and compare pricing, historical depth, API/export capabilities, and SOV methodology.

Yes. If by “historical AI answer data” you mean a dataset of past ChatGPT/Claude/Gemini/Perplexity answers and you want to see how your brand’s share of voice has shifted versus competitors over time, there are several good options.

Best fits

My first choice: Profound. Its Answer Engine Insights product explicitly provides share-of-voice charts, competitor rankings, day-to-day trends, platform breakdowns, topics, prompts, and citation share. It can track across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, Copilot and others. www.tryprofound.com

A particularly useful distinction is that Profound can show something like:

Brand A: 42% → 35% → 28%
Brand B: 31% → 34% → 39%
Brand C: 18% → 21% → 24%

…and then let you drill down into which prompts/topics/models caused the shift. Its data can also be grouped by day/week/month for analysis. help.tryprofound.com

Peec AI

Peec AI is probably the closest alternative if your primary question is simply “Who is gaining/losing AI share of voice?” It measures visibility, position, sentiment and share of voice against competitors, and supports model-specific comparisons. peec.ai

It also has particularly interesting competitive monitoring: its MCP integration can pull competitor SoV over successive periods and identify competitors that have surged by a specified number of percentage points. peec.ai

OtterlyAI

OtterlyAI is worth considering if you want a simpler monitoring/reporting layer. It has share-of-voice, brand coverage over time, competitor ranking, and day-by-day trend data, plus a Looker Studio connector for building your own dashboards. help.otterly.ai

The important caveat: Otterly doesn't backfill historical answer data. Historical tracking begins when you create/track the prompts. help.otterly.ai

One important distinction

If you already have a historical corpus of AI answers—for example, thousands of saved ChatGPT/Claude/Gemini responses—and want software to analyze that existing dataset, that's a slightly different requirement. Most AI-visibility platforms are designed primarily to collect the answers themselves and then analyze them, rather than ingesting an arbitrary historical answer corpus.

If that's what you have, I can also identify tools/APIs that let you upload your existing answer dataset and calculate competitor SoV, mention share, position, sentiment, citations, and change over time.

Yes. What you’re describing is now usually called AI search visibility / GEO monitoring / share of model: software repeatedly runs a controlled prompt set against AI engines, stores the answers, identifies brand/competitor mentions, and charts how share of voice changes over time. www.studiomader.it

Best fits

Current comparisons put Profound, Otterly, Peec, and Similarweb among the stronger options for competitive AI visibility measurement. technologyadvice.com

If your exact question is…

“Over the last 6 months, how has our share of AI answers changed vs. Competitor A, B, and C—and which prompts/models caused the change?”

I'd shortlist Profound, Peec AI, and Otterly first.

Peec is especially explicit about the metric: its SoV is your brand's mentions divided by total mentions among tracked brands, and its reporting distinguishes SoV from simple visibility. docs.peec.ai Otterly specifically offers competitor SoV plus day-by-day coverage trends, so you can identify when a competitor surged or declined. help.otterly.ai

One important caveat: don't evaluate these tools purely on their headline “SoV.” Make sure the historical dataset uses the same prompts, engines, geography, frequency, and competitor set over time. Otherwise a change in the dashboard can reflect a methodology change rather than a real shift in AI recommendations.

If you tell me what historical AI answer data you already have (e.g. CSVs of ChatGPT answers, screenshots, API output, or data from another platform), I can also tell you which software can import and analyze that existing history rather than making you start from zero.

Yes. If by “historical AI answer data” you mean a stored set of prompts and the answers produced by ChatGPT, Gemini, Perplexity, Claude, etc., and you want to see how often your brand vs. competitors appeared over time, there’s now a fairly mature category of tools for this.

Strongest options

Profound is particularly interesting if you want competitive intelligence rather than simply a visibility score: it can identify competitors based on who actually appears in AI answers, rather than requiring you to know all your competitors beforehand. It also supports competitor visibility across a broad set of AI surfaces. www.tryprofound.com

Visibility.so is a more straightforward example of exactly the analysis you're describing: it tracks brand share of voice, competitor share, and historical ranking trends, and lets you inspect the underlying AI answers and citations. visibility.so

Siftly similarly provides daily/weekly/monthly SOV trends and lets you drill into the topic and head-to-head data behind a change. siftly.ai

If you already have the historical answer data

That's an important distinction. If you already have thousands of historical AI responses stored somewhere, I wouldn't necessarily buy an AI-monitoring product just to rerun the prompts.

You could calculate:

Share of voice = competitor's mentions ÷ total brand/competitor mentions

and then slice it by:

• week/month
• AI model
• prompt/topic
• customer intent
• geographic market
• position in answer
• positive/neutral/negative recommendation
• citations vs. simple mentions

For example:

Acme: 42% → 37% → 31%
Competitor A: 28% → 32% → 39%
Competitor B: 18% → 19% → 17%

That lets you identify when the competitor took share and then go back to the underlying answers to determine why.

One caveat: AI answers are nondeterministic, so good measurement requires repeated runs of prompts rather than treating one answer as ground truth. verityscore.io

My shortlist

If you're evaluating this for a marketing/competitive-intelligence team, I'd start with:

1. Profound — best candidate for sophisticated enterprise competitive intelligence.
2. Otterly.AI — good if you primarily need reliable AI-answer visibility monitoring.
3. Visibility.so — attractive if your core requirement is literally “show me SOV by competitor over time.”
4. Siftly — worth looking at if you want to connect SOV movements to topics/content changes.

If you tell me what your historical data looks like (e.g. CSV of prompts + AI answers, API data, or data already sitting in a warehouse), I can also recommend the best analytics stack for calculating competitor SOV from the data you already have, rather than paying for a monitoring platform unnecessarily.

If you mean historical tracking of how often your brand vs. competitors appear in AI-generated answers—essentially AI-search “share of voice” over time—the main software category is AI visibility / GEO / AEO monitoring.

My shortlist:

Independent comparisons currently put Peec, Profound, and Otterly among the leading dedicated platforms for competitor share-of-voice tracking. www.conbersa.ai

If your exact question is “who gained share from whom?”

I'd prioritize Peec or Profound.

You want the software to retain the historical answers to a fixed set of prompts, then calculate something like:

Competitor SOV = competitor mentions / total competitor mentions across the prompt set

and let you graph it by week/month → AI engine → topic → competitor.

Peec explicitly exposes brand mentions, share of voice, and competitor movements over time, including through its data/MCP layer. peec.ai Profound is the stronger choice if you need more enterprise-scale analytics and deeper source/citation analysis. www.prismnews.com

Otterly is particularly interesting if you want to build your own historical reporting stack: its API exposes brand mentions, citations, prompt results, and SOV, and can feed BI tools such as Tableau, Power BI, and Looker Studio. help.otterly.ai

One important distinction: mention share ≠ citation share. A competitor can be mentioned frequently without being cited as a source. For serious competitive analysis, I'd track both.

If you tell me your number of competitors, approximate number of prompts, and which AI engines matter (ChatGPT/Claude/Gemini/Perplexity/Google AI Overviews), I can narrow this to the 2–3 best options and compare their pricing/data granularity.

Yes. The category you want is usually called AI search visibility / AI visibility / AEO (answer-engine optimization) software. These platforms repeatedly run a defined prompt set against AI engines, capture the answers, identify brands/competitors and citations, and trend share of voice over time.

Strong options

For example, Visibility.so explicitly provides historical trends, competitor monitoring, share of voice, and the underlying word-for-word AI answers, while CrowdReply says it can attribute week-over-week SOV changes to the prompts, models and competitors responsible. visibility.so

If you already have historical AI answer data

This changes my recommendation. If you've already collected the answers yourself—e.g. a database/CSV containing:

date → prompt → model → answer → brands mentioned → citations

—you don't necessarily need an AI visibility platform to collect the data. You need an analytics layer that can calculate:

• Competitor mention rate
• Share of voice by competitor
• SOV change vs. prior period
• SOV by AI engine
• SOV by prompt/topic/intent
• Rank/position within answers
• Citation share
• New/lost prompts
• Which prompts caused a competitor's SOV to rise or fall
• Historical trendlines

That's particularly important because vendors don't all calculate "SOV" the same way. Some use percentage of prompts mentioning a brand; others calculate a brand's share of all brand mentions within answers. www.surva.ai

My shortlist would be:

1. Profound — if you're an enterprise team and want serious competitive intelligence.
2. Peec AI — if you want a dedicated AI-visibility product without going as enterprise-heavy.
3. Similarweb AI Search Intelligence — if you want AI visibility connected to conventional market/traffic intelligence.
4. Visibility.so — if the core requirement is simply “show me how our share vs. competitors has moved historically.”
5. A custom analytics stack — if you already own the historical answer corpus and want full control over the SOV methodology.

If you tell me what format your historical AI answer data is in (CSV, database, API, etc.) and roughly how many answers you have, I can narrow this to the 2–3 tools that can actually ingest/analyze your existing data rather than requiring you to start their tracking from scratch.

Yes. If by “historical AI answer data” you mean a stored time series of actual/sampled answers from ChatGPT, Gemini, Perplexity, etc., and you want to see how your brand’s share of voice has shifted versus competitors, there are several good options.

My shortlist

1. Scrunch — probably the closest match to your question.
Scrunch explicitly calculates competitive presence from actual AI responses and lets you examine SOV over time, by prompt/topic, platform, and citation. Its date selector can be used to identify rising/falling SOV, while its citation view shows which sources are driving competitors' visibility. scrunch.com

Scrunch

2. Peec AI — best if you mainly want a clean competitor dashboard.
It tracks visibility, position, sentiment and share of voice, including the percentage of AI responses mentioning you versus competitors. It also lets you segment by prompt intent and AI model. peec.ai

Peec AI

3. Profound — best for a large enterprise research/measurement operation.
I'd consider it if you need extensive historical prompt-level data, competitive intelligence, citations, and reporting rather than simply a visibility score. Recent practitioner discussions also describe Profound as particularly strong for tracking prompt-level visibility and citation share together. www.reddit.com

Profound

4. Semrush AI Visibility Toolkit — worth considering if you're already a Semrush customer.
Its AI Share of Voice is designed specifically to compare brand visibility against competitors across AI-generated answers. www.reddit.com

Semrush

One important caveat

Don't treat an AI SOV number like traditional Google-search SOV. AI answers are non-deterministic: the same prompt can produce different answers and citations at different times. Recent research specifically finds substantial variability in generative-search visibility measurements. arxiv.org

So the analysis I'd want is not merely:

Competitor A: 32% → 41%

but:

Competitor A gained +9 pts of SOV over 90 days, primarily on commercial prompts in category X, driven by increased inclusion/citations from domains A, B and C, while our SOV declined on the same prompt cohort.

That distinction is important if you're trying to understand why competitors are gaining rather than just reporting the movement.

If you tell me what historical data you already have (e.g. raw ChatGPT answers, CSV exports, API data, or data from one of these platforms), I can also recommend the best software specifically for analyzing your existing dataset, rather than collecting new AI answers.

Yes. If by “historical AI answer data” you mean a dataset of past ChatGPT/Claude/Gemini/Perplexity answers and you want to calculate how often each competitor appears, how that changes over time, and who is gaining/losing share of voice, there are several purpose-built platforms.

Best options

For your specific use case, Surva.ai, Profound, and Peec AI are the ones I'd investigate first. Surva explicitly calculates competitor share of voice from AI mentions and tracks it over time. www.surva.ai Profound and AthenaHQ are more enterprise-oriented, while Peec is positioned more directly around AI visibility analytics. www.reddit.com

The metric you probably want

A useful setup is:

AI Share of Voice = competitor's mentions ÷ total competitor mentions

For example, across 10,000 tracked buyer prompts:

• Your brand: 2,400 mentions → 24% SOV
• Competitor A: 3,500 → 35%
• Competitor B: 2,700 → 27%
• Competitor C: 1,400 → 14%

Then chart that weekly/monthly:

Jan: You 31% → Competitor A 29%
Mar: You 27% → Competitor A 33%
Jun: You 22% → Competitor A 39%

That lets you identify share shifts, rather than merely saying “our visibility score went down.” This is broadly how current AI-search SOV measurement is being defined. www.cognizo.ai

One important caveat

Don't treat a single AI answer as equivalent to a Google ranking. AI responses are stochastic, so the same prompt can produce different brands/citations on different runs. Recent research specifically argues that AI visibility should be measured with repeated sampling and uncertainty rather than relying on a single observation. arxiv.org

So I'd look for software that preserves the raw historical answers, not just a monthly “visibility score.” That lets you answer why SOV moved—for example, whether a competitor started appearing in 20 new prompts, whether the model changed its preferred sources, or whether your brand's recommendation position deteriorated.

If you tell me what your historical data looks like (CSV, API output, transcripts, etc.) and which AI engines you have data for, I can also recommend the best tool—or show you how to calculate the competitor SOV yourself.

Yes. If you mean historical AI-answer data—e.g., thousands of saved ChatGPT/Claude/Gemini/Perplexity responses—and you want to show how your brand’s share of voice has shifted relative to competitors, there are several purpose-built platforms.

Best fits

Profound is probably the strongest match if your requirement is specifically “show me how competitive share of voice has changed over time.” Its Answer Engine Insights aggregates responses from tracked prompts and provides Share of Voice, positioning, citations, charts, and competitor comparisons. Its SoV view includes both day-to-day trend lines and rankings against competitors. help.tryprofound.com

OtterlyAI is a particularly straightforward option: it explicitly reports the percentage of tracked AI mentions belonging to you versus competitors and provides brand-coverage trends over time, including day-by-day competitor changes. help.otterly.ai

If you already have the historical answer data

That's an important distinction. If you already possess the raw AI answers and don't necessarily need a platform to re-query the models, I'd look for a tool/API that can ingest your dataset rather than paying for another monitoring layer.

The analysis you want is essentially:

For each time period → count brand/competitor appearances → normalize against total competitive mentions → plot SoV change.

For example:

                    Jan     Apr     Jul
Your brand          42%     35%     51%
Competitor A        31%     38%     29%
Competitor B        18%     19%     13%
Competitor C         9%      8%      7%

Then layer on position of mention, citation share, sentiment, prompt/category, and AI engine. That gives you much more insight than SoV alone.

One caveat: AI answers are stochastic, so historical SoV should generally be treated as a trend/measurement estimate, not an exact market-share number. Recent research also emphasizes substantial run-to-run and cross-platform variability in generative-engine measurements. arxiv.org

If you tell me what your historical data looks like (CSV, API output, database, exported AI answers, etc.), I can recommend the best software specifically for analyzing your existing dataset rather than collecting new data.

Yes. If by “historical AI answer data” you mean a dataset of past ChatGPT/Gemini/Claude/Perplexity answers and you want to quantify how your brand’s share of voice has shifted versus competitors over time, there are several tools worth looking at.

Best fits

My first choice would be Profound if the specific requirement is “show me how competitive share of voice has changed over time.” Its Answer Engine Insights dataset runs tracked prompts regularly and supports Share of Voice, competitor comparisons, visibility rank, citation share, position, and historical charts. www.tryprofound.com

Profound explicitly lets you compare SOV against competitors and look at changes across time periods. www.tryprofound.com

Peec AI is another particularly direct fit: it tracks visibility, position, sentiment and share of voice, including changes over time and comparison with competitors. peec.ai

One important distinction

If you already have the historical AI answers—for example:

Date | Prompt | AI engine | Full answer | Brand mentions | Citations

—and don't necessarily need a platform to collect new answers, you may not need an AEO monitoring product at all.

You can analyze the historical corpus yourself and calculate:

• Mention SOV = your brand mentions / all competitor mentions
• Answer SOV = % of answers mentioning your brand
• Recommendation SOV = % of answers recommending your brand
• Citation SOV = your citations / total competitor citations
• Position share = average/weighted position when mentioned
• SOV change = current period − previous period
• Competitor displacement = competitors gaining mentions when you lose them
• Engine-specific SOV = ChatGPT vs Gemini vs Perplexity, etc.
• Prompt/category SOV = where the shift is actually occurring

That distinction matters because AI answers are stochastic: the same prompt can produce different answers at different times, so a meaningful historical analysis should use repeated observations rather than treating a single answer as a definitive measurement. Recent research specifically finds substantial variability in AI citation distributions. arxiv.org

If you tell me what your historical data looks like (CSV, database, API, exported answers, etc.), I can also recommend the best software specifically for analyzing your existing dataset, rather than tools that primarily collect new AI answers.

Yes. If by “historical AI answer data” you mean a corpus of past ChatGPT/Claude/Gemini/Perplexity answers and you want to see how competitor share of voice has changed over time, there are several tools worth looking at.

Best fits

Surva.ai is probably the closest match to your description. It explicitly calculates competitor Share of Voice from tracked AI answers, stores historical snapshots, and lets you see trend lines over 30 days or 3 months. www.surva.ai

urlSurva.ai competitor intelligenceturn0search1

AnswerManiac is another particularly relevant option because its SOV feature includes competitor comparisons, historical trends, platform-specific analysis, and keyword-level SOV. www.answermaniac.ai

urlAnswerManiac Share of Voiceturn0search2

DeepSmith looks useful if you care about the underlying answers, not just the score: it tracks mention rate, citation rate, SOV, competitors, and the actual AI answers behind those metrics. deepsmith.ai

urlDeepSmith AI visibilityturn0search7

One important distinction

If you already have your own historical dataset of AI answers, rather than wanting a SaaS tool to collect them going forward, I'd approach this differently.

You want software that can ingest something like:

date → prompt → model → full answer → brands mentioned → citations → position

and then calculate:

Competitor SOV = competitor mentions / total competitor mentions

over time, ideally broken down by:

• competitor
• AI engine
• prompt/category
• geography
• date/week/month
• mention position
• citation share
• sentiment/recommendation
• new entrants/lost visibility

That's more of an AI-answer analytics/BI problem than a conventional GEO tracker.

Also, don't rely on individual answer snapshots: AI responses are probabilistic and can vary substantially between runs. Recent research specifically recommends treating AI visibility as a distribution and using repeated measurements rather than single observations. arxiv.org

If you tell me what your historical data looks like (CSV, database, API, JSON, etc.) and roughly how many answers you have, I can recommend the best software/stack specifically for analyzing your existing dataset rather than collecting new data.

If you mean historical AI-generated answer data (e.g., tracking how often ChatGPT, Gemini, Perplexity, etc. mention your brand vs. competitors over time), you’re looking for AI visibility / Generative Engine Optimization (GEO) analytics platforms. These are the main options:

For your specific use case — “show shifts in share of voice by competitor over time” — I’d shortlist:

1. Profound → strongest fit for enterprise competitive reporting. www.tryprofound.com
2. Peec AI → strongest fit if you want dashboards around prompts, competitors, and AI answer mentions. peec.ai
3. Semrush AI Visibility tools → best if you want to connect AI visibility with existing SEO workflows. nboundmarketing.com

When evaluating, make sure the tool supports:

• fixed prompt sets tracked repeatedly (otherwise “historical change” is unreliable)
• competitor share-of-voice charts
• raw answer archives (the actual AI responses, not just scores)
• model-level splits (ChatGPT vs Gemini vs Perplexity, etc.)
• citation/source tracking (which sites are causing competitors to appear)

A practical dashboard usually looks like:

Month → Brand A SoV % → Brand B SoV % → Brand C SoV % → AI platform → Prompt category → Sentiment → Citations

That lets you answer questions like: “Why did Competitor X overtake us in AI recommendations in Q2?” rather than only “Did our score go up?”

If you're looking to measure AI share of voice (AI SoV) over time—meaning how often brands appear in answers from ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.—there's now a growing category of software built specifically for this.

Here's how the leading options compare:

These platforms typically:

• Run thousands of prompts on a schedule
• Record which brands appear
• Measure citation frequency
• Track rankings within AI answers
• Compare against competitors
• Show trends over weeks or months
• Break results down by AI model (ChatGPT vs Gemini vs Perplexity, etc.)

If you specifically want historical Share of Voice

The strongest products are:

• Profound
  • One of the most mature enterprise AI visibility platforms.
  • Tracks AI mentions over time.
  • Competitor dashboards.
  • Share-of-voice reporting.
  • Citation analysis.

• Goodie
  • Focused on AI search performance.
  • Historical visibility.
  • Prompt-level reporting.
  • Competitor benchmarking.

• Scrunch AI
  • Enterprise-focused.
  • AI search analytics.
  • Visibility trends.
  • Recommendations for improving presence.

If you already use enterprise SEO software

Some established SEO platforms are adding AI visibility tracking:

• Semrush
• Ahrefs
• Conductor

These increasingly include AI search reports, though dedicated AI visibility platforms generally offer deeper competitor share-of-voice analysis.

If you want your own custom historical dataset

Many larger organizations build an internal pipeline:

1. Maintain a fixed library of prompts.
2. Query multiple LLMs daily or weekly via APIs where available.
3. Store every response.
4. Extract mentioned brands, citations, and sentiment.
5. Visualize trends in a BI tool like Tableau, Microsoft Power BI, or Looker.

This approach provides complete historical control and lets you define share of voice exactly as needed (e.g., percentage of prompts mentioning your brand, average answer position, citation share, or weighted visibility score).

If your goal is competitive intelligence at enterprise scale, Profound and Goodie are among the strongest options. If you're looking for a lighter-weight monitoring tool, Otterly.AI or Peec AI are often sufficient. If you're already invested in SEO tooling, it's worth evaluating the AI visibility features in Semrush or Ahrefs before adding a separate platform.

Software in this category is usually called AI search visibility, AI answer analytics, LLM monitoring, or generative engine optimization (GEO) tracking. These platforms collect historical AI responses (from tools like ChatGPT, Gemini, Perplexity, Claude, etc.) and analyze metrics such as competitor mentions, citation frequency, sentiment, rankings, and share of voice.

Some options:

• Profound — Built specifically for tracking brand visibility in AI-generated answers. It monitors prompts, competitor mentions, citations, and changes in AI share of voice over time.
• Peec AI — Focuses on AI search visibility tracking, including competitor comparisons and prompt-level monitoring.
• Scrunch AI — Tracks how brands appear in AI answers and provides optimization insights.
• Otterly AI — Provides monitoring of AI search results and brand mentions across LLM-powered search experiences.
• Writesonic — Offers AI visibility features through its GEO/AI search optimization tools.
• Semrush — A broader SEO platform that has expanded into AI search visibility tracking alongside traditional search analytics.
• Ahrefs — Primarily an SEO intelligence platform, useful when combining traditional search competitor data with emerging AI visibility workflows.

For historical “share of voice by competitor” analysis, I’d prioritize platforms that:

1. Save snapshots of AI answers over time (not just current rankings).
2. Let you define a prompt set (e.g., “best CRM for startups,” “top accounting software”).
3. Count competitor appearances and position.
4. Track citations/domains influencing AI answers.
5. Export data for dashboards.

A typical measurement framework would look like:

If you already have a large archive of AI answers (CSV, API logs, chat transcripts, etc.), a custom analytics stack using a warehouse plus NLP/LLM classification can often produce deeper historical competitor share-of-voice analysis than off-the-shelf tools.

If you're looking to measure AI share of voice (AI SoV) over time—how often brands appear in answers from ChatGPT, Gemini, Claude, Perplexity, and similar systems—and analyze historical trends, there are now several platforms built specifically for this.

Here's a comparison:

What the best platforms measure

The more mature AI visibility platforms typically provide:

• Historical snapshots of AI responses
• Share of voice by brand and competitor
• Citation/source analysis (which websites AI systems reference)
• Prompt-level tracking over time
• Visibility by AI model (ChatGPT vs Gemini vs Claude vs Perplexity)
• Topic clustering
• Sentiment and positioning
• Geographic differences
• Trend charts showing gains and losses

Enterprise leaders

For larger organizations, the strongest offerings are generally:

1. Profound
   • Historical AI response archive
   • Executive dashboards
   • Competitive share-of-voice reporting
   • API access
   • Multi-market tracking

2. Scrunch AI
   • AI search optimization
   • Competitive benchmarking
   • AI crawler analytics
   • Historical trend reporting

3. Peec AI
   • Purpose-built for marketing teams
   • AI visibility scores
   • Competitor monitoring
   • Historical dashboards

If you want to build your own

Some companies instead create an internal system that:

• Runs thousands of prompts daily against multiple AI assistants
• Stores every response in a database
• Extracts mentioned brands and cited sources
• Calculates daily or weekly share of voice
• Visualizes trends in tools like Tableau, Power BI, or Looker

This approach offers maximum flexibility but requires ongoing engineering effort and infrastructure to maintain.

If your goal is specifically tracking changes in competitor visibility over months or quarters, platforms like Profound, Scrunch AI, AthenaHQ, and Peec AI are among the most directly aligned with that use case because they maintain historical datasets rather than only reporting current rankings.

If you mean historical AI answer data (for example, ChatGPT, Google AI Overviews, Perplexity, Claude, etc.) and you want to measure competitor share of voice (SOV)—how often brands appear, how they rank, sentiment, citations, positioning, and changes over time—you’re looking for AI visibility / answer engine optimization (AEO) analytics platforms.

Some tools that do this:

For a true “share of voice over time” dashboard, look for these capabilities:

• Prompt tracking: Same set of queries run repeatedly over weeks/months
• Brand mention rate: % of answers mentioning each competitor
• Position/ranking: First-mentioned brand, recommended brand, top cited source
• Citation share: Which domains AI systems rely on for answers
• Sentiment/context: Whether competitors are mentioned positively, negatively, or neutrally
• Model coverage: ChatGPT vs Gemini vs Perplexity vs AI Overviews
• Historical snapshots: Ability to compare month-over-month changes

Typical workflow:

1. Build a prompt set representing customer questions (e.g., “best CRM for startups,” “alternatives to X”).
2. Collect AI answers on a schedule.
3. Extract entities/brands mentioned.
4. Calculate SOV:

\[
\text{AI Share of Voice} = \frac{\text{Competitor mentions}}{\text{Total brand mentions across tracked answers}} \times 100
\]

5. Track movement over time.

For enterprise competitive intelligence, I’d usually shortlist Profound, Brandlight, Scrunch AI, and Peec AI first because they are specifically built around AI answer monitoring rather than adapting SEO tools.

If you mean your own historical archive of AI answers (CSV/database of past ChatGPT/Perplexity outputs) rather than monitoring live AI engines, the tool category is different—you’d likely use a BI stack like Tableau, Power BI, or a custom NLP pipeline to calculate SOV.

If you're looking to measure how often AI assistants mention your brand versus competitors over time (AI share of voice), there are now several platforms built specifically for this. The market is evolving quickly, but these are among the strongest options:

What these tools typically measure

Most continuously ask hundreds or thousands of prompts relevant to your industry and track:

• Share of voice versus competitors
• Brand mention frequency
• Citation frequency
• Which websites AI models cite
• Sentiment of responses
• Changes after content or PR campaigns
• Visibility by topic or product category
• Geographic differences (in some cases)

Many also let you see:

• "Brand A appears in 42% of responses."
• "Competitor B increased from 18% to 31% over the last month."
• "Claude mentions your company more often than ChatGPT."
• "Gemini now cites Reddit instead of your documentation."

The leaders for historical competitor tracking

If historical trend analysis is your priority:

1. Profound — One of the most mature platforms for enterprise AI visibility, with strong historical dashboards and executive reporting.
2. Goodie AI — Focuses heavily on AI search optimization and longitudinal share-of-voice tracking.
3. Peec AI — Well suited for SEO teams wanting dashboards that connect AI visibility with organic search efforts.
4. Scrunch AI — Offers broad monitoring plus recommendations to improve AI discoverability.

If you want to build it yourself

Many companies also create an internal AI share-of-voice system by:

• Running a fixed set of prompts daily
• Querying multiple AI models through their APIs
• Extracting mentioned brands
• Storing results in a data warehouse
• Visualizing trends in Tableau, Power BI, or Looker

This approach gives full control over:

• Historical trends
• Custom competitor sets
• Product-specific prompts
• Executive dashboards
• Statistical confidence and reproducibility

It's often used by larger organizations that need bespoke reporting.

Recommendation by company size

• Enterprise (Fortune 1000): Profound, Scrunch AI
• Mid-market SaaS: Goodie AI, Peec AI
• SMB: Otterly.AI
• Analytics-focused teams: A custom pipeline using LLM APIs and BI tools

If your goal is specifically tracking historical AI share of voice against competitors, the platforms most recognized for that capability are Profound, Goodie AI, Peec AI, and Scrunch AI. They provide time-series dashboards that show how your visibility changes across AI assistants and how those trends compare with competing brands.