ChatGPT answers, unedited

If you mean historical AI-generated answer data (e.g., ChatGPT, Gemini, Claude, Perplexity responses over time) and want to measure competitor share of voice (SoV)—how often each brand appears, how that changes, and which prompts drive the movement—you’re looking for AI visibility / generative engine optimization (GEO) analytics platforms. Several tools now specialize in this. CITATIONLY RankZero

Tools to evaluate

If you already have historical answer exports

If you have a database of past AI answers (CSV, JSON, API logs, etc.), you may not need a SaaS tool. A common stack is:

• Data warehouse: Snowflake, BigQuery, PostgreSQL
• Entity extraction: LLM classification or NLP pipelines to detect:

• brand mentions
• competitor mentions
• sentiment
• answer position
• citations/sources
• BI layer: Tableau, Power BI, Looker, or Metabase
• Metrics:

• AI Share of Voice = competitor mentions ÷ total tracked brand mentions
• mention rate by prompt category
• trend by model (ChatGPT vs Gemini vs Claude, etc.)
• citation share
• average answer position

What I would compare when choosing

For a serious competitive program, check whether the tool supports:

1. Historical backfill — can it analyze old answers, not just future monitoring?
2. Prompt-level drilldown — can you see which questions caused a competitor’s gain?
3. Model segmentation — ChatGPT vs Gemini vs Claude vs Perplexity.
4. Competitor discovery — does it find unexpected brands appearing in answers?
5. Raw answer storage — can analysts inspect the underlying AI responses?

If you already have the historical AI answer dataset, the best fit may be a custom analytics layer rather than a monitoring SaaS. If you tell me the data source (ChatGPT logs, API calls, scraped answers, enterprise search logs, etc.) and volume, I can suggest a more specific architecture.

Yes. If your goal is specifically “take historical AI answers and show how our share of voice has shifted vs. competitors over time,” there are now several purpose-built platforms.

Strong options

• Profound — enterprise-oriented AI visibility/answer-engine intelligence. Good fit if you have a large prompt set, multiple brands/markets, and want historical competitive analysis.
• DemandSphere — tracks LLM visibility historically and explicitly supports position and share-of-voice vs. competitors across AI engines. DemandSphere
• RankZero — particularly focused on competitor head-to-heads, showing SOV by engine, prompt, and over time, plus the sources that competitors are getting cited from. RankZero
• Searchpanel — tracks competitors on the same scoring framework as your brand, including share of voice, head-to-head position, citations, and historical answer data. Searchpanel
• CrowdReply — interesting if you want the SOV movement tied directly to the prompts/models responsible for the change. It says it can show week-over-week changes and their underlying prompts and competitors. CrowdReply
• SearchDock — useful if you specifically want SOV calculated from stored historical captures, with separate trends for ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. SearchDock
• Rankvia — has a clearly documented methodology for calculating AI SOV from observed answers and supports historical/platform-level competitive measurement. Rankvia

The key distinction I'd look for

If you already have a historical dataset of AI answers, don't just buy a tool that gives you a current “AI visibility score.” You want one that can preserve/reprocess the underlying observations:

date → prompt → AI engine → answer → brands mentioned → citations → position

Then you can calculate something like:

Competitor A SOV = AI answers mentioning Competitor A ÷ total competitor-brand appearances

and plot that monthly/weekly.

That's materially better than a dashboard that only gives you today's visibility score. Also check whether the platform lets you add a competitor later and backfill historical SOV; CrowdReply, for example, says it can backfill a newly added competitor from existing prompt data. CrowdReply

If you tell me (1) whether you already have the historical AI-answer data, (2) which engines—ChatGPT/Gemini/Perplexity/etc., and (3) roughly how many prompts, I can narrow this to the 2–3 tools that fit and compare their historical SOV capabilities.

Yes. If by “historical AI answer data” you mean a stored time series of LLM responses and you want to see how your brand’s share of voice has shifted against competitors, there are several purpose-built platforms.

Best options

• tryprofound.com — best for enterprise/deep analysis.

It runs tracked prompts daily, stores the resulting AI answers, and provides Share of Voice, visibility rank, citation share, sentiment, and position over time. You can break results down by prompt, topic, platform, region, and competitor. Its SOV chart specifically supports day-to-day trend analysis. Profound Help Center Profound Help Center Profound
Best if: you need serious historical reporting and competitive intelligence.

• otterly.ai — best straightforward SOV dashboard.

It tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, stores answers, and calculates mentions, average rank, sentiment and share of voice for each competitor. Its SOV view is explicitly a trend line that can be broken down by prompt, engine and market. Otterly Otterly AI
Best if: you primarily want “our SOV went from 18% → 24% while Competitor A fell from 31% → 25%.”

• peec.ai — best for marketing teams.

It tracks visibility, position, sentiment and SOV daily across AI platforms and lets you benchmark named competitors on the same prompts. It also has competitor gap analysis and can identify where competitors are appearing while you aren't. Peec AI Peec AI
Best if: you want the SOV trend plus recommendations for what to do about competitive gaps.

The important distinction

There are actually two different things you might mean by “historical AI answer data”:

1. Historical tracking of your own prompt set

Example: “We have run 2,000 prompts every day since January. Show me how Nike's vs. Adidas's SOV changed.”
→ Profound, OtterlyAI, or Peec are a good fit.

2. Historical analysis of what real users have been asking AI, rather than a synthetic prompt set

→ Profound is particularly interesting. Its newer Profound Index is based on 1.5B+ real user conversations and provides category-level competitive AI visibility benchmarks. Profound Profound Help Center

My shortlist

If your specific requirement is “upload/use our historical AI answers and calculate how competitor SOV has shifted over time,” I'd start with Profound. It has the most developed analytical model around historical competitive visibility, rather than simply counting brand mentions. Profound Profound Help Center

If you tell me what your historical data looks like (CSV of answers, API data, ChatGPT transcripts, prompts + answers, etc.), I can also identify which software can ingest that existing dataset rather than requiring you to start collecting data from scratch.

Yes. If by “historical AI answer data” you mean stored responses to the same prompts over time, then you want an AI search / GEO visibility platform with competitor-level Share of Voice (SOV), not a traditional SEO rank tracker.

Best fits

• Visibility.so — probably the closest match to your description. It tracks the exact AI answers, calculates brand-vs-competitor SOV, and shows historical week-over-week changes. It covers ChatGPT, Claude, Gemini, and Perplexity. Visibility.so

visibility.so

• RankZero — strong if you specifically want competitive intelligence. It tracks SOV by prompt and engine, shows when competitors pull ahead, and provides a head-to-head timeline plus the prompts where you're losing. RankZero

rankzero.io

• CrowdReply — particularly interesting if you already have a historical prompt dataset. It says it can calculate SOV by brand, LLM, prompt category, and intent, and attribute week-over-week changes to the prompts/models/competitors driving them. CrowdReply

crowdreply.io

• BrandReply — more focused on the straightforward metric: “what percentage of AI answers mention us vs. competitors?” It also gives prompt-level gaps, competitor ranking, sentiment, and trend lines. BrandReply

brandreply.ai

• Searchpanel — useful if your emphasis is competitive SOV + citations. It calculates SOV over a tracked prompt set and lets you compare the sources AI is citing for you versus rivals. Searchpanel

searchpanel.ai

If you already have the historical answer data

That's an important distinction. If you have, say:

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

you don't necessarily need another tool to collect the data. You need an analytics layer that can normalize the answers and produce:

• Share of Voice: % of competitive mentions belonging to each brand
• Mention rate: % of prompts where each brand appears
• Prominence: position/order in the answer
• SOV change: week-over-week / month-over-month
• Competitor gains/losses
• Prompt-level winners and losers
• SOV by AI engine
• SOV by topic / intent
• Citation/source overlap
• “Competitor entered/leaving answer” events

For example:

That lets you answer the much more useful question: “Why did our AI visibility gain 7 points, and which competitors did we take share from?”

My shortlist: I'd start with Visibility.so, RankZero, and CrowdReply. If you tell me whether you already have the historical AI answers in a database/CSV or need a tool to collect them going forward, I can narrow this down considerably.

Yes. The category you’re looking for is usually called AI search visibility / GEO / AEO tracking. These platforms repeatedly run a defined set of prompts against ChatGPT, Perplexity, Gemini, Claude, Google AI Overviews, etc., then measure brand mentions, citations, position, and competitor share of voice over time.

Best fits for your specific use case

• Peec AI — probably the closest match. It explicitly tracks share of voice vs. competitors, with daily metrics and historical trends broken down by AI engine. It also lets you compare visibility, position, sentiment and SOV side-by-side. Peec AI

peec.ai

• Profound — strongest choice if you're doing this at enterprise scale. It tracks prompts, AI answers, citations, competitors and visibility across multiple answer engines, with deeper analytics around why visibility changes. Independent 2026 comparisons rate it particularly highly for overall AI-search monitoring. TechnologyAdvice Benchline Reports

tryprofound.com

• Otterly.AI — more lightweight and marketing-team-friendly. Good if your goal is essentially "run these prompts every week and show me how our visibility compares with competitors." TechnologyAdvice

otterly.ai

• Ahrefs Brand Radar — interesting if you're already an Ahrefs customer and want AI visibility alongside traditional SEO/competitive data. It has AI citation and competitor benchmarking capabilities. TechnologyAdvice DataDab

ahrefs.com

• Semrush AI Visibility Toolkit — worth considering if your team already lives in Semrush and wants AI visibility added to the existing SEO reporting stack. TechnologyAdvice DataDab

semrush.com

If you specifically mean historical data you already have

There's an important distinction:

A. "I want software to collect this going forward and show SOV trends."
→ Peec AI or Profound are my first two to evaluate.

B. "I already have thousands of historical AI answers stored and want software to analyze that dataset."
→ That's a different problem. Most GEO trackers are designed primarily around their own collected prompt history, rather than ingesting an arbitrary historical corpus. You'd likely want a data/BI layer that can parse your existing answers into:

date → prompt → AI engine → brand mentioned → competitor mentioned → position → citation → sentiment

and then calculate:

Share of Voice = competitor mentions / total qualifying brand mentions

You could then plot, for example:

Your brand: 42% → 37% → 29%
Competitor A: 18% → 24% → 31%
Competitor B: 12% → 15% → 19%

That lets you identify when a competitor took share and drill back to the actual AI answers/prompts responsible.

One particularly useful feature to look for is prompt-level historical replay, rather than just a single "AI visibility score." That gives you the ability to say "Competitor X gained 9 points of SOV since March because they started appearing in these 47 prompts across ChatGPT and Perplexity." Peec explicitly exposes this kind of competitor/SOV analysis, while Profound tends to offer deeper enterprise-level diagnostics. Peec AI TechnologyAdvice

If you tell me whether you already have the historical AI answers (CSV/database/API) or need a tool to start collecting them, I can narrow this down to the 3 best options and compare their historical-data capabilities, API/export support, pricing, and SOV methodology.

Yes. What you’re describing is now generally called AI search visibility / AI share-of-voice tracking: the software repeatedly runs a fixed set of prompts against AI answer engines, stores the answers, identifies brand/competitor mentions, and charts how visibility changes over time. NeuralGen The Rank Masters

Best options

• tryprofound.com — best for enterprise/deep historical analysis. Strong prompt-level, competitor, citation/source, and historical reporting. I'd shortlist this if you need defensible reporting for leadership or a large brand. AI Search Tools Bloomiro
• peec.ai — probably the best fit for exactly “how has our share of voice shifted vs competitors?” It emphasizes prompt-level visibility, competitor benchmarking, sentiment, citations, and trend data. AI Search Tools The Rank Masters
• otterly.ai — good lower-cost starting point. Tracks mentions, citations and competitor visibility over time, but the analytics are less deep than Profound/Peec. AI Search Tools
• scrunchai.com — worth considering if you want broader AI-engine coverage and stakeholder-friendly reporting/competitor heatmaps. AI Search Tools
• Promptwatch — interesting if you want to go beyond measurement into finding the competitor answer gaps and taking action on them, rather than just reporting the trend. AI Search Tools

For your specific use case

If your underlying question is:

“From January to September, how did Brand A's share of AI answers change relative to Brand B, C and D, and which prompts/models drove the change?”

I'd rank them:

1. Peec AI → best balance of competitor SOV + historical/prompt analytics
2. Profound → best if you need enterprise-grade depth and reporting
3. Scrunch → good reporting/heatmaps
4. Otterly → good if cost and simplicity matter

One important distinction: make sure the product stores the actual historical answers, not merely today's aggregate visibility score. You want to be able to drill from SOV → competitor → prompt → model → individual answer → cited sources. Otherwise, you can see that share of voice moved but not understand why. AI Search Tools

If you tell me roughly how many prompts, competitors, and AI engines you need to track, I can narrow this to the 2–3 products that best fit.

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

Some options:

If you already have historical AI answer exports

If you have a database of past answers (for example: prompt, date, model, response text), you can also build this yourself:

1. Store every answer with: - timestamp
2. AI model (ChatGPT, Claude, Gemini, etc.)
3. prompt/category
4. response text
5. Run entity extraction to detect: - your brand mentions
6. competitor mentions
7. recommendation rank/order
8. sentiment/context
9. cited sources
10. Calculate: - AI Share of Voice = competitor mentions ÷ total tracked brand mentions
11. trend by week/month
12. SoV by model
13. SoV by buyer intent category

For a mature marketing team, I’d usually shortlist:

• Scrunch or Sonalyze for broad enterprise monitoring
• CiteGraph or Searchpanel if you care about auditability of the underlying answers and citations
• A custom warehouse + LLM extraction pipeline if you already have years of historical answer logs

One key question: do you already have the historical AI answers stored, or are you looking for a tool that will start collecting them and build the trend line going forward?

Yes. What you’re describing is generally called AI search visibility / GEO (generative engine optimization) monitoring: software that stores AI answers over time and calculates how often your brand and competitors appear, are cited, and rank within those answers.

Best fits

• tryprofound.com — best for enterprise/historical analysis. Tracks prompts and AI answers over time and provides competitor share-of-voice, visibility, citations, and deeper analytics. I’d shortlist this if you want to answer questions like “Our AI share of voice went from 18% to 27% while Competitor A fell from 31% to 24%.” DataDab Design Copy
• peec.ai — best for straightforward competitor benchmarking. It is particularly focused on comparing brands across the same prompt sets and showing changes in visibility over time. Tenten GEO
• otterly.ai — good lower-cost monitoring option. Tracks recurring prompts and brand mentions/citations, with competitor monitoring, although I'd favor Profound if historical competitive analytics are the primary requirement. AISEOShift
• scrunchai.com — worth considering if you want broader AI-engine coverage and visibility/citation analytics. It was acquired by Sitecore in 2026. AISEOShift
• ahrefs.com — interesting if you're already an Ahrefs customer and want AI-answer visibility alongside conventional SEO/brand data. DataDab

If your specific requirement is historical data → share-of-voice trend

I'd rank them:

One important caveat: these platforms aren't really analyzing an arbitrary historical archive of AI answers in the way a BI tool analyzes a database. Most continuously run a defined prompt set against AI engines and build the historical dataset themselves. So if you already have years/months of saved ChatGPT, Gemini, Perplexity, etc. answers, the question becomes different: you may want a tool/API that can ingest your existing answer corpus and calculate SOV retrospectively.

If that's your situation, tell me what format your historical AI answers are in (CSV, JSON, database, screenshots, etc.) and which AI engines, and I can identify the software that can actually ingest that historical data rather than just start tracking from today.

Yes. If you mean historical AI-generated answers—e.g., you have thousands of past ChatGPT/Perplexity/Gemini answers and want to see “our brand went from 18% to 27% share of voice while Competitor A fell from 35% to 24%”—there are several tools built for this now.

Best options

• Profound — probably the strongest choice for enterprise-grade historical analysis. It tracks AI answers, competitor mentions, citations, prompts, and trends, with relatively deep analytics. Tomek Sikora GitHub
• Peec AI — good balance of competitive visibility tracking and citation/source analysis. Particularly useful if you want to understand why a competitor is winning, not just who is winning. Conbersa
• Otterly AI — more lightweight and accessible. Good for scheduled monitoring of a defined prompt set and seeing changes in visibility/mentions over time. Design Copy
• OnPagePilot — explicitly snapshots share of voice daily, retains historical scans, and overlays competitor SOV so you can see trends rather than a single snapshot. OnPagePilot
• SearchDock — interesting if you already have historical answer/capture data. Its share-of-voice reporting is calculated from stored captures rather than firing a new AI query when you view the report. SearchDock
• Birdeye — worth considering if this is part of a broader local/reputation/brand-monitoring workflow; its AI visibility reports include competitor SOV over time across ChatGPT, Gemini and Perplexity. Birdeye Help Center

If you already have the historical answer data

This changes my recommendation. You don't necessarily need another AI-monitoring platform. What you need is an analytics layer that can ingest your historical answers and calculate something like:

Ideally, you also want to slice that by AI engine, prompt/topic, geography, product, mention vs. citation, and answer position.

One important methodological point: don't simply calculate “mentions / total answers” and call that competitive SOV. Different answers can mention multiple competitors. Some platforms instead calculate your share against the configured competitor set, while others use answer-level presence. The definition can materially change the trend. Citations.io SearchDock

My shortlist:
Profound if you want enterprise analytics → Peec if you want strong competitive/source analysis → SearchDock or a custom BI layer if you already possess the historical answer corpus.

If you tell me what your historical data looks like (CSV, JSON, database, API, etc.) and which AI engines it covers, I can recommend the best software specifically for analyzing your existing dataset, rather than tools that only monitor future answers.

Yes. What you’re describing is usually called AI search visibility / GEO tracking / share of model (or AI share of voice): software repeatedly captures AI answers, stores the historical responses, identifies brand/competitor mentions and citations, and charts how each competitor’s share changes over time.

Best options

Profound is probably the one I'd investigate first if you have a substantial historical dataset and want to answer questions like “Did Competitor X gain 12 points of AI share of voice after this period, and which prompts/citations drove the change?” It focuses on prompt-, answer-, competitor-, and citation-level analysis. Tomek Sikora Design Copy

Peec AI is another strong choice if competitive benchmarking and citation/source analysis are the priority. GitHub

If you already have the historical AI answers rather than needing software to collect them, that's an important distinction. You may not need a monitoring platform at all—you need an analysis layer that can ingest your historical answer corpus and calculate:

• competitor mention rate
• share of voice by competitor
• share of citations
• average position/order in the answer
• sentiment/recommendation strength
• SOV by prompt/topic
• SOV by model (ChatGPT vs Gemini vs Perplexity, etc.)
• SOV change over time
• which prompts caused a competitor to gain/lose share
• which URLs/domains are responsible for the change

For example, SearchDock explicitly calculates SOV from stored AI-answer captures rather than making a new model call when you open the report, which is particularly relevant to your "historical AI answer data" requirement. SearchDock OnPagePilot similarly describes daily historical snapshots and competitor SOV overlays. OnpagePilot

My shortlist

If your key requirement is “I have historical answers and want to see competitor SOV shifting over time”, I'd evaluate:

1. Profound — deepest enterprise-grade analysis.
2. Peec AI — strong competitor/citation analysis.
3. SearchDock — particularly interesting if your existing stored answer data is the starting point.
4. Zumi — good visualization of SOV trends and competitor movement. Zumi Zumi
5. OnPagePilot — simpler historical SOV implementation. OnpagePilot

One caveat: don't compare SOV numbers between vendors without checking their methodology. They can use different prompt sets, definitions of "mention," citation counting, competitors, and model sampling, so two platforms can report materially different SOV for the same brand. Design Copy

If you tell me what your historical data looks like (e.g. CSV of prompts + ChatGPT answers from the last 12 months), I can narrow this down to the 2–3 tools that can actually ingest/analyze it rather than requiring you to start collecting data again.

Yes. What you’re describing is generally called AI search visibility / AI share-of-voice tracking: ingesting historical answers from ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, etc., then showing how often you and competitors are mentioned over time.

The strongest options I’d look at are:

My shortlist

1. Peec AI — probably closest to your description

Peec explicitly calculates visibility and share of voice against competitors, with daily metrics broken down by engine. You can track prompts and see how your position, sentiment, visibility and SOV change over time. Peec AI

peec.ai

2. Profound — best if you have a large historical dataset

Profound is more enterprise-oriented and focuses heavily on prompt-level monitoring and competitive analytics. I'd favor it if you need lots of prompts, markets, engines and executive reporting rather than just a lightweight dashboard. MentionRadar GitHub

tryprofound.com

3. OtterlyAI — particularly good for explicit SOV trend analysis

Otterly says it stores the generated answers and calculates mentions, rank, sentiment and share of voice across competitors, with trend lines that can be broken down by prompt, engine and market. Otterly

otterly.ai

One important distinction

If by "historical AI answer data" you mean you already have a database of thousands/millions of past AI responses and want software to analyze your existing dataset, that's a somewhat different requirement.

Most AI-visibility products primarily collect the answers themselves on an ongoing schedule and then calculate SOV. They aren't necessarily designed as a general-purpose analytics layer where you upload your own historical answer corpus.

For your use case, I'd look for these capabilities specifically:

• Answer-level historical storage
• Competitor/entity extraction from each answer
• SOV = competitor mentions / total relevant brand mentions
• SOV trends by week/month
• SOV by prompt/category
• SOV by AI engine
• Average position/rank
• Citation share in addition to mention share
• Ability to compare a competitor's gain/loss in SOV over time
• Raw-answer export/API, so you can do your own analysis

If you tell me what your historical data looks like (e.g. CSV of 100k ChatGPT answers, API responses, or data from an existing platform), I can recommend the best software specifically for analyzing that existing dataset, rather than just monitoring future AI answers.

My shortlist

1. Peec AI — probably closest to your description

Peec explicitly calculates visibility and share of voice against competitors, with daily metrics broken down by engine. You can track prompts and see how your position, sentiment, visibility and SOV change over time. Peec AI

peec.ai

2. Profound — best if you have a large historical dataset

Profound is more enterprise-oriented and focuses heavily on prompt-level monitoring and competitive analytics. I'd favor it if you need lots of prompts, markets, engines and executive reporting rather than just a lightweight dashboard. MentionRadar GitHub

tryprofound.com

3. OtterlyAI — particularly good for explicit SOV trend analysis

Otterly says it stores the generated answers and calculates mentions, rank, sentiment and share of voice across competitors, with trend lines that can be broken down by prompt, engine and market. otterly.ai

otterly.ai

One important distinction

If by "historical AI answer data" you mean you already have a database of thousands/millions of past AI responses and want software to analyze your existing dataset, that's a somewhat different requirement.

If you mean historical tracking of AI-generated answers (ChatGPT, Gemini, Claude, Perplexity, etc.) to see how your brand’s “share of voice” changes versus competitors, you’re looking for AI visibility / Generative Engine Optimization (GEO) analytics platforms. These tools repeatedly run a prompt set, store answers, extract brand mentions, and trend competitor presence over time. Sonalyze Surva.ai

Some options:

Key capabilities to look for:

• Prompt panel history — the tool should rerun the same buyer questions every week/month so shifts are meaningful. AnswerMonk
• Competitor mention extraction — not just rankings, but “which brands appeared in the answer.” Sonalyze
• Model-level splits — ChatGPT vs Gemini vs Claude vs Perplexity often produce different competitive landscapes. Cogvert
• Citation/source analysis — identify which websites, reviews, or content sources are causing a competitor to win mentions. Searchpanel
• Backfilled history — useful if you add competitors later and want to compare against past data. CrowdReply

If you already have exported historical AI answers (CSV, transcripts, API logs), another route is to build this yourself:

1. Store answers by date/model/prompt.
2. Use an LLM classifier to extract every brand mentioned.
3. Calculate: - mention rate = % of answers containing brand
4. share of voice = brand mentions ÷ all competitor mentions
5. sentiment/recommendation rank
6. Trend the metrics in BI tools like Tableau or Power BI.

For a marketing team trying to answer “Are we losing AI recommendations to competitors?,” I’d prioritize a GEO-specific platform rather than a general SEO suite, because traditional SEO tools generally do not capture AI answer mentions directly. Sonalyze

Yes. If by “historical AI answer data” you mean a dataset of actual ChatGPT/Gemini/Perplexity/etc. responses over time, there are several purpose-built platforms now.

Best options

• scrunch.com — probably the closest match to your description. It calculates AI share of voice from actual responses, comparing how often your brand and competitors are mentioned. You can trend it over time and slice by competitor, prompt/topic, AI platform, geography, persona, etc. It also lets you add competitors and apply them to historical data. Scrunch Scrunch
• tryprofound.com — strongest for deeper competitive/enterprise analysis. Its Answer Engine Insights dataset includes daily prompt responses and metrics including visibility, share of voice, citation share, position, sentiment, and competitor rankings. Its SOV chart can show day-to-day changes, and results can be broken down by platform and topic. Profound Profound Knowledge Base Profound Knowledge Base
• peec.ai — good lighter-weight option. It tracks visibility, position, sentiment and share of voice versus competitors across AI models, with daily updates and per-engine comparisons. Peec AI

If your specific goal is “show me how competitive SOV has shifted”

I'd rank them:

1. Scrunch → best fit for historical competitive share-of-voice trending
2. Profound → best if you want SOV plus sophisticated citation/competitive intelligence
3. Peec → good if you want a simpler AI-visibility dashboard

One important distinction: “visibility” and “share of voice” aren't necessarily the same metric. For example, Profound defines SOV relative to total category visibility, while Scrunch calculates competitive presence based on the percentage of tracked AI responses mentioning each brand. Profound Scrunch

If you already have the raw historical AI answers yourself (CSV/JSON/database), rather than wanting a monitoring SaaS, I can also suggest the best analytics stack for turning those answers into a competitor-by-month SOV chart + gain/loss analysis.

Yes. If by “historical AI answer data” you mean a stored dataset of repeated ChatGPT/Claude/Gemini/Perplexity answers, and you want to see how often each competitor was mentioned/recommended over time, there are several purpose-built platforms.

Best fits

• tryprofound.com — probably the strongest enterprise-oriented option. It focuses on AI visibility/GEO, competitive benchmarking, citations, prompts, and historical performance. Good if you need executive reporting and a larger research program.
• visibility.so — particularly aligned with your question. It explicitly provides historical trends, competitor AI share of voice, exact answer inspection, and source rankings. Visibility.so
• scoutllm.ai — tracks visibility and competitor share of voice daily, with per-engine breakdowns and historical trends. It also records the actual answers and citations behind the measurements. Scout LLM
• searchdock.io — interesting if you specifically want SOV calculated from stored historical captures, rather than having the dashboard make a fresh model call. It provides per-engine historical series and raw mention/citation counts alongside the derived SOV. SearchDock
• llmmetrix.com — good for head-to-head competitor analysis. It plots each competitor's share over successive scans and can alert when a rival's SOV changes materially. LLM Metrix
• brandreply.ai — specifically built around AI share of voice, tracking mentions, position, sentiment and competitor ownership across repeated prompt runs. BrandReply

One important distinction

If you already have your own historical answer dataset—for example:

date | prompt | model | answer | brands mentioned | citations

—you don't necessarily need an AI-visibility SaaS. You could put that data into a BI/analytics stack and calculate:

Competitor SOV = competitor mentions ÷ total competitor mentions

then plot it by week/month, model, prompt category, geography, etc.

If you're starting from scratch, I'd shortlist Profound, Visibility.so, and Scout LLM. If you already possess the historical answers, I'd look for software that can import/analyze existing answer data, because many AI-visibility platforms primarily analyze answers that they themselves collected.

If you tell me roughly how your historical data is stored (CSV, database, API, etc.) and how many answers/prompts you have, I can narrow this down to the 2–3 tools that actually fit.

Yes. If by “historical AI answer data” you mean a corpus of past ChatGPT/Gemini/Perplexity/etc. answers and you want to see how competitor mentions and share of voice changed over time, there are several purpose-built platforms.

Best fits

• tryprofound.com — strongest overall for enterprise

• Tracks AI answers daily and maintains historical data.
• Share of Voice, visibility, position, sentiment, and citation share.
• Competitor benchmarking by prompt, topic, platform, and time period.
• Its SOV charts explicitly show day-to-day changes, and you can compare against a previous period. Profound Profound Knowledge Base
• Particularly good if you want to answer: “Did Competitor A gain AI visibility at our expense, and on which prompts?”
• peec.ai — very good for straightforward SOV monitoring

• Measures AI visibility, position, sentiment and Share of Voice versus competitors.
• Tracks across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode and other engines.
• Data is updated daily, allowing you to see trends in mentions and SOV. Peec AI
• Good choice if your primary dashboard is essentially brand vs. competitor over time.
• otterly.ai — worth considering for a lighter-weight setup

• More focused on practical AI-search monitoring and competitive visibility than deep enterprise intelligence.
• Recent independent benchmarking puts it among the most frequently appearing AI-visibility tools. Foglift

The important distinction

If you already have historical answer data—for example, thousands of JSON/CSV records containing:

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

—then I wouldn't necessarily buy an AI-visibility tracker just to calculate SOV.

You can calculate something like:

Competitor SOV = competitor mentions ÷ total competitor mentions × 100

and then plot it by week/month, model, prompt category, geography, etc. That's essentially how platforms such as Profound define the metric. Profound Knowledge Base

For example:

That lets you see “Competitor A took +16 percentage points of AI SOV while we lost 14 points”, rather than just looking at raw mention counts.

If you tell me what format your historical AI answer data is in (CSV, database, Snowflake, BigQuery, etc.), I can recommend the best software specifically for analyzing that existing dataset rather than collecting new AI answers.

Yes. If by “historical AI answer data” you mean you already have a corpus of past ChatGPT/Perplexity/Gemini/etc. answers and want to see how the percentage of answers mentioning each competitor changes over time, there are several tools built specifically for this.

Best fits

• scrunch.com — probably the closest match. It stores AI responses, calculates competitive presence/share of voice, and lets you trend it over time by competitor, topic, prompt, platform, persona, funnel stage, etc. It also lets you inspect the underlying answers. Scrunch Scrunch
• otterly.ai — strong if you want a straightforward dashboard for mentions, share of voice, competitor benchmarking, sentiment, and citations across multiple AI engines. It explicitly tracks the generated answers and scores competitors on the same prompts. Otterly
• visibility.so — particularly interesting for your exact use case because it offers historical week-over-week trends, competitor SOV, and access to the exact AI answer behind each measurement. Visibility.so
• tryprofound.com — worth considering for an enterprise-grade AI visibility program. It's one of the major platforms in this category, particularly if you need deeper analytics and large-scale monitoring. The Wall Street Journal Lyra
• answermonk.ai — interesting if you specifically want to replay a consistent prompt panel and measure competitor SOV changes month over month. Its methodology explicitly compares recorded answers and reports who moved. AnswerMonk

What I'd choose

If your requirement is:

“Take historical AI answers → identify every brand mentioned → calculate competitor share of voice → show how that share shifts by week/month/topic/model.”

I'd shortlist Scrunch, Otterly, and Visibility.so.

Scrunch looks strongest for analytical segmentation. Its documentation specifically describes trending SOV and slicing it by topic, platform, persona, funnel stage, country, branded/non-branded prompts, etc. Scrunch

If you already have the historical answer dataset yourself, rather than needing a vendor to collect it, that's a slightly different problem. You may not need a full AI-visibility platform—you could build the analysis layer yourself:

raw AI answers → entity/brand extraction → answer-level mentions → competitor normalization → SOV calculation → time-series dashboard

In that case, I can also recommend software specifically for importing your existing historical answer data (rather than tools that collect new answers), which is a narrower and potentially more useful shortlist.

If you mean historical AI-generated answer data (e.g., past ChatGPT/Gemini/Claude/Perplexity responses) and want to see competitor share of voice trends—who is being mentioned, recommended, cited, or gaining/losing visibility over time—you’re looking for AI visibility / GEO (Generative Engine Optimization) intelligence platforms rather than traditional SEO tools.

Some options:

Key capabilities to look for:

• Prompt replay / historical snapshots - "Run the same 500 buyer questions every week/month and compare changes."
• Competitor mention extraction - Brand mentions, recommendations, rankings, sentiment.
• Citation analysis - Which websites, reviews, forums, or articles caused an AI model to favor a competitor.
• Model-level splits - ChatGPT vs Gemini vs Claude vs Perplexity (competitors often win on one model and lose on another). Cogvert Marketing Pvt Ltd
• Share-of-voice trends - Example: "Competitor A moved from 18% → 31% of AI recommendations over 90 days."

If you already have a dataset of historical AI answers (CSV/API exports/logs), you may not need a SaaS tool. A custom pipeline can work:

1. Store answers + timestamp + model + prompt.
2. Use an LLM/entity extraction step to identify brands mentioned.
3. Calculate: - mention share
4. recommendation share
5. citation share
6. sentiment share
7. Dashboard in Tableau, Microsoft Power BI, or Looker Studio.

For most marketing/SEO teams today, I’d shortlist:

• Enterprise: Scrunch or Gensiv
• B2B competitive intelligence: Sonalyze
• Fast GEO monitoring: AnswerMonk or CrowdReply
• Custom analytics stack: warehouse + LLM extraction + BI dashboard

The right choice depends heavily on whether you already have the historical answer corpus or need the platform to collect it going forward.

If you mean software that takes historical AI-generated answers (ChatGPT, Perplexity, Gemini, Claude, AI Overviews, etc.) and shows how competitor visibility changes over time, you’re looking for AI visibility / Generative Engine Optimization (GEO) analytics platforms. These tools typically track prompts, capture answers, extract brand mentions, and calculate AI share of voice (SoV). Answer Engines Optimization Slate

Some options:

For a typical marketing analytics use case, I’d shortlist:

• Enterprise brand / CMO dashboard: Profound, Otterly.AI, Surva.ai
• SEO/GEO team trying to improve rankings inside AI answers: Peec AI, Rankscale, AnswerMonk
• Need historical competitor movement (“Competitor X gained 12 points of AI SoV since January”): Surva.ai, GeoEcho, Searchpanel, AnswerMonk AnswerMonk GeoEcho Surva.ai

The key capabilities to compare are:

• Prompt panel management — can you define the buyer questions you care about?
• Historical snapshots — does it store every answer over time?
• Competitor discovery — does it find unexpected competitors appearing in AI answers?
• Share-of-voice calculation — mentions only, or weighted by ranking/recommendation position?
• Citation/source analysis — can it show why a competitor is winning (reviews, articles, forums, docs, etc.)? Answer Engines Optimization Searchpanel

If you already have a database of historical AI answers (for example, exported ChatGPT responses), a BI stack like BigQuery/Snowflake + an LLM extraction pipeline + Tableau/Looker/Power BI can also calculate custom AI share-of-voice metrics.

Yes. What you’re describing is usually called AI visibility / GEO tracking: software repeatedly runs a fixed prompt set against LLMs, stores the answers, identifies brand/competitor mentions, and calculates share of voice (SOV) over time.

The strongest options I’d look at are:

• Profound — best fit for larger enterprises and sophisticated competitive intelligence. It tracks AI answers, competitors, citations, and changes over time.
• Peec AI — strong for competitive benchmarking and agency-style reporting; useful if you want to compare multiple brands across prompt sets.
• Otterly.AI — more accessible/SMB-oriented, with AI search monitoring and competitor visibility tracking.
• LLM Metrix — particularly relevant to your exact question: it provides competitor SOV, head-to-head tracking, historical scans, and alerts when a competitor's SOV changes materially. LLM Metrix
• Visibility.so — tracks SOV, historical ranking trends, exact AI answers, citations, and competitor rankings across ChatGPT, Claude, Gemini and Perplexity. Visibility.so
• CrowdReply — interesting if you specifically want to understand why SOV changed, since it ties week-over-week changes to prompts, models, competitor mentions, and the underlying answers. CrowdReply
• Searchable — combines SOV with position, sentiment, citations, and historical change at the prompt/platform level. Searchable

For your specific use case

If the workflow is:

“We have 5,000 historical AI answers → identify every competitor → calculate each competitor's SOV → show how that SOV shifted month by month → let me drill into the answers causing the change.”

I'd shortlist Profound, LLM Metrix, CrowdReply, and Visibility.so.

The important distinction is whether you already have the historical answer dataset. Most of these platforms primarily collect the answers themselves going forward. If you have a large existing dataset (CSV/database of ChatGPT/Claude/Gemini answers), you may actually want an analytics layer that can ingest your historical responses rather than another monitoring tool.

If you tell me what your historical data looks like (CSV, database, API, etc.) and roughly how many answers/prompts you have, I can recommend the best stack for analyzing it.

If you mean historical AI-generated answer data (e.g., past ChatGPT/Gemini/Claude/Perplexity responses) and want to see competitor share-of-voice shifts over time, you’re looking for AI visibility / GEO (Generative Engine Optimization) competitive intelligence platforms. The main options are:

What to look for specifically

For your use case, prioritize tools that store historical answer snapshots, not just a current score. Useful capabilities:

• Prompt panel history — rerun the same buyer questions weekly/monthly
• Competitor mention share — % of tracked answers naming each brand
• Trend charts — “Competitor X gained +12 pts since January”
• Model breakdowns — ChatGPT vs Gemini vs Claude vs Perplexity
• Citation/source analysis — why a competitor is appearing more often
• Answer archive — ability to inspect the exact responses behind the change

A typical metric looks like:

AI Share of Voice = (competitor mentions ÷ total brand mentions across tracked AI answers) × 100

Example:

• January: Your brand 35%, Competitor A 40%, Competitor B 25%
• June: Your brand 28%, Competitor A 52%, Competitor B 20%

The useful insight is not just “you dropped”; it’s which prompts, models, and cited sources caused the shift.

If you already have a historical dataset of AI answers (CSV/API exports), you can also build this yourself with a pipeline:

1. Store answers by date + model + prompt
2. Extract brand mentions with an LLM/NLP classifier
3. Normalize competitor names
4. Calculate share-of-voice by month, prompt cluster, and model
5. Visualize in Tableau, Power BI, Looker, or a custom dashboard

For a marketing/competitive intelligence team, I’d generally shortlist Sonalyze, Visibility.so, AnswerMonk, and LLM Metrix depending on whether you need enterprise reporting, SEO integration, or deeper prompt-level analysis. Sonalyze Visibility.so AnswerMonk LLM Metrix

1. Store answers by date + model + prompt
2. Extract brand mentions with an LLM/NLP classifier
3. Normalize competitor names
4. Calculate share-of-voice by month, prompt cluster, and model
5. Visualize in Tableau, Power BI, Looker, or a custom dashboard

For a marketing/competitive intelligence team, I’d generally shortlist Sonalyze, Visibility.so, AnswerMonk, and LLM Metrix depending on whether you need enterprise reporting, SEO integration, or deeper prompt-level analysis. Sonalyze Visibility.so AnswerMonk LLM Metrix

Yes. The category you’re looking for is generally called AI visibility / LLM visibility / GEO monitoring software. These tools repeatedly run a fixed set of prompts against ChatGPT, Gemini, Perplexity, Google AI Overviews, etc., save the answers, identify which brands appear, and calculate share of voice and competitive movement over time.

Best fits for your use case

For example, OtterlyAI explicitly reports share of voice by prompt, engine, and market as a trend line, while also storing the underlying AI answers and competitor mentions. Otterly

Visibility.so is particularly close to your wording: it provides historical week-over-week trends, competitor SOV, and the exact AI answer for each tracked prompt. Visibility.so

LLM Metrix is interesting if the primary question is “Are we gaining or losing ground to competitor X?” It plots each competitor's SOV scan-by-scan and tracks head-to-head movement over time. LLM Metrix

The important distinction

If you already have historical AI answers/data, rather than wanting to start collecting them, I'd evaluate the products differently.

You want a system that can take something like:

Date → Prompt → Model → Answer → Brands mentioned → Position → Citations

and produce:

• Competitor SOV over time
• Your SOV change vs. each competitor
• SOV by LLM
• SOV by prompt/category/intent
• Who gained when you lost
• Changes in mention position
• Changes in citation share
• The underlying answers explaining why the shift happened

That last point is important: a simple "brand mentioned/not mentioned" metric can obscure meaningful changes in positioning. Some platforms now distinguish appearance/coverage from actual share of the competitive answer space. Otterly AnswerMonk

My shortlist

If I were evaluating these specifically for historical AI-answer data → competitor share-of-voice trends, I'd start with:

1. Profound — strongest candidate if this is an enterprise competitive-intelligence project.
2. OtterlyAI — good if you want a relatively accessible monitoring/analytics product.
3. Peec AI — worth evaluating for marketing-oriented competitive benchmarking.
4. Visibility.so — particularly interesting if historical trend visualization is the core requirement.
5. LLM Metrix — worth a look if head-to-head competitor movement is the primary analysis.

There are also newer products such as AnswerMonk that explicitly replay a fixed prompt panel and calculate monthly changes in each competitor's SOV, which is useful if methodological consistency over time is especially important. AnswerMonk

One caveat: because LLM answers are nondeterministic, a good historical system needs to control for prompt set, model/version, search mode, geography, and sampling frequency. Otherwise an apparent "SOV shift" can partly be measurement noise. Semrush describes this issue explicitly in its own AI-visibility methodology. Semrush

If you tell me what your historical data looks like (CSV, API, database, exported ChatGPT answers, etc.) and roughly how many answers/prompts you have, I can narrow this to the 2–3 tools that can actually ingest/analyze that existing dataset, rather than just tools that collect new data.

You want a system that can take something like:

Date → Prompt → Model → Answer → Brands mentioned → Position → Citations

and produce:

Yes. If by “historical AI answer data” you mean a stored corpus of past ChatGPT/Claude/Perplexity/Gemini/AI Overview answers, there’s now a fairly mature category of software for this.

Best fits

For example, OtterlyAI explicitly stores generated answers and calculates each brand's share of answers, with trend lines that can be broken down by prompt, engine and market. Otterly

Visibility.so is particularly close to what you're describing: it offers historical trends showing how AI presence changes week over week, alongside competitor SOV and the underlying exact answers. Visibility.so

If you already have the historical answer data

This is an important distinction. If you already possess the raw historical answers—rather than wanting a platform to continuously collect them—I'd look for software that can ingest/import historical observations, rather than simply buying an AI-monitoring crawler.

The metric you probably want is something like:

Competitor SOV at time T = competitor mentions / total competitor mentions across the defined prompt set

Then plot it by week/month × competitor, with separate cuts for:

• AI engine
• prompt/topic
• customer intent
• geography
• answer position
• mention vs. citation
• sentiment
• your brand vs. each competitor

That distinction matters because “share of answers mentioning us” and “share of all brand mentions” can produce very different numbers. OtterlyAI, for example, explicitly distinguishes Brand Coverage from Share of Voice. Otterly

My shortlist

If your primary question is “Which competitors are gaining/losing AI share of voice over time?”, I'd start with:

1. OtterlyAI — best straightforward SOV/trend product.
2. Profound — strongest choice if this is an enterprise competitive-intelligence program.
3. Peec AI — good middle ground for marketing/GEO teams.
4. Visibility.so — particularly attractive if historical trend visualization is the core requirement.
5. Similarweb — best if you want AI visibility integrated with broader market/traffic intelligence.

There are also newer tools specifically positioning themselves around stored historical AI answers rather than live dashboard queries; SearchDock, for example, calculates SOV from previously captured answers and keeps per-engine historical series. SearchDock

If you tell me what your historical data looks like (e.g. CSV of prompts + answers + dates, or data already collected from ChatGPT/Perplexity/etc.), I can narrow this to the 3 tools that can actually work with your existing dataset, rather than tools that require you to start collecting data from scratch.

The metric you probably want is something like:

Competitor SOV at time T = competitor mentions / total competitor mentions across the defined prompt set

Then plot it by week/month × competitor, with separate cuts for:

Yes. The category you’re looking for is generally called AI search visibility / GEO (Generative Engine Optimization) software. These platforms repeatedly run a fixed set of prompts, store the resulting AI answers, identify which brands are mentioned/cited/recommended, and calculate share of voice over time.

Best fits

• otterly.ai — Probably the clearest fit if your main question is “How has our share of AI answers changed versus competitors?” It tracks mentions, sentiment, citations and SOV across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with daily trend data. Otterly
• tryprofound.com — Strong enterprise-oriented option for detailed prompt-level monitoring and competitive analysis. Good if you need to retain and analyze lots of historical AI answers rather than just see a headline visibility score. AI Visibility Software GitHub
• peec.ai — Good purpose-built GEO platform for comparing brands across AI engines and monitoring changes in visibility. GitHub Conbersa
• similarweb.com — Worth considering if you want AI share-of-voice data connected to broader competitor/traffic intelligence. It is more of an enterprise market-intelligence solution than a pure AI-answer tracker. Prism News Prism News
• visibility.so — Particularly relevant if you want historical week-over-week trends plus the ability to inspect the actual answer behind a change. It explicitly offers SOV, historical trends, prompt-level answers and source/citation tracking. Visibility.so

What I would look for

If your requirement is specifically:

“Take 6–12 months of historical AI answers and tell me how Brand A's share of voice moved relative to Brand B, C and D.”

I'd prioritize OtterlyAI, Profound, and Visibility.so.

The important distinction is whether the platform actually stores/replays a consistent prompt panel. You want something like:

…and then be able to drill from the 19% → 42% shift down to which prompts, AI engines, citations and answers caused the change. That's much more useful than a generic “AI visibility score.”

One other important point: SOV definitions vary. Some tools calculate it from the proportion of tracked answers mentioning each brand; others calculate a brand's share of all brand mentions. So I'd compare methodologies before comparing numbers across vendors. Otterly, for example, explicitly distinguishes brand coverage from SOV. Otterly

If you tell me your use case (e.g. SaaS, agency, consumer brand) and roughly how many prompts/competitors you need to analyze, I can narrow this to the 3 best platforms and compare historical data depth, AI engines, exports/API, pricing, and competitive SOV reporting.

Yes. If you mean historical AI/LLM answers—e.g., thousands of ChatGPT, Gemini, Perplexity, Claude, or Google AI answers—and you want to see how your brand's share of voice has changed versus competitors over time, there are several purpose-built platforms.

Best options

Profound is probably the strongest fit if you have a substantial historical answer dataset. Its Answer Engine Insights dataset tracks prompts daily and lets you chart SOV, rankings, visibility, citations, and positioning against competitors. You can slice the data by topic, prompt, platform, and region. help.tryprofound.com

Profound

Scrunch is particularly good for the specific question "who is gaining/losing ground?" It tracks actual AI responses and lets you trend competitive presence over time, drill from aggregate SOV → topic → individual prompt → actual answer, and filter by AI platform, persona, funnel stage, etc. It also supports historical backfill for suggested competitors. scrunch.com

Scrunch

Peec AI has a more straightforward SOV model: percentage of AI responses mentioning you versus competitors, with daily trends, average position, sentiment, and engine-level breakdowns. peec.ai

Peec AI

One important distinction

If you already own the historical AI answers (CSV/database/API data) rather than needing a platform to collect them, I'd approach this differently. You don't necessarily need an AI-search monitoring product.

You can calculate something like:

SOV for competitor X at time T = mentions of X ÷ total competitor/brand mentions in the same answer set

Then trend:

• SOV by week/month
• SOV change vs. prior period
• SOV by AI engine
• SOV by topic/category
• SOV by prompt
• average position in the answer
• co-mention rate (which competitors appear alongside you)
• citation share
• gains/losses following model updates

For example, Profound explicitly defines SOV as the brand's share of total brand mentions in the tracked AI responses, while Scrunch calculates competitive presence from actual responses. scrunch.comhelp.tryprofound.com

My shortlist: If you're evaluating software for an enterprise analytics use case, I'd demo Profound and Scrunch first, then Peec as the simpler alternative.

If you tell me what your historical data looks like (CSV, API responses, database, etc.) and roughly how many answers you have, I can recommend whether you need one of these platforms or whether it's better to build the SOV analysis yourself.

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

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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

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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.