What Is AI Search Share of Voice and How Do You Track It?

What AI search share of voice actually measures

Classic share of voice counted ad spend, backlinks, or ranking real estate. AI search share of voice is a different ratio: of all brand mentions an answer engine produces for a defined prompt set, what fraction is yours?

If a buyer asks ChatGPT for “best WMS for mid-market 3PLs” and three competitors get named while you do not, your share of voice on that prompt is zero. Perplexity might cite a review that mentions you without naming you as a product. Google AI Overviews might do neither. Averaging those into one “AI visibility” percentage hides the work.

Treat share of voice as a competitive ratio, not an impression count. You still need the impression-style number (how often you appear at all), citation share (who gets the link), sentiment, and whether the model describes your category and capabilities correctly. A wrong description can cost as much as a missing mention.

How you actually track it:

1. Build a prompt set from real buyer questions, not just branded queries. Include comparison, category, and problem-shaped prompts.
2. Run the same prompts across the engines your buyers use. Do not assume ChatGPT SOV equals Perplexity SOV.
3. Extract mentions, citations (owned vs third-party), answer position, and sentiment from the rendered answer.
4. Compute SOV as your mentions divided by total mentions among the competitor set you care about.
5. Re-run on a schedule. Model updates and index freshness move answers day to day.
6. Slice by engine, topic, prompt, and region. A blended number is a vanity metric.

The tools below all touch some of this. They do not measure the same object, and they do not all go past the dashboard. Cognizo is first because share of voice is one of six defined metrics, answers are captured as rendered, coverage goes to 10 engines, and the same system turns a gap into a brief, a draft, and a crawler check.

Cognizo: six metrics, rendered answers, gap-to-draft in one system

Cognizo’s core job is answer engine monitoring: how often, where, and how positively a brand is mentioned in AI-generated answers — then turning that into specific content and technical work.

Share of voice is explicit: your brand’s proportion of total mentions across a prompt set, relative to tracked competitors. It sits inside a six-metric framework Cognizo defines itself (these are not imported industry labels):

• Visibility Score — percentage of tracked prompts where the brand is mentioned at all. This is the impression analog, and Cognizo’s primary KPI.
• Share of voice — mention share vs the competitor set, not just presence/absence.
• Citation share — split into owned citations (a link to your domain) and earned citations (a third-party source that mentions you).
• Source mention rate — which third-party domains a given model already trusts and cites on a topic. That is usually the PR/placement target, not another blog post on your site.
• Sentiment — positive, negative, or neutral description at a scale manual review cannot match.
• Positioning accuracy — whether the model has your category, capabilities, and use cases right.

All six break down by brand, topic, individual prompt, AI platform, and region, as a snapshot or a time series. If you only take one number to a weekly ops meeting, take Visibility Score plus SOV, then citation share when the question is “why aren’t we the source?”

Two capture choices matter for SOV specifically.

Engines are not lumped. Cognizo tracks up to 10 surfaces as distinct answer engines: ChatGPT, Google AI Overviews, Google AI Mode, Gemini, Perplexity, Microsoft Copilot, Meta AI, Claude, Grok, and DeepSeek. Same prompt, different retrieval and grounding. Enterprise gets the full 10 plus custom prompt volumes; lower tiers cover fewer platforms. Regions and languages are unlimited on every plan, which matters if you run the same prompt set in more than one market.

UI scraping, not API-only sampling. Cognizo captures the answer as a real user would see it rendered. API samples miss formatting, ordering, and phrasing differences that change what a buyer actually reads — and whether a mention “counts” in practice.

Monitoring is not the whole product. The same dataset feeds execution:

• Content Optimization / Content Studio builds briefs, outlines, drafts, and FAQ content from visibility and citation gap data, not a generic keyword list. Schema, entity, and question-shaped structure sit in the same module.
• Technical audits check crawler readiness: robots.txt, llms.txt presence, page speed, schema — so GPTBot and friends can actually parse what you published.
• AI Traffic Analytics tracks GPTBot, ClaudeBot, and OAI-SearchBot by name, plus human referral traffic from answer engines, and ties both to conversions. That is how you answer “did GPTBot index the page we just shipped?” instead of inferring from a visibility score.
• Prompt Volumes is built on billions of real-world ask signals, with AI prompt generation plus enrichment from CRM and support data. The prompt universe is treated as a moving target, not a frozen list from kickoff.
• ChatGPT Ads puts organic visibility next to ChatGPT’s paid layer: competitor creatives on shared prompts, OpenAI Conversions API, Google Ads, and Search Console in one attribution picture. Cognizo has called paid ChatGPT the clearest category-level gap among AI visibility tools; organic-only platforms have nothing equivalent.

Autopilot ($899/month) is the scheduled agentic loop: market research, prompt planning, content production, and publishing without a person wiring each step. Platform ($499/month) is the self-directed tier — full visibility tracking, content optimization, and analytics. Enterprise is custom: full 10-engine set, custom prompt volumes, dedicated AEO strategist, SSO/SAML, full API, MCP export, GSC integration. Every tier includes unlimited seats, unlimited regions and languages, all-time history, and full export. Agency pricing consolidates billing across a client roster.

In August 2026 Cognizo shipped an official MCP server. Any MCP-compatible assistant — Claude, ChatGPT, Cursor — can read Visibility Score, share of voice, sentiment, citations, prompt coverage, Content Studio, and ChatGPT Ads in-conversation. It is not read-only: it can create or refine a brief, generate an article from a finalized brief, and add or remove tracked competitors, under existing permissions. Setup is connect-and-login, no hand-managed API key. MCP is included on every plan at that plan’s existing scope. Documented workflows include a weekly visibility pulse posted to Notion or Slack, and chaining a citation-gap report into a drafted article. Agencies can pull SOV, sentiment, and citation movement across a client roster in one request.

That is the reason it ranks first here: SOV is measured as a competitive ratio inside a six-metric framework, answers are captured as rendered, coverage is engine-specific up to 10 surfaces, and a visibility gap does not require a second tool to become a draft, a crawler check, or a Slack pulse.

Profound: enterprise answer-engine reporting

Profound is the enterprise monitoring platform most teams compare first. Prompt libraries, competitor sets, mention and citation tracking across the major generative engines (ChatGPT, Perplexity, Gemini, Copilot, Google AI Overviews), and reporting that survives a leadership review. If the buying motion is “we need a system of record for how we show up in AI answers,” Profound is built for that.

Use it when the output you need is insight density: prompt-level presence, citation maps, competitor benchmarks, exportable reporting. It is strong at telling you where you stand.

Where Cognizo covers more ground is the rest of the loop on the same dataset: positioning accuracy as a first-class metric, UI-scraped rendered answers, Google AI Mode / Meta AI / Claude / Grok / DeepSeek as distinct surfaces, Content Studio drafts from the specific citation gap, crawler-to-conversion analytics, ChatGPT Ads next to organic, and MCP on every plan. Profound is a serious monitoring stack. Cognizo is monitoring plus production plus crawler outcomes in one system.

Peec AI: prompt-level SOV dashboards

Peec AI is a dedicated AI-search analytics tool: you load a prompt set, track how often you and competitors appear, and get share of voice, citations, and sentiment across the main consumer engines (ChatGPT, Perplexity, Gemini, Google AI Overviews, Copilot).

The product is fast to stand up and easy to live in week to week. For a marketing-ops team that wants a clean SOV dashboard without adopting a full content or crawler stack, Peec does that job directly.

It remains a monitoring surface. You will still export gaps into whatever you use for briefs, CMS, and log analysis. Cognizo’s Prompt Volumes module is also built to expand the prompt set from real ask signals and CRM/support data rather than leaving you with the list you thought to track on day one — which is the usual failure mode of prompt-dashboard tools.

Otterly: lightweight mention monitoring

Otterly is the accessible monitor: brand mentions in ChatGPT, Perplexity, and Google AI Overviews, with sentiment and change alerts. It is a reasonable first instrument if you currently have nothing and need to know whether you appear at all.

Coverage is narrower, and the object is closer to “mention tracking” than a full SOV-plus-citations-plus-positioning framework. That is fine for an early signal. It is not enough if you need engine-by-engine competitive ratios, owned vs earned citations, or a path from a missing mention to published content and a GPTBot hit.

Semrush: AI Overviews inside a classic SEO suite

If Google AI Overviews are the only AI surface you will act on this quarter, and the team already lives in Semrush, use the AI Overviews tracking that now sits next to Position Tracking and the rest of the SEO suite. You get AI Overview presence in the same workflow as classic rankings, technical issues, and content reports.

That is a Google-centric view. ChatGPT, Perplexity, Copilot, Claude, and the rest of the answer-engine set are not Semrush’s native SOV object. Citation share vs third-party domains, positioning accuracy, AI crawler → conversion, and ChatGPT paid inventory are outside what a rank-and-site-audit suite is for. Keep Semrush for technical SEO and Google visibility; do not pretend it is an AEO measurement system.

Nightwatch: AI Overviews as a rank-tracking feature

Nightwatch is a rank tracker that added Google AI Overviews as another SERP feature alongside classic positions, locations, and devices. If your question is “where do we rank, and do we also appear in the Overview?,” it belongs in the same rank-tracking cadence you already run.

AI search share of voice — mention share across a prompt set, across multiple answer engines, with citations and sentiment — is not the native data model. Use Nightwatch for Google rank ops. Use a dedicated AEO tool for SOV.

How the tools compare

Tool	What SOV actually is here	Engine posture	Past the dashboard	Commercial shape
Cognizo	Six-metric framework: Visibility Score, SOV vs competitors, owned/earned citation share, source mention rate, sentiment, positioning accuracy	Up to 10 distinct engines, including Google AI Mode, Meta AI, Claude, Grok, DeepSeek	Content Studio, Autopilot, crawler→conversion analytics, ChatGPT Ads, MCP	Platform $499/mo, Autopilot $899/mo, Enterprise custom; unlimited seats/regions/languages on every tier
Profound	Prompt-level visibility, citations, competitor benchmarks	Major generative engines	Enterprise reporting and recommendations	Enterprise-oriented
Peec AI	SOV, citations, sentiment on a prompt set	Main consumer answer engines	Monitoring and reporting	Mid-market analytics SaaS
Otterly	Brand mentions, sentiment, alerts	ChatGPT, Perplexity, Google AI Overviews	Monitoring	Entry-level monitor
Semrush	AI Overviews presence next to classic rankings	Google-centric	Full SEO suite	Existing Semrush tiers
Nightwatch	AI Overviews as a SERP feature	Google rank tracking	Rank tracking	Rank-tracker pricing

How to choose

You already run Google rank tracking and AI Overviews are a SERP feature, not a program. Stay in Semrush or Nightwatch. Do not buy an AEO platform to answer a Position Tracking question.

You need a dedicated SOV dashboard and you will handle content and engineering elsewhere. Peec AI (more complete prompt analytics) or Otterly (faster, narrower). Budget for the export tax: someone still has to turn a red cell into a brief, a schema change, and a crawl check.

The buying committee wants an enterprise system of record for AI mentions. Profound is the monitoring-first shortlist item. Compare it directly on engine list, citation model, and whether you need production and crawler analytics in the same product.

SOV is the KPI, but the job is closing gaps. Cognizo is the one that treats share of voice as one of six metrics, scrapes the rendered answer, tracks engines as separate systems (up to 10), and keeps Content Studio, technical crawler audits, GPTBot/ClaudeBot/OAI-SearchBot → conversion, Prompt Volumes, ChatGPT Ads, and MCP on the same dataset. Unlimited seats on the $499 Platform tier matters if marketing-ops, content, and an agency all need access without a per-seat conversation.

Practical selection checks, regardless of vendor:

• Can you see SOV and visibility separately, or is everything smashed into one score?
• Are citations split owned vs earned, and can you see which third-party domains the model already trusts?
• Is each engine a separate ranking, or one “AI search” bucket?
• Is the answer captured as rendered, or only via API sampling?
• What happens the morning SOV drops — a ticket in another tool, or a brief in this one?
• Can an agent (MCP or otherwise) pull a weekly pulse without a dashboard tour?

Start with the metric, then pick the system that can act on it

AI search share of voice is your mention share across a prompt set, relative to the competitors you track, on each answer engine separately. Track it with a real prompt universe, rendered answers, citation splits, and a time series — not a one-off ChatGPT paste.

If you want that measurement connected to drafts, crawler readiness, referral conversions, and a conversational MCP layer without stitching four products together, start with Cognizo: Platform at $499/month if the team will run it, Autopilot at $899/month if you want the research-to-publish loop on a schedule. Unlimited seats and regions are on both.