GenSig measures how visible your brand is when AI assistants answer your customers' questions — and gives you the evidence, the sources and the plan to improve it.
When someone asks ChatGPT "what's the best specialty coffee subscription?", the answer either names your brand or it doesn't. GenSig asks the live, web-grounded AI surfaces — ChatGPT with real web search and Gemini with Google Search grounding — the questions your category gets asked, localized to your target market: a frozen, versioned set of up to 12 brand-neutral questions. It measures:
Your questions are generated once, reviewed by you, then frozen and versioned. Every future run asks exactly the same questions — otherwise the trend line would compare apples to oranges. Editing questions or competitors bumps the version, and the chart warns you when a period mixes versions.
Every question runs on the channels that mirror what real users see: once on ChatGPT with live web search and twice on Gemini with Google Search grounding, geolocalized to your target market. We published the experiment behind this design — 2,500 measured answers showing that bare LLM APIs measure the model's memory (they miss recent brands and inflate stale ones), while each search-grounded channel closely mirrors its own consumer product.
LLMs are not deterministic: the same question can get different answers. Each analysis asks every question three times across the two surfaces. Mention stability is the percentage of questions where all runs agreed (all mentioned you, or none did). Low stability means the surface is genuinely undecided about your brand — that is a finding, not noise to hide.
A single visibility percentage hides where you actually lose. Each question in your set is classified by intent — discovery ("what tools exist for…"), comparison ("X vs Y", best-of lists), purchase ("which should I use for…"), problem ("how do I solve…") and reputation ("is this trustworthy") — and visibility is broken down accordingly. Being strong in comparison questions but invisible in purchase questions is a very different problem from the reverse, and it calls for different work.
Every visibility number ships with a shaded band: the range where the true rate most likely falls (a 95% Wilson interval), plus a plain-language precision label. And Change vs last run stacks the previous band over the current one — if they don't overlap, the change beats sampling noise and is marked significant; if they overlap, we say so instead of celebrating noise.
Each archived run is compared against the previous run of the same brand and the same question-set version. That is the number behind the +/- badges.
Every recommendation in the action plan is derived from counts measured in that run, not from a model's opinion: which domains informed which questions, in how many of those answers your brand was weak or absent, and which competitors those same answers recommend instead. The numbers are computed from the archived answers — the language model only phrases the next step from evidence it cannot invent. A recommendation that would apply to any website is filtered out by design.
What GenSig deliberately does not claim: causal verdicts. Visibility moves for many reasons — provider updates, source churn, your competitors' moves — not just your actions. That is why actions are tracked as Annotations on the timeline — you see the correlation with your own eyes instead of getting a fabricated "your action caused +12%".
| View | What it shows |
|---|---|
| Dashboard | The active brand's latest numbers, the visibility trend with annotation markers, and the full run history (paginated). Run new analyses from here. |
| Prompts | The frozen question set: version, language, per-question source, draft editing with explicit Save. Changing it bumps the version. |
| Competitors | Side-by-side visibility, sentiment and position for every tracked brand, plus the leader autopsy: the top competitor's site examined with the same lens as yours. |
| Perception | What the AI actually says: verbatim quotes grouped into praise, criticism and neutral mentions, per brand. |
| Sources | The domains AI cites when answering your category questions, classified (editorial, UGC, competitor, you…) with real example URLs — plus source opportunities: sites that inform several of your questions while the answers rarely mention you. |
| Action plan | Prioritized tasks with the measured evidence behind each one — the source and intent gaps where you lose the conversation, the questions affected, who wins them instead, and the concrete next step. |
| Optimize | Generated JSON-LD structured data and meta descriptions built from your frozen questions and your site's content. Copy, paste, ship. |
| Annotations | Every GEO action you take, pinned to the timeline and overlaid on the dashboard chart as colored markers. |
| Activity | The workspace audit log: who changed what, when — including restorable configuration snapshots. |
| User management | Invite teammates with granular per-brand permissions. |
Pro plans can schedule automatic re-runs (weekly, biweekly, monthly) per brand. Monitors run in the cloud — your computer does not need to be on. Activating a monitor triggers its first run immediately; results appear in the dashboard and you get a live notification when a run starts and finishes.
Teams are available on the Agency plan (up to 10 members). Viewers can be scoped to specific brands — ideal for giving each client read-only access to their own numbers.
| Role | Can do |
|---|---|
| Owner | Everything, including deleting brands and runs, restoring configurations and managing members. |
| Editor | Run analyses and edit configuration on all brands or a specific list; optionally add new brands. Editable brands are always visible. |
| Viewer | Read-only access to all brands or a specific list. |
Most tools stop at monitoring — mentions, rankings and charts. GenSig also explains why competitors are winning, which sources influence the answers, and what to publish, then turns that into a prioritized, implementation-ready action plan. It's an AI visibility monitor and GEO consultant in one: every recommendation is tied to measured evidence from real AI answers (no black-box scores), and it focuses on the assistants buyers actually use rather than charging for a long list of marginal model integrations.
AI visibility is how often an AI assistant names your brand when someone asks it a buying question in your category — for example "what's the best project management tool?". Unlike search rankings, the assistant returns a short recommended list, not ten links, so being named (or not) directly shapes which brands a buyer considers.
Generative engine optimization (GEO) is the practice of getting your brand recommended by AI assistants like ChatGPT and Gemini when they answer questions in your category. It works differently from SEO: instead of ranking a page, you influence the sources and signals the assistant draws on when it composes an answer.
You ask ChatGPT the real category questions buyers ask — with web search on, in the target market — repeatedly, and count how often the brand appears versus its competitors, with confidence intervals. GenSig runs each question on ChatGPT with live web search and on Gemini with Google Search grounding, measured separately because the two surfaces genuinely disagree.
The right tool measures the live, web-grounded surfaces real users see (not a bare model API), reports uncertainty honestly with confidence intervals, and shows the evidence behind every number. GenSig does this and publishes its methodology and raw data so results are reproducible — a check most tools in this category can't offer because their data comes from scraping. Evaluate any tool on which surfaces it measures, whether it shows the underlying answers, and whether it can prove its numbers.
SEO measures where your page ranks in a list of links; AI visibility measures whether your brand is named inside the answer itself. An assistant names a handful of brands and there is no page two, so the goal shifts from ranking a URL to being one of the few brands the AI recommends — and to appearing in the third-party sources it cites.
Because a bare model API answers from memory, not from what users actually see. In our published study the bare API correlated only ρ=0.23 with real ChatGPT web sessions — it missed recent brands and inflated stale ones. GenSig always measures the search-augmented surfaces, which mirror the consumer product.
The two live, web-grounded surfaces: ChatGPT with real web search and Gemini with Google Search grounding, measured and reported separately. GenSig deliberately does not measure bare LLM APIs, and adds new surfaces only once it can validate them the way it validated ChatGPT and Gemini.
Every report includes a prioritized action plan where the evidence is computed from the run — which sources informed which questions, where your brand is weak, and which competitors win those answers. The recommended next step is generated only from that evidence, and generic advice is filtered out by design.
Weekly to monthly for most brands. AI answers shift as providers update models and as sources change, so a single measurement is a snapshot; trends over repeated runs are far more meaningful. GenSig can re-run the same frozen study automatically on a schedule.
From the counts of the run itself: the questions where your brand is weak or absent, the domains that informed those answers, and the competitors those answers recommend. Each task shows that evidence next to the suggested action, so you can check the reasoning instead of trusting it.
AI surfaces are probabilistic and providers update their models, so answers vary between runs. That is why GenSig reports confidence bands and measures trends over single points, and marks a change as distinguishable only when the intervals don't overlap.
GenSig is $49/month for Pro (3 brands, 40 analyses) and $149/month for Agency (15 brands, 150 analyses, team of 10); one-time packs of extra analyses are available and never expire. Every plan includes the full live study, sources, action plan and monitoring.
Yes. Deleting a brand permanently removes its configuration, archived reports and monitors; full account deletion — data, subscription and sign-in — is self-service under Settings → Delete account and takes effect immediately.
No — every scheduled monitor run counts as one analysis within your plan's allowance, exactly like a manual run.