The GEOFixer coverage matrix is a persona-by-LLM grid. Every cell is a real visibility score — the percent of audit conversations where your brand was the top recommendation for that buyer persona on that model. Green, yellow, and red cells tell you in one glance which buyers are invisible to which engines. Click any cell to drill into the actual conversations.
Every model the panel queries is a column. Every buyer persona on your brand profile is a row. The cell is the visibility score for that persona on that model.
The score column you actually care about depends on where your buyer asks. The matrix lets you see all of them at once instead of guessing which one is most representative.
| Persona | gpt-4o-mini | gpt-5 | gemini-flash | haiku | sonar-pro | deepseek | mistral | google AO |
|---|---|---|---|---|---|---|---|---|
| Marketing manager | 82 | 78 | 61 | 54 | 71 | 48 | 52 | 22 |
| Agency lead | 76 | 65 | 58 | 42 | 73 | 31 | 44 | 18 |
| Founder, B2B SaaS | 68 | 62 | 55 | 36 | 60 | 28 | 41 | · |
| Brand defender | 79 | 74 | 63 | 51 | 70 | 45 | 53 | 19 |
| PR / comms | 59 | 55 | 47 | 31 | 48 | 22 | 35 | · |
This sample shows what a typical mid-stage B2B SaaS coverage matrix looks like after two weeks of audit data. The two big stories: Google AI Overviews is the biggest gap (column average 19), and the PR / comms persona is the weakest row (across-the-board mid-to-low). Those are two specific, actionable problems, not a single vague "improve AI visibility" todo.
A single composite GEO score is useful as a headline. It is useless for diagnosing where to act. The matrix shifts the question from "is my AI visibility good?" to two more useful questions:
Most "AI visibility" tools report a single score. The serious work happens in the matrix.
Each cell is a click-through. Pick a cell and you see the actual conversations: which prompts the panel ran for that persona, what each model answered, where your brand appeared (or did not), and which competitor the model recommended instead.
That is the part that turns the matrix from a dashboard into a tool. You see the recommendation in context. You see exactly what the model would tell a real buyer asking that real question. You see the missing argument in your evidence base. Then GEOFixer's Autopilot writes the content to close that specific gap.
The data is real. There is no estimation, no extrapolation, no "modeled visibility." Every cell is a count of actual model API calls.
promoter_conversations with the exact model_version served (we capture claude-haiku-4-5-20251001, gpt-4o-mini, sonar-pro etc, not just panel slot names). Visibility = wins / total over a 28-day rolling window.Per-cell drill-in shows you the raw turns — not a summary, the actual conversation. You can copy them into a brief, paste them into a Slack thread, send them to a writer.
Inside the product, you will find the coverage matrix in two places:
/dashboard/geofixer — your own brand. The matrix sits below the per-LLM trend chart./clients/<client-id>/geo — for agency users, the same matrix scoped to a specific client.Both surfaces also have a "By model" tab that breaks down visibility by exact model_version so you can compare Sonnet vs Opus vs Haiku within Claude lineage, gpt-4o vs gpt-4o-mini vs gpt-5 within OpenAI lineage. That is where you spot model upgrades and degradations as the providers ship them.
Start a 14-day trial. The matrix populates within the first audit cycle.
Start free trial →