Inside your brand kit · GEO Analysis · Ask · Measure · Be the Answer
When shoppers ask AI what to buy — is your client the answer?
The GEO audit agencies bill for, built into the brand kit. Fifty real shopper questions asked live across ChatGPT, Perplexity, Gemini and Claude — mention rates, share of voice against the competitors your client names, reputation signals with receipts, a white-label report, and the corrective llms.txt written against the exact questions they're losing. Re-run monthly: the deltas are the evidence.
Ask · Measure
Fifty real questions, four AI engines — one answer shelf
When shoppers ask AI what to buy, somebody gets recommended. The pulse asks the questions your client's buyers actually type — live, across all four engines — and measures whether your client is the answer, and who is when they're not.
The question pack · 50
you edit every one · locked for comparison
Four engines
200 live answers per pulse
The AI shelf
Questions you approve
Drafted from the brand kit or uploaded from the sheet your media buyer signed off — every question is reviewed before a single answer is bought.
Asked the way buyers ask
Live web search on, locale pinned — the same answering mode shoppers see, not a lab shortcut.
Evidence, always
Two hundred verbatim answers with every cited source — so the score is backed by receipts, not a black-box number.
The audit, end to end
From the question pack your client approves to the report they keep — every step with evidence attached.
The question pack
Fifty questions you control
Real shopper questions across five lanes — buying-intent, brand, comparisons, use cases, reputation. Draft them with AI from the brand kit, upload the spreadsheet your media buyer approved, or type them in — you review every question before anything runs.
Asked live
The four engines, as buyers see them
Every question is asked with live web search on — the mode shoppers actually use — across ChatGPT, Perplexity, Gemini and Claude, pinned to your market's locale. Two hundred answers per pulse, captured verbatim with every source they cited.
The shelf
Share of voice you define
The competitor set is yours — the brands your client actually cares about, never picked for you. On every unbranded question, the pulse counts who the engines put on the shelf and ranks the whole set, your client highlighted.
Reputation
What shoppers hear at the moment of doubt
“Is it legit?” “Common complaints?” “Worth the money?” The reputation lane captures how the engines answer purchase-doubt questions — including the complaint themes they repeat — so the narrative gets managed instead of discovered.
The evidence
Every answer, verbatim
Browse all two hundred answers by lane — what each engine said, word for word, which brands it named, and exactly who it cited. The cited-domain rollup becomes the map of where AI recommendations are earned.
The deliverable
A client report built to bill
White-labeled with the brand kit's logo: executive verdict, four-engine scoreboard, share of voice, verbatim exhibits, reputation chapter, and a full every-question appendix. Prints to PDF with an agency-ready filename — and the recommended-actions page detaches for findings-only delivery.
The corrective action
The analysis doesn't stop at a score — it writes the fix
Trackers hand you a number. The pulse hands your client a corrected llms.txt — the machine-readable brand file AI engines read — authored from the live website and the client's existing file, aimed at the exact questions they're losing, with every change documented.
The gap map
from the last pulse — automatically
The corrected llms.txt
Ready to deployImproves — never replaces
If the client's site already has an llms.txt, it's fetched and treated as the incumbent: its true content carries forward with the new layers woven around it.
Written against the gaps
The lost questions from the pulse steer what the file covers — the client's real answers, placed exactly where the engines look.
Receipts, not a diff
Every corrective action is listed — what was preserved, what was added, what was fixed — so the value is visible without comparing files line by line.
Prove it moved
Same fifty questions next month — the deltas are the evidence
The pack is locked, so every re-run is a fair comparison. Deploy the corrections, run the pulse again, and the report shows the movement — the number the retainer bills against.
This pulse vs the last
same locked packmovement reported at pack and lane level — where it's statistically honest
Run the whole loop from Claude
Draft the pack, run the pulse, read the gaps, generate the report and the corrected llms.txt — the same audit, driven from a conversation.
Run the audit
“Run an AI visibility pulse for Acme — how do the AI engines recommend us?”
Read the results
“Where is Acme losing on the AI shelf — which questions, on which engines?”
Deliver the fix
“Write Acme's corrected llms.txt against the gaps and give me the client report link.”
Ask an engine the same question twice and the wording can change. That's why movement is only ever reported at pack and lane level, where the aggregate is stable — and why single answers are shown as evidence, never tracked as metrics.
Month-over-month comparison only runs against the same locked question set — editing the pack starts a new version and a new baseline. That's the fence that keeps the comparison honest.
Gemini answers get mention tracking like the rest, but it doesn't disclose which sites it read — so its “cites your site” column stays honest and empty rather than guessed.
It's written only from what the materials support — the live site, the existing file, the brand kit — and every corrective action is listed. You review it before it ships to the client's site; nothing deploys itself.