The Real-Market AI Ranking Engine for local recommendations.
This is not a passive tracker. The real-device capture network runs city-level AI searches, turns the captures into a priority-weighted execution blueprint, and re-runs the same markets until the answer changes.
Every measurement is a record.
Each prompt we run against each engine, in each market, becomes a ranking input. Here is what becomes the execution blueprint.
Every metro you operate in, measured independently.
The exact buyer-intent question a customer would ask.
ChatGPT, Gemini, Perplexity, or the Google Maps local pack.
Were you named, recommended first, buried lower, or absent from the answer?
Who AI recommended instead, in rank order, and what reason it gave.
Full-frame capture, transcript, timestamp, and the rank stamp in the pixels.
How favorably the answer frames your brand, reputation, and proof.
The URL, profile, or public source AI appears to trust for the claim.
Wrong facts, stale services, unsupported claims, or repeated misinformation.
Revenue value, rank gap, competitor strength, reputation risk, and speed to proof.
Modeled value of the recommendations you are losing.
How each answer shifted since the last real-device capture.
Your city x prompt x engine matrix.
One grid answers the first ranking question: in this market, for this prompt, on this engine, did AI name you, rank you below competitors, or ignore you?
Named, but below three practices the engine recommends first.
The same practice falls much deeper on Gemini.
Visible, but last among the cited answer set.
The map/profile layer is part of the evidence record.
Not public in this proof row
Karma Garage Door moved from #12 to #1 in 14 days.
Not public in this proof row
Engine rationale leads with perfect rating and feedback.
Sample baseline frames this as a winner report.
The baseline shows which source signals defend the lead.
Public proof capture shows the studio first.
Map/profile facts are checked against AI rationale.
HVAC sample baseline shows the only locally verifiable answer.
Sample report includes source signals and review proof.
Not public in this proof row
Google Business Profile and local review data anchor the fix.
| Market / prompt | ChatGPT | Gemini | Perplexity | Google Maps |
|---|---|---|---|---|
Veneers dentist Los Angeles, CA public same-prompt capture set | #4 / 25 Named, but below three practices the engine recommends first. | #12 / 25 The same practice falls much deeper on Gemini. | #4 / 4 Visible, but last among the cited answer set. | Map truth The map/profile layer is part of the evidence record. |
Garage door repair Delray Beach, FL verified movement story | Held Not public in this proof row | #1 / 40 Karma Garage Door moved from #12 to #1 in 14 days. | Held Not public in this proof row | Reviews Engine rationale leads with perfect rating and feedback. |
Permanent cosmetics Middleburg Heights, OH sample baseline + public proof | Named Sample baseline frames this as a winner report. | Named The baseline shows which source signals defend the lead. | #1 / 3 Public proof capture shows the studio first. | Profile Map/profile facts are checked against AI rationale. |
HVAC repair Energy, IL sample baseline | Winner HVAC sample baseline shows the only locally verifiable answer. | Winner Sample report includes source signals and review proof. | Held Not public in this proof row | GBP Google Business Profile and local review data anchor the fix. |
AI named three cosmetic practices first, then placed the target practice below the recommended set.
Open full“veneers dentist”
AI named three cosmetic practices first, then placed the target practice below the recommended set.
Google Maps profile + visible specialist proof
Package cosmetic specialty proof, review mass, and before/after portfolio evidence so the engine can justify a higher recommendation.
Public-safe sample from the same-prompt evidence set. Client-specific prompt libraries and source maps stay gated.
Each cell opens the underlying measurement: prompt family, answer, rank position, source type, screenshot receipt, and the execution lever that moves the next run.
The baseline becomes a weighted plan.
The paid baseline is not a static report. It scores every market/category gap and turns the highest-leverage items into the first execution cycle.
The baseline is the market operating map.
It is not a static PDF. It turns real-device captures into the prioritized work that gets re-measured in the first 14-30 day proof window.
AI respects the dentistry. It does not recommend it first.
The practice is named, but below the practices AI can package as more visible cosmetic specialists.
AI already names the expert. The plan defends the lead.
The answer is strong across engines, so the baseline focuses on drift defense and reputation durability.
Markets, services, prompt families, engines, screenshots, and evidence IDs.
Who AI names first, where you appear, and what reason the engine gives.
Review summaries, complaints, stale facts, specialist proof, and trust blockers.
AEO, local SEO, GBP, citations, review, source, and content fixes ordered by likely lift.
The same market and prompt set is re-captured in the first 14-30 day window.
The answers your customers actually see.
Three AI engines plus the Google map pack: the surfaces that put a single recommendation in front of a buyer and decide who gets the call.
ChatGPT
The default AI answer for millions of buyer questions every day.
Gemini
Surfaced across Search, Android, and Workspace at consumer scale.
Perplexity
Citation-first answers that name and link the brands it trusts.
Google Maps
The map pack and local results buyers compare against every AI answer.
See your matrix, fully populated.
We run every prompt across every engine in every market you name, then hand you the grid, reputation-defense ledger, priority-weighted execution plan, and first 14-30 day proof window.