Real devices. Real markets. Real ranking loops.
One-off prompts and synthetic checks cannot tell you what to fix. The AI ranking engine runs the market from physical devices, turns the answer into a priority-weighted execution blueprint, then re-runs the same prompts to prove what moved.
Map -> Weight -> Execute -> Re-measure -> Prove
The AI ranking engine is the mechanism: it maps how AI ranks each market and category, weights the missing trust and reputation signals by business impact, executes the plan, re-measures in the first 14-30 day proof window, and proves the climb with device screenshots.
Map -> Weight -> Execute -> Re-measure -> Prove
The baseline is the operating map.
We do not publish the full playbook, but buyers should know how decisions are made. The AI Ranking Program starts by ordering work against the factors most likely to move revenue and prove movement quickly.
Services or products where one recovered buyer materially changes ROI.
Cities, suburbs, and locations where the brand wants to win first.
How far the business is from the top answer set by engine and prompt family.
The sources, reviews, citations, and proof assets AI uses to justify the current winner.
Review summaries, complaints, false claims, stale facts, and hallucinations blocking trust.
The fixes most likely to create a clean 14-30 day re-measurement signal.
What each method can prove.
The question that matters isn't which tool is cheapest — it's whether the reading can be defended after the fact. Only one of these methods produces an artifact.
Queries the model API directly — no real app, no location, no rendered answer. Whatever it reports, there is nothing to show a stakeholder.
Scripts a server-side browser — closer, but it is a bot session from a datacenter, stripped of the local, on-device context your customer actually has.
Metro-local real-device sessions. Every reading ships as a timestamped screenshot with the rank stamped in the pixels of the file itself — verifiable by anyone, after the fact.
And the answers themselves are unstable across engines: in the current real-device rank-tracking evidence library, the three engines named a different #1 in 90.7% of 2,328 side-by-side comparisons - measured on real devices. A single synthetic check cannot see any of that.
We publish the evidence standard, not the operating recipe.
Public pages show the capture artifact, verification gate, aggregate proof, and methodology boundary. Gated reports show the register and buyer-specific baseline. Exact prompt sets, raw evidence folders, source maps, and operating mechanics stay client-side or NDA-only.
Eleven signals, every single run.
Each measurement is fully instrumented, so every claim in your report traces back to a captured artifact.
Audit-grade, not anecdotal.
When you take a finding to a franchisee, a board, or a platform, it needs to survive the question ‘how do you know?’ Every measurement answers it four ways.
Screenshot
The full-frame answer exactly as a customer saw it on the device.
Transcript
Verbatim answer text — searchable, quotable, diffable run over run.
Timestamp
When the answer was captured, to the second, in the market’s timezone.
Metadata
Engine, prompt, location context, and device class behind every capture.
Screenshot + transcript + timestamp + metadata = a record that holds up under scrutiny.
Location-controlled, by design.
We measure with metro-local real-device sessions under location-controlled measurement. Our language is deliberate and compliant: we describe the market context an answer was captured in, never the spoofing of any signal.
Ranking proof you can put in front of anyone.
A real-device ranking baseline across your markets and categories — every engine, every city, screenshot-backed, timestamped, and converted into the first priority-weighted execution plan.