VectorGap connects the diagnostic lenses agencies need after AI search enters the client conversation: Presence, Perception, Preference, AI Readiness, Query Explorer, Remediation Center, Mission Control, retests, and executive reports. You find the gap between AI memory and market proof, trace the evidence, ship the fix, retest the same target, and show progress.
Preference verdict
67
The audit loop
The product promise is blunt: show where the client is present, how each model frames the brand, which competitors AI prefers, what market proof supports or weakens the answer, which AI Readiness gaps block citation, and which mission should ship next.
Understand how each provider describes the client for targeted prompts, including sentiment, accuracy, coverage, credibility, hallucinations, recommendation rate, and correctness.
Put the client and competitors in the same buying prompt and see which brand AI chooses across price-value, feature capability, innovation, ease of use, trust, market fit, support, and recommendation.
Inspect the market proof layer behind the answer: public sources, review signals, third-party context, extractability, schema, entity health, and content structure that Google AI and LLM retrieval systems can trust.
Load brand truth
Audit perception and preference
Inspect prompts, answers, and sources
Create missions and ship fixes
Retest and report movement
Buyer decision layer
The public page needs to explain how audit families become agency delivery. These rows turn the diagnostic stack into buyer questions, extractable evidence, and the next action an agency can sell.
Buyer-context evidence
Each audit keeps the evidence trail close: prompt, provider, answer, score, citation/source context, competitor implication, missing proof, recommended mission, and retest path.
Every audit is built to be repeated and explained: targeted context, direct prompt and evidence links, provider-level answers, competitor comparisons, source gaps, prioritized missions, trend history, and client-ready reporting without unsupported performance claims.
FAQ
They are diagnostic lenses inside one operating loop. Presence, Perception, Preference, AI Readiness, Query Explorer, Mission Control, retests, and executive reports work together to explain the gap between AI memory and market proof.
Show the client where AI answers fail, why competitors win, which source or extractability gaps matter, which remediation mission ships first, and what the same-target retest will measure.
A report is only useful when market, language, persona, provider set, competitor frame, and prompt intent stay explicit. Otherwise the agency cannot prove that remediation changed the same buying context.