Provider variance
Differences between providers reveal where the brand has fragile proof, unclear entities, or source gaps.
Methodology
A serious AI visibility workflow needs a reproducible methodology.
VectorGap gives agencies a structured way to audit how AI systems describe, cite, compare, and recommend client brands, then repeat the same targets after remediation work.
What the buyer can verify
ChatGPT, Claude, Gemini, Perplexity, Grok, and Mistral in the standard audit panel.
Market, language, industry, persona, and competitor context preserved for every audit.
Prompt-level answer evidence with sources, hallucination checks, and missing-proof gaps.
Remediation missions, retests, and client-ready exports for recurring agency work.
6
standard providers
98
LLM prompts
40
AI Preference prompts
Provider panel
Six standard providers, interpreted as separate evidence streams
VectorGap compares provider behaviour instead of pretending one answer represents the market. ChatGPT, Claude, Gemini, Perplexity, Grok, and Mistral each have different answer, retrieval, and citation patterns.
Market and language context
Audits preserve country, language, industry, persona, and competitor context so results can support targeted agency work.
Prompt-level evidence
Scores stay tied to prompts, answers, source context, and category labels so teams can inspect the reason behind the number.
Scoring
LLM Perception and AI Preference answer different questions
LLM Perception checks how AI describes the client. AI Preference checks whether AI would choose the client against competitors. Agencies need both to understand visibility and commercial recommendation risk.
LLM Perception
Measures coverage, sentiment, accuracy, hallucination risk, source quality, and how answers change by provider and target context.
AI Preference
Uses direct competitive matchups across categories such as price/value, capabilities, innovation, ease of use, trust, market fit, support, and overall recommendation.
Score construction
Preference scoring weighs category strength, direct wins, rank strength, evidence support, and provider consistency so a score reflects decision quality, not raw mentions.
Retests
Progress only matters when the same target is repeated
Retests compare the same brand, market, language, persona, provider set, and competitor context after remediation work. That makes reports defensible and helps agencies show whether shipped work changed AI answers.
Before state
Capture the weak answer, provider, source gap, competitor implication, and mission before work starts.
Remediation evidence
Link actions such as proof blocks, schema, entity facts, and source updates to the specific audit gaps they target.
After state
Rerun the same audit target and compare recommendation rate, rank, evidence strength, hallucination reduction, and provider consistency.
FAQ
Questions agencies ask before using VectorGap
Does VectorGap claim perfect ground truth across AI systems?
No. AI answers are probabilistic and provider behaviour changes. VectorGap makes audits inspectable, repeatable, and evidence-led so agencies can reason about movement instead of relying on a single opaque score.
Why include AI Preference?
Mention visibility is not the same as being chosen. AI Preference shows whether a provider recommends the client against named alternatives and which proof gaps affect that recommendation.