○InitializedDeepenAI Brand Perception & Semantic Diagnostics

Perception Gap Intervention Test

Question

If we correct a small number of validated perception gaps through targeted content, entity, structured-data, evidence or Knowledge Graph changes, does the AI framing measurably improve on the exact same prompts?

Findings

START HERE — 1) Select 3–5 perception gaps with clear business importance and repeated baseline evidence. 2) Assign ONE primary intervention hypothesis to each gap and document exactly what will change. 3) Freeze the original prompts, scoring rubric and baseline outputs. 4) Implement the intervention. 5) Confirm the changed evidence is actually available to the tested system (indexed/retrievable for public models, injected into the controlled KG/RAG context for internal tests). 6) Re-run the exact same benchmark. 7) Measure targeted association improvement and unintended regressions. 8) Update each Gap Card with before/after evidence and decision.

SUCCESS THRESHOLD — Consider the intervention promising if the targeted perception score improves by >=15 percentage points across at least 2 external models without a >10 point regression on another core dimension. In a controlled KG/RAG environment, target >=20 points because context exposure is known. Repeat once before calling the result stable.

DECISION — If >=2 of the 3–5 interventions pass the threshold, deepen toward a repeatable Brand Perception Audit + remediation methodology. If only controlled-agent results improve, keep the method for agent/KG optimisation but do not claim external GEO impact. If no stable shift is observed, document the gap as diagnostic-only and do not industrialize the intervention.

Public-model before/after tests are quasi-experimental, not clean causal proof: model updates, index changes and unrelated web signals can alter outputs. Treat external shifts as evidence of association unless a controlled environment isolates the changed context.

Data Inputs

3–5 validated Gap Cards from Brand Perception Gap BenchmarkFrozen baseline results: same prompts, same scoring dimensions, same target modelsOne primary intervention hypothesis per gap: content clarification, stronger entity separation, structured data, claim-to-evidence links, Knowledge Graph relation, third-party evidence or a controlled combinationBefore/after evidence snapshotExact validation question defined before intervention

Next Move

If successful, define a standardized AI Brand Perception Audit deliverable and test it on a second brand from a different category.

Linked Simulations

○InitializedBrand Knowledge Graph POCKnowledge Graphs & Semantic Representation

○InitializedBrand Perception Gap BenchmarkAI Brand Perception & Semantic Diagnostics

○InitializedWebsite → Ontology AuditKnowledge Graphs & Semantic Representation

Value

★★★★★

Maturity

★★★★★

Decision

Deepen

Created

Aug 27, 2026

Updated

Aug 27, 2026