○InitializedDeepenAI Brand Perception & Semantic Diagnostics

Brand Perception Gap Benchmark

Question

Can we measure stable and actionable gaps between the brand representation the business intends and the semantic framing repeatedly produced by AI systems, and can optional open-model interpretability provide useful corroborating hypotheses?

Findings

START HERE — 1) Choose one brand with a positioning you can define unambiguously. 2) Write the Expected Brand Model before querying any AI. 3) Create 25 fixed prompts across the seven prompt families. 4) Run the exact same prompts on at least 3 models, at least 3 times each. 5) Score each answer on: correct category association, core-attribute recall, competitor differentiation, persona understanding and recommendation/decision framing. 6) Flag only gaps recurring across at least 2 models and 2 runs. 7) For each recurring gap, inspect what evidence/signals are available on the web. 8) Optionally inspect Gemma SAE/NLA-style signals for the 3–5 most important gaps. 9) Create a structured Gap Card for each validated issue.

Gap Card output must contain: entity, exposing prompt, expected framing, observed framing, models/runs where it recurred, evidence available, suspected missing/weak relation, business risk, proposed content/semantic/ontology intervention and exact validation question for the re-test.

SUCCESS THRESHOLD — Deepen only if the benchmark reveals at least 5 recurring perception gaps with >=70% behavioural stability across repeated runs, and at least 3 of those gaps can be translated into a concrete intervention hypothesis. If results are mostly random/model-specific, keep as research only and do not operationalize.

Do not interpret a single SAE feature, Neuronpedia label or open-model activation as 'the model's belief'. Open models are laboratories, not proxies for ChatGPT/Gemini/Claude. Interpretability signals are supporting evidence only and must align with repeated observable behaviour.

Data Inputs

1 brand with a clearly documented positioningExpected Brand Model defined BEFORE testing: category, 5–10 core attributes, 3–5 differentiators, target audiences/personas, key products/services, competitors and must-not-confuse-with concepts25 controlled prompts split across brand, category, product, attribute, competitor, persona and decision-intent promptsSame prompt set executed on at least 3 AI systems and repeated at least 3 timesEvidence inventory: website, structured data, key third-party sources, reviews/media/public knowledge sources when relevantOptional open-model analysis with Gemma Scope / Neuronpedia for a small subset of high-value gaps

Next Move

Run Perception Gap Intervention Test on 3–5 validated high-value gaps using the exact same benchmark prompts and scoring framework.

Value

★★★★★

Maturity

★★★★★

Decision

Deepen

Created

Aug 27, 2026

Updated

Aug 27, 2026

Tools Used

Gemma Scope

Google DeepMind interpretability resources for inspecting internal representations in Gemma models via sparse autoencoders. Optional R&D layer for testing semantic framing hypotheses; not a proxy for closed-model ground truth.

Neuronpedia

Open interpretability interface for exploring sparse-autoencoder features in open-weight models. Use only to generate hypotheses about recurring semantic associations; individual features must not be treated as definitive brand perception.