How do we run a monthly “AI mock interview” (buyer-style Q&A) to test our GEO coverage and trust across ChatGPT, Gemini, Deepseek, and Perplexity?

Why this test matters (Awareness → Interest)

• Buyer behavior shift: buyers ask AI directly (e.g., “Which supplier can solve this technical requirement?”) instead of searching keywords.
• GEO definition (ABKE): GEO is an infrastructure that helps an enterprise be understood, trusted, and prioritized by AI systems through structured knowledge, evidence, and semantic linkage.
• Mock interview principle: use the buyer’s decision questions as the unit test for your “AI-readable digital expert persona”.

Monthly SOP (Evaluation): same prompts, multiple AIs, measurable scoring

1. Build a “Buyer Question List” (Prompt List)
   Source questions from: RFQs, email threads, sales call notes, technical clarification logs, and objection handling documents. Store as a versioned list (e.g., PromptPack_v2026-03) so results are comparable month-to-month.
2. Run the same questions on multiple models
   Minimum set (as referenced by ABKE): ChatGPT, Gemini, Deepseek, Perplexity. Use identical language, context, and constraints to reduce noise.
3. Capture outputs with evidence fields
   Record: date/time, model name/version (if shown), full prompt, full answer, cited sources/links (if provided), and screenshots or exported transcripts.
4. Score each answer using 3 hard criteria (0–2 per criterion)

   Criterion	0 points	1 point	2 points
   A. Presence (Are you mentioned?)	Not mentioned	Mentioned inconsistently / buried	Mentioned clearly in recommended shortlist
   B. Accuracy (Is the description correct?)	Key facts wrong / confusing identity	Partially correct, missing key scope	Correct scope + correct positioning
   C. Verifiability & Touchpoints (Can a buyer validate and contact?)	No sources, no proof, no next step	Generic sources or weak/unclear next step	Points to verifiable pages/docs + usable contact path

   Sum score per question per model: 0–6. Track trends over time to quantify GEO coverage and trust improvement.

What questions should be in the prompt list? (Interest → Evaluation)

Include questions that mirror B2B procurement decision logic. Examples (template-style):

• Supplier reliability: “Which companies provide [solution/category] and how can I verify their delivery capability?”
• Technical fit: “Which supplier can solve [specific technical requirement] and what evidence should I ask for?”
• Risk & compliance: “How do I validate [standard/certification/documentation] for [industry]?”
• Comparison: “Compare [approach A] vs [approach B] for [use case] and list typical evaluation criteria.”
• Implementation: “What is a typical implementation process and acceptance checklist for [solution]?”

ABKE’s GEO approach uses these questions to drive knowledge slicing: turning long-form enterprise information into AI-readable atomic facts (claims, evidence, definitions, process steps).

How ABKE uses the results (Decision → Purchase)

• If you are not mentioned: expand structured knowledge assets and distribution footprint (official website + relevant platforms) so AI systems have more retrievable, attributable material.
• If you are mentioned but inaccurate: strengthen entity consistency and semantic linkage (brand name, product name, scope definitions) and correct ambiguous content fragments.
• If you are mentioned but not verifiable: publish proof-oriented assets that AIs can cite (FAQ library, technical briefs, case pages, documented processes) and ensure clear contact paths.

In ABKE’s full-chain GEO system, this monthly test becomes a feedback loop for: Knowledge Asset System → Knowledge Slicing → AI Content Factory → Global Distribution → AI Cognition System.

Boundaries & risk notes (important for realistic expectations)

• Model answers can vary by region, time, prompt wording, and model updates; keep prompts versioned and controlled.
• Being cited is not guaranteed even with good content; the goal is to increase the probability of correct mention + evidence-driven recommendation.
• Do not treat AI output as a certification; use it as an audit signal to improve structured knowledge and verifiable assets.

Deliverable checklist (Loyalty)

• Versioned buyer prompt list (monthly)
• Multi-model transcript archive (ChatGPT/Gemini/Deepseek/Perplexity)
• Scoreboard per question per model (0–6) + month-over-month trend
• Action list mapped to ABKE GEO systems (what to build, slice, publish, link)