Positioning gap
Clearer story, stronger proof
VectorGap should make its agency operating model, audit-fix-retest loop, pricing logic, onboarding flow, and report output easier for AI and buyers to repeat accurately.
See the brand story AI tells before the client hears it from a buyer.
Perception is the agency diagnostic for how AI describes a client before a recommendation battle begins. VectorGap audits prompts such as what is it, can I trust it, is it worth the price, should I buy it, reviews, alternatives, problems, capabilities, and pricing across ChatGPT, Claude, Gemini, Perplexity, Grok, Mistral, and DeepSeek — then separates AI memory from current web evidence so hallucination risk, coverage, source quality, missions, retests, and client-ready reporting make sense.
- Provider-level answers for trust, pricing, reviews, alternatives, recommendation, product, and legitimacy prompts.
- Coverage, accuracy, hallucination, visibility, and recommendation scores tied back to both answer evidence and current web proof.
- Web-evidence comparison and truth-layer review so wrong, missing, stale, or unsupported claims become visible.
- Mission Control handoff, same-target retests, and exports for client services and QBRs.
98
LLM prompts
14
buyer-question templates
7
standard providers
Prompt coverage
14 buyer questions
The audit covers practical questions buyers ask AI: what it is, whether it is legit, whether to buy, price/value, reviews, alternatives, problems, products, capabilities, trust, and recommendations.
Agency output
Answer → evidence gap → mission
Each weak or inaccurate answer can become an agency action: fix the public fact, strengthen market proof, repair source evidence, retest, and export the next client report.
AI can describe the client confidently and still describe it wrong
Clients usually discover the problem after a prospect asks an AI system and repeats the answer back to them. Agencies need to catch the weak story first: the vague category label, missing differentiator, stale pricing assumption, unsupported trust claim, or competitor-framed alternative list.
Description quality
See whether the provider explains what the client does, who it serves, why it matters, and which category it belongs in without flattening the positioning.
- What is it?
- Products
- Capabilities
Trust and legitimacy
Inspect whether AI frames the client as credible, risky, thinly evidenced, overhyped, or unsupported when buyers ask trust-heavy questions.
- Can I trust it?
- Is it legit?
- Is it a scam?
Commercial intent
Catch pricing, worth-it, recommendation, review, should-buy, and alternative prompts before the client loses confidence or budget.
- Worth price
- Should buy
- Alternatives
A score is useful only when the agency can inspect the answer behind it
VectorGap does not treat Perception as one opaque number. The audit separates coverage, accuracy, hallucination risk, visibility, recommendation strength, provider variance, and web-evidence support so the agency can explain the result without pretending AI answers are static.
Coverage and visibility
Find prompts where the client is omitted, barely explained, or described with a generic category label that weakens the next recommendation.
Accuracy and hallucinations
Compare provider answers against Brand Knowledge so invented features, stale facts, wrong pricing, or missing claims become remediation work.
Provider variance
Use differences between ChatGPT, Claude, Gemini, Perplexity, Grok, Mistral, and DeepSeek to identify fragile facts, stale AI memory, and source gaps.
What is the difference between AI Readiness and Perception?
AI Readiness checks whether AI systems can retrieve, cite, and reuse the right public evidence. Perception checks how AI systems describe, classify, trust, price, and recommend the brand. Clients need both when the website may be technically retrievable but the AI answer still misstates the brand, omits key proof, or frames a competitor as safer.
Use Perception to prove misrepresentation
Preserve the prompt, provider, answer excerpt, truth-layer comparison, hallucination risk, source gap, mission, and retest target before asking the client to fund a fix.
Use GEO to repair the evidence layer
Improve the page, schema, source, entity, FAQ, and proof blocks that the next retest should be able to retrieve, cite, and defend.
Use both for client reports
Show what AI said first, why the answer was weak or unsupported, what changed on the site or source layer, and whether the same target improved after remediation.
Turn perception gaps into a report the client can act on
The agency output is not “AI mentioned you.” It is a defensible work sequence: show the answer, identify the incorrect or weak public proof behind it, create the mission, ship the fix, retest the same target, and include the movement in the client report.
Baseline report
Package the current AI story by provider and prompt category so the client sees which answers help, hurt, or confuse the market.
Truth and proof sprint
Fix company facts, category definitions, pricing explanations, comparison-safe claims, FAQ/schema support, and extractable proof blocks.
Retest and QBR pack
Repeat the same prompts after the work ships and show whether coverage, accuracy, hallucination risk, and recommendation strength changed.
Perception gap → report proof → Agency OS
Perception diagnostics become buyer value when weak answers turn into report evidence, checkout-risk reduction, and recurring delivery capacity. Route the buyer from inspectable perception proof into pricing proof-before-payment, then into Agency OS when they are ready to run the same workflow across a client portfolio.
Inspect the perception report
Show how weak or wrong buyer answers become prompt evidence, truth gaps, Mission Control actions, same-target retests, and client-ready reporting.
Open pageReduce payment risk
Pricing proof-before-payment lets the agency inspect the GBI, Perception evidence, Mission Control actions, and report shape before it commits to the operating plan.
Open pageBuy the operating capacity
Agency OS turns perception diagnostics into recurring brand-story audits, truth/proof missions, same-target retests, exports, and client-ready reports.
Open pageWhich evidence does AI need before it can reuse the page?
This table turns the page into a structured extraction target: the buyer question, the evidence an AI system can read, and the action an agency can sell or execute next.
| Buyer question | What AI can extract | Agency action |
|---|---|---|
| What story does AI tell about the client? | Provider answers for trust, pricing, recommendations, reviews, alternatives, products, capabilities, problems, and legitimacy are tied to prompt categories and evidence quality. | Find wrong, missing, stale, or unsupported claims and turn them into Brand Knowledge, page, FAQ, source, and proof updates. |
| Where does perception create commercial risk? | Coverage, accuracy, hallucination risk, visibility, recommendation strength, and provider variance show whether the client looks credible before a competitor comparison. | Prioritize the prompts that affect sales conversations, then scope a truth/proof cleanup sprint and retest the same buyer questions. |
| How does this feed a client-ready report? | The audit connects prompt evidence, answer excerpt, truth gap, mission scope, and retest target in one chain. | Export the baseline, assign remediation work, rerun the same prompt set, and report movement in the next client review. |
Questions agencies ask before turning AI visibility into client work
What does a Perception audit measure?
It measures how AI providers describe, classify, trust, price, recommend, and explain the brand across buyer-like prompts. VectorGap keeps the answer evidence attached to coverage, accuracy, hallucination, visibility, and recommendation signals.
How does this become agency revenue?
The audit creates a baseline report, a truth/proof/source cleanup sprint, and a retest pack. That gives the agency a concrete diagnostic, implementation scope, and recurring reporting motion.
Why not just ask ChatGPT manually?
Manual checks are not repeatable enough for client work. VectorGap preserves provider, prompt, market, language, persona, score, answer evidence, mission, and retest context so the agency can report movement.
What is the difference between AI Readiness and Perception?
AI Readiness checks whether public pages, schema, sources, and proof can be retrieved, cited, and reused. Perception checks how providers describe, classify, trust, price, and recommend the brand. Agencies use Perception to prove misrepresentation and AI Readiness to repair the evidence layer behind the next retest.
How do agencies prove AI hallucinations to clients?
Show the prompt, provider, answer excerpt, unsupported or hallucinated claim, truth-layer comparison, source gap, recommended mission, and same-target retest target. The client sees the risk before approving remediation.