AI systems recommend your competitors more than you.
Answerability is a long-form intelligence report on how ChatGPT, Claude, Gemini, Perplexity, and Grok answer buyer questions in your category — and an operational roadmap for closing the gap.
AI systems recommend your competitors more than you.
AI Visibility
Competitor share
Answerability
AI systems are becoming the pre-sales layer.
Buyers increasingly evaluate providers inside ChatGPT, Claude, Gemini, Perplexity, and Grok before they ever visit a website.
An illustrative reconstruction of what most companies see when they audit a high-intent buyer query in their category. A real diagnostic shows the actual prompt, the actual competitors, the actual domains, and the exact source paths the engines surfaced — across all five engines, across all 60 prompts.
We are entering a world where recommendation layers matter more than rankings.
Three independent failure modes.
A page can fail on any one axis for reasons the others can't fix. We score every cited URL on Retrieval, Trust, and Answerability — separately.
Can AI systems access, crawl, parse, and structurally understand your content?
Example failure: a sample study living as a PDF with no HTML wrapper, no schema, and no extractable text.
Do AI systems treat your content as cite-worthy when an answer is on the line?
Example failure: no named methodology reviewer, no credentialed engineer attached to claims, no third-party corroboration.
Can your content function as the quoted answer to a specific buyer question?
Example failure: a calculator that renders in JavaScript and produces no extractable prose for an AI to lift.
Most companies aren't failing because they can't be read. They're failing because they're not being believed.
Each AI system behaves differently.
Patterns observed across our standing prompt set, updated monthly. Causal claims are deliberately avoided — these are observed correlations, not declared ranking factors.
| Engine | Visibility | Appears to favor | Typical failure mode |
|---|---|---|---|
OChatGPTOpenAI · GPT-4o |
38% |
Structured author entities, dated content, deep-linked sub-pages — commonly present among cited pages. | Weak author entity. No Person schema or sameAs on service pages. |
AClaudeAnthropic · Sonnet 4.6 |
51% |
Methodology depth, quote-safe paragraphs — observed to co-occur with cited results. | Thin “how we work” documentation. Few 40–80 word extractable chunks. |
GGeminiGoogle · 2.5 Pro · with Search |
0% |
High external entity corroboration — Knowledge Graph, Wikidata, news mentions commonly present among cited pages. | No Wikidata item, weak entity graph, Google Business unverified. |
PPerplexitySonar Pro |
27% |
Primary-source citations — statutes, regulators, peer-reviewed work — commonly present in cited results. | Unsourced numerical claims. No hyperlinks to primary references. |
XGrokxAI · Grok 3 |
14% |
Recency, social and trade-press surface, active publishing cadence — observed to co-occur with cited pages. | No recent published mentions. Last cornerstone page dated 11 months ago. |
Pattern from a recent engagement — numbers shown are illustrative and will differ for every category. Single-run observational sample; findings describe co-occurrence within this engagement’s prompt set, not declared ranking factors. Last updated May 23, 2026.
A long-form intelligence dossier.
Nine chapters across executive summary, framework, buyer segments, engine behavior, competitor landscape, URL work orders, trust gap, 30-day roadmap, and methodology appendix. Designed to be printed, circulated internally, and revisited operationally. Re-audit included at 90 days.
AI systems recommend your competitors more than you.
How Answerability is scored.
Who AI systems cite instead.
Priority URL work orders.
What to do, week by week.
Every page becomes a work order.
A scored URL becomes a scoped fix. Each work order names the bottleneck, the action, the effort, and the affected buyer queries — pulled directly from the report.
Every engagement begins with the buyers, not the keywords.
Before a single prompt runs, we construct the buyer archetypes for your category. Each one becomes a named profile with decision criteria, language patterns, and the queries they actually type. Visibility failures are often segment-specific — the same site can succeed with one buyer type and disappear with another.
The fields are constant. The content is built from your buyers.
Profile
Demographic and situational detail: age, role, portfolio or business stage, geography, discovery channel, advisor relationships.
Decision criteria
What this buyer stress-tests before short-listing a provider. Specific to their domain — credentials, methodology, audit history, pricing transparency, peer signals.
Search behavior
Representative queries this buyer types into AI engines — the awareness, comparison, risk, pricing, and fit questions that drive 80% of their pre-purchase research.
Bottleneck axis
Which of Retrieval, Trust, or Answerability is suppressing your visibility with this archetype — and the specific trust signal most absent from your pages.
The audit math, made visible.
Every engagement produces 300 observations across the five engines — one for every prompt × engine pair.
Example from one engagement — one of four archetypes built for a specialty professional-services firm. Yours will look nothing like this; the fields are constant, the content is built from your buyers.
The Risk-Averse Established Buyer
- StageEstablished operator, 10+ years in business
- Decision lensAudit / risk defensibility over savings or speed
- Advisor stanceBrings options to her professional advisor before deciding
- WillingnessWill pay a premium for peace of mind
- Verifiable credentials of the person actually signing the work
- Documented outcomes in audits or examinations, not just claims
- Specific scope of post-engagement support — hours, who responds, who pays
- How methodology aligns line-by-line with the regulator’s published guidance
- most established [provider] with audit history
- [provider] firms with zero adverse rulings
- what is included in [provider] post-engagement support
- regulator-defensible methodology for [domain]
Named credentialed reviewer attribution −47 vs competitors. She never sees a verifiable human on our pages.
How we build these. Archetypes are constructed from your stated ICP, your top-of-funnel CRM patterns, sales-call transcripts where available, and language pulled from adjacent buyer communities — Reddit, vertical forums, industry trade press. Each archetype produces 15 prompts across awareness, comparison, risk, pricing, fit, and post-purchase stages. No keyword tools. No generic SEO term lists. The prompts are the questions actual buyers are typing into ChatGPT, Claude, Gemini, Perplexity, and Grok.
Built for companies losing visibility they didn't know they had.
If you're already showing up in AI answers, you don't need us. If your competitors are showing up and you aren't — and you can't explain why — this report explains why.
You’re losing referrals to competitors with named experts on every page.
AI engines cite review sites instead of your category pages.
Trust signals determine the shortlist before a discovery call.
AI engines are reading your “X vs Y” pages and citing the competitor.
Three ways to work with us.
Each engagement produces a written artifact. None of them produce a dashboard. All of them are confidential under MNDA.
The standing artifact. A long-form intelligence report analyzing how AI systems retrieve, trust, and cite your company across buyer-intent queries.
- Executive summary + framework
- Per-engine visibility analysis (5 engines)
- Competitor citation landscape
- URL-level scoring (up to 50 URLs)
- Scoped work orders
- 30-day roadmap + 45-min walkthrough
- 90-day re-audit included
The full diagnostic, plus a focused four-week sprint producing the highest-leverage retrieval, trust, and answerability assets identified in the report. Your team or contractor ships them; we review before they go live.
- Everything in Diagnostic
- Up to four 60-min working sessions, scheduled in advance
- Deliverables: llms.txt, JSON-LD schema package
- Methodology page draft, entity graph URL list
- Your team or contractor handles deployment
- We review the shipped pages mid-sprint
- 90-day re-audit included
The standing prompt set re-run every 90 days against your updated site. A quarterly delta briefing tracks score movement and surfaces new failures. Begins after the day-90 re-audit included with the Diagnostic.
- Quarterly re-audit (same prompt set)
- Delta briefing per cycle
- Engine behavior change notes
- New competitor citation surfacing
- Quarterly 45-min readout
- Diagnostic required to start
How an engagement works.
Three discrete steps. Each has a defined artifact. The thing you're paying for is the written work, not the meeting time.
Scope the audit.
You share your domain, top three competitors, and the buyer questions that matter most. We build the prompt set together and run the audit across all five engines.
Deliver the report.
You receive the dossier by email as a PDF, plus a 45-minute walkthrough. Every URL on your site that appeared in any cited result gets scored and gets a work order.
Verify the lift.
We re-run the same prompt set against your updated site and produce a delta report. You see exactly which actions moved which scores — and which still haven't moved.
How the audit runs.
A standing protocol, versioned and updated as engine behavior shifts. Findings describe observed patterns within a bounded sample — not universal ranking rules.
Answerability is an independent research practice. The principal investigator is an economist and AI researcher whose prior work spans applied machine learning, internet platforms, and expert analysis in technology-related matters.
Engagements are produced as structured research artifacts using the proprietary Retrieval / Trust / Answerability framework, scored against the standing 60-prompt audit set, and reviewed before delivery.
Questions buyers ask first.
If yours isn't here, write to hello@answerability.ai.
Traditional SEO optimizes for search-engine ranking signals — backlinks, keyword density, technical crawl health — under the assumption that the user reads a list of results and picks one. AI-mediated discovery skips the list. The model reads, decides, and recommends.
Our scoring rubric (Retrieval / Trust / Answerability) was built for the answer-layer behavior, not the rank-list behavior. There is meaningful overlap with technical SEO on the Retrieval axis. There is essentially none on Trust and Answerability.
No. AI engines do not publish their retrieval or ranking weights, and any honest practice has to refuse a guarantee. What we do guarantee is the artifact: a scored URL ledger, scoped work orders, a sequenced roadmap, and a re-audit at day 90 against the identical prompt set so movement is measurable.
Engagements where the client shipped the priority work orders typically see meaningful citation movement within the re-audit window.
The framework, the engine set, and the scoring rubric are standing protocol. Every other element — the buyer archetypes, the 60 prompts, the URL ledger, the competitor landscape, the work orders, the 30-day roadmap — is built from your domain, your buyers, and your category.
If we ran the audit against your two closest competitors next week, you'd get three reports that look related in chrome and unrelated in content.
It's almost always the entity graph, not the audit. Engines that lean on Wikidata, Knowledge Graph, and verified business listings — Gemini in particular — won't surface a company that isn't in those graphs, no matter how good the on-site content is. We surface this explicitly in the per-engine analysis and it usually becomes the highest-leverage line item on the roadmap.
Yes — use Read the sample report. We send a real diagnostic to your work email, anonymized where contractually required. No subscription, no follow-up sequence, no qualification gate.