We measure how AI systems represent your company.
Answerability measures how AI systems represent your company — where you surface, where competitors own the territory, and why the engines disagree. We publish the methodology, deliver the diagnosis, and build the fixes — or hand you the roadmap to run yourself.
What we are
Answerability.ai studies one narrow question with rigor: when a buyer asks an AI system for a recommendation in your category, what does the system answer, which sources does it cite, and how do you compare to the firms it names instead. Each engagement produces a written intelligence report — and when you want the fixes built, we build them (your developer deploys, we verify), or hand you the roadmap to run in-house.
We are a measurement practice. We describe what we observe across the major AI systems and turn it into an operational reading. We do not sell rankings, traffic, or guaranteed placement, and we have no incentive tied to any particular outcome.
Why this matters now
AI search systems — ChatGPT, Claude, Gemini, Perplexity, and Grok — increasingly mediate the moment a buyer first encounters a vendor in your category. Most companies have no instrument for whether they are cited, by which engines, or against which competitors. The behavior of these systems shifts faster than traditional SEO frameworks can describe, and the operative levers are different. We built a standing measurement protocol for that gap.
What we measure — our Answerability framework
We score every cited URL against our own operational framework — a measurement model we maintain, not a field standard or governance taxonomy. It has three pillars:
- Content — whether your material answers the questions buyers actually ask, in a form an engine can lift as an answer.
- Retrieval — whether engines can access, crawl, parse, and structurally understand that material.
- Trust — whether engines treat the source as cite-worthy, from internal evidence and external corroboration.
Answerability is the composite the three pillars roll up to in our scoring. A page can fail on any one of them for reasons the other two cannot fix. The full treatment of each pillar lives in our research notes; the homepage framework section shows failure-mode examples.
How we measure
- A standing set of roughly 60 buyer-intent prompts, constructed from your ideal-customer profiles and category.
- Run across five engines — ChatGPT, Claude, Gemini, Perplexity, Grok — producing on the order of 300 observations per engagement.
- A 21-day capture window, with URL-level scoring across the three pillars.
- A protocol that is versioned and revised monthly as the engines change.
We report observed co-occurrence — the characteristics that tend to accompany a citation — not declared ranking factors. The distinction matters, and it leads directly to the next section.
What we do not claim
AI engines do not publish their retrieval or ranking weights. We make no causal claim about why a system cites what it cites, and we guarantee no specific result — no ranking, no citation, no traffic. Findings describe behavior observed within a bounded capture window against non-deterministic systems, not universal or permanent rules; results are directional. The scoring blends protocol-based measurement with interpretive judgment — treat the numbers as a structured, comparative reading, not a calibrated scientific constant. AI systems are non-stationary and their behavior changes frequently, which is the reason monthly Visibility Intelligence is included with every Diagnostic. What you are commissioning is the measurement and the operational reading of it — not a promised outcome.
Vendor-neutrality
We accept no referral fees, affiliate commissions, or placement payments from any platform, tool, or vendor that might appear in a report, and hold no partnership that could bias a finding. Nothing in a report is there because someone paid for it.
What this is not
To set expectations precisely, an engagement is:
- Not a guarantee of AI visibility, citation, or ranking.
- Not a ranking product or a monitoring dashboard.
- Not paid placement, and not a route to one.
- Not access to model training, and not any ability to alter a model's outputs.
- Not a substitute for your broader marketing or content strategy — a measurement and a reading that should inform it.
If you are weighing this against the monitoring tools in the market, our field guide to AI-visibility tools lays out the distinction plainly: those platforms are instrumentation; a diagnostic is interpretation and remediation. Many organizations use both.
How an engagement works
- Scope. You share your domain, top competitors, and the buyer questions that matter most. We construct the prompt set together.
- Deliver. A long-form report with per-URL scoring, scoped work orders, and a sequenced 30-day roadmap — delivered as a PDF, with a 45-minute walkthrough.
- Implement (optional). We build the highest-leverage fixes — content, schema, retrieval, trust assets — and your team deploys; or you take the roadmap and run it in-house.
- Re-audit. At day 90 we re-run the identical prompt set against your updated site and produce a delta brief, so movement is measurable against the original baseline.
See the engagement tiers for current pricing, or order an engagement directly.
Research lead
Research lead 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. Reports are signed by the practice as an institution; individual attribution is withheld so deliverables read as a versioned body of work rather than a personal brand. This is a presentation choice, not a concealment — engagement clients know exactly who they are working with under NDA.Confidentiality
Every engagement is governed by a mutual non-disclosure agreement signed before scoping, which remains in force after delivery. Your domain, competitors, scores, and report contents do not leave the engagement. Reports referenced on this site as examples either carry explicit written permission from the engagement's sponsor, or are anonymized to the point where the original engagement is not identifiable. Data handling is documented in full in our privacy policy.
Research corpus
The framework is formalized in a versioned set of working notes — a methodology primer and a note on each of the three pillars — published openly at Insights. They are the reference behind the scoring in every engagement, and they are revised as the engines change.
Work with us
If your buyers are forming shortlists inside AI systems, a diagnostic tells you where you stand, against whom, and what to change. Commission an engagement, or write to hello@answerability.ai — every message is answered personally.