A proposed method for auditing a business's readiness for agent commerce

Published 12 Nov 2026 by Clear Rank Studio. This is a proposed methodology in validation, not a demonstrated service, and not an offer.

What the audit is intended to answer

When a buyer asks ChatGPT, Perplexity, Gemini, Claude, Copilot or Google's AI Overviews about a product or service like yours, what do they actually say? Do they name you, name a competitor, or get it wrong? And when an AI agent tries to transact with your site - find a price, check stock, complete a purchase - can it?

Proposed method

Real buyer questions. 15-20 questions the business's actual buyers ask, intended to be agreed with the business from their own sales conversations and support inbox, not invented keyword lists.
Consumer AI products, manually inspected. The intent is to put each question to the consumer AI products buyers use (chatgpt.com, Perplexity, Gemini, claude.ai, Copilot, AI Overviews where accessible), inspected manually where authorised within each product's free-tier limits and terms, with every answer saved and dated. This coverage is proposed scope: access may require accounts and may be limited by rate limits or product terms. Actual coverage and turnaround depend on a product-by-product access and quality test; they are not demonstrated capability today. Where a like-for-like API exists for a surface, an API pass may add breadth and would always be labelled a model-level sample - a proxy for the consumer product, not a measurement of it.
Named competitors. Answers would be benchmarked against the competitors the business names, so the output is "they name X, not you, on these questions" - not a generic score.
Agent-readiness checks. Two distinct levels. Our free public checker analyzes pasted HTML or a local HTML file entirely in the browser, using heuristic checks for titles, headings, metadata, structured data and related page structure. It does not fetch or verify an arbitrary customer site. A separate server-side demonstration checks only a fixed allowlist of example sites, including robots.txt, llms.txt, sitemap and response-status observations. Those checks are not proof of crawler compliance or full standards conformance. The intended audit would add transactability checks - whether an agent client can find pricing, stock and complete a purchase flow - which are proposed and not yet tested on customer sites.
Prioritised fix list. Every finding would land as a concrete fix, ordered by expected impact, with the evidence attached.

What it is not

Not a ranking promise. No one can guarantee what an AI product will say tomorrow.
Not an automated screenshot farm, and not yet a service with demonstrated coverage or turnaround.
Not a substitute for the free checks. HTML structure checks (including our own free checker) answer only part of this. The intended difference is dated, saved, per-answer evidence across the consumer surfaces, benchmarked against named competitors, plus agent-transaction readiness.

Why publish the method first

So the method can be judged on its merits before anyone is asked to rely on it. It is published as part of a validation exercise; there is no paid offer.