Know exactly where you stand across search and AI answers, before you spend on anything else

We build the panel from your real US buyer questions, run it against US engine results at the stated locale, and score it against your named US competitors. Locale is a measured input, not an accent. The firm, RavenGroup Global Tech Private Limited, bills in USD, and every reading is stamped with the exact locale and engine set it was taken against, so you can see the market it was measured in rather than take it on faith.

No. A technical specialist assembles the question panel, reads the raw signals from every pillar, writes the narrative, and reviews the whole diagnostic before it reaches you. Technology speeds and sharpens that judgment, it does not replace it. A technical specialist directs every engagement and reviews it before delivery. The deliverable itself shows its working: the disclosed panel, the dated samples, and the reasoning behind each priority are things a bare tool export never contains.

Yes. Every part of it is checkable before you commit a dollar. The method is published in full on the methodologies page, so you can inspect exactly how the Machine-Readiness Score is constructed before you buy. A technical specialist with years of hands-on work in search and AI visibility reads and reviews every audit. It is a low-cost, one-time diagnostic, not a retainer, and the risk reversal is plain: if it surfaces no actionable finding, there is no charge.

A gig-marketplace audit gives you a tool export. This audit gives you a named specialist reading your specific visibility across the AI-answer surface most cheap audits never touch, your competitors scored on identical questions, and a prioritized plan of what to fix first. The price sits deliberately above the marketplace floor because sub-floor pricing signals the churn-and-burn work most owners are trying to avoid. For a single surface read, the Single-Surface Audit starts at $179.

We freeze your real buyer questions and run each across each engine many times, then report how often you appear as an appearance rate with a confidence band, stamped with the engine, locale and date, because all three change the result. We treat the AI-answer pillar as the most uncertain and show it with the widest band. Engines also differ sharply in how often they cite anyone at all: one 2026 analysis reported by DigitalApplied found Perplexity cited sources in roughly 97 percent of answers versus about 16 percent for ChatGPT, which is exactly why we sample per engine rather than checking once.

No. AI Overview selection is undocumented and volatile, engine behavior changes, and search results personalize, so no one can promise that. The audit measures your present position, ranks the corrections that move it most, and reports with variance. We commit to method and measurement, never to a promised number.

One thing, and it is about the diagnostic, not about outcomes: if the audit surfaces no actionable finding, you do not pay. That is a risk reversal on the usefulness of the work. There is no guarantee of a rank, a citation, or a traffic figure, because those depend on engines nobody controls. What we guarantee is that the reading is dated, disclosed, and reviewed by a specialist before it reaches you.

A domain-authority number is a single third-party estimate of link strength. The Machine-Readiness Score is a composite of four measured pillars, Classic Search, AI Answers and Share-of-Answer, Reputation and Sentiment, and Technical Foundation, normalized to one figure from 0 to 100 with the pillars always shown beneath it. It measures whether buyers can find and choose you across the surfaces they actually use, not just your link profile, and we document its construction rather than hide it.