Find out exactly where you are losing visibility, and what to fix first

An audit hands you a score and a list. This hands you a decision. The Visibility Gap Diagnostic exists to answer one question: of the four surfaces, which one is actually costing you customers, and what to fix first. We read all four, isolate the single gap doing the damage, explain why it is happening, and route to the exact next correction in order. For the full scored baseline with every pillar broken out, that is the Surface Intelligence Audit. For an end to the guessing about which problem to solve, this is the router.

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. A specialist directs every engagement and reviews it before delivery, wherever the specialist sits.

No. A technical specialist assembles the question panel, reads the raw signals from all four surfaces, writes the diagnosis, and reviews the whole document before it reaches you. Technology speeds and sharpens that judgment, it does not replace it. Nothing here is synthetic or churned out. The proof is in the deliverable itself: the disclosed panel, the dated samples, the named gap and the reasoning behind each priority are things a bare tool export never contains.

Not anymore, and this is the exact trap the diagnostic exists to catch. Ranking on Google and being named inside an AI answer are separate outcomes drawn from overlapping but different signals. In a 2026 test across the major AI engines, Uberall found that 83 percent of restaurants never appeared when someone asked for a nearby place to eat, even though 86 percent had a Google presence. You can rank well and still be absent from the answer your buyer actually reads. The only way to know is to measure the AI-answer surface directly, which most audits never touch.

Because no two visibility problems are the same size. One business has a clean surface and a single AI-answer gap, another has a broken technical foundation, conflicting addresses across forty directories, and a thin review profile all at once. Publishing one number for both would be a fiction, and pricing by how expensive a business looks would misrepresent the work involved. We publish the full method and deliverables here, read your surface, then confirm the exact figure in writing. You see the substance before any number.

Then we state that plainly, and route the fix regardless of whether it is one we sell. If your gap is a technical foundation issue your existing developer can close, or a review problem you can run in-house, the routing plan says so. Earning your next engagement by pointing you to the right fix, even when it is not ours to sell, is the better trade.

No. AI answer selection is undocumented and volatile, engine behavior changes, and search results personalize, so no one can promise that. The diagnostic measures your present position, names the gap costing you the most, and routes to the correction most likely to move it. We commit to method and measurement, never to a promised number.

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 surface 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.