A scan produces more than the headline number. This page walks through each section of the report, what it is computed from, and how to act on it.
Score and tier
The headline is your 0 to 100 score and its tier, from Invisible to Leader. The formula and weights are documented in How the score is calculated. Two brands are only comparable on the same query set style, so treat the score as your benchmark to move, and the tier as the plain-language verdict.
Mention rate vs overall score
The report says your brand appeared in X of 25 queries. That mention rate is usually higher than your overall score, and the gap is informative. Mention rate counts a query as a win if any single engine named you. The overall score is sample-level, so it also punishes inconsistency across engines and low-quality mentions. A high mention rate with a low score means you are known but not recommended: engines name you occasionally, buried in lists, rather than putting you first.
The engine matrix
The per-engine breakdown shows how many queries each engine mentioned you in. Read it as a map of where your visibility problem lives. Strong on the web-grounded engine but absent from the direct models means the live web knows you but training data does not: typically a young brand, fixable with sustained coverage that models eventually absorb. The reverse pattern, known to models but missing from web-grounded answers, means your current web presence is not surfacing in retrieval, which is an SEO and content problem you can attack now.
Share of voice and competitors
Every brand named in every answer is extracted and tallied. Your share of voice is your mentions divided by all brand mentions in the scan. The same tally, sorted, becomes your competitor table: the brands AI engines recommend in your space, ranked by how often they appear. Expect surprises. The brands AI engines push are often not the competitors on your sales battlecards, and the table tells you whose content the engines have learned from.
Content gap roadmap
The queries where you were not mentioned are the raw material for the content gap section. From those, the report generates 5 to 10 recommendations, each with a topic, a suggested content type, the target queries it should win, an estimated visibility lift, and a priority. The logic is direct: these are real questions AI engines answered without you, so publishing strong, citable content on exactly these topics is the shortest path to appearing in those answers.
Missed opportunity estimate
The report translates your visibility gap into an estimated monthly cost: missed visitors and lost customers. This is a directional model, not a measurement, and it is computed transparently:
- Your niche is classified into one of 15 categories (SaaS, ecommerce, agency, fintech, and so on), each with a benchmark range for monthly AI-driven search volume and a typical conversion rate.
- Your capture rate (queries where you were mentioned) is compared against your top competitor's capture rate from the same scan.
- The gap between the two, applied to the niche volume benchmark, gives missed visitors; the niche conversion rate turns that into lost customers.
Use it to size the problem and prioritize, not as revenue attribution. The per-query evidence and competitor table are the hard data; this section is the business translation.
Improving your score
The score moves when the answers move, and the answers move on two different clocks. Web-grounded visibility responds in weeks: publish content targeting your gap queries, earn citations on the pages engines retrieve, and make sure your own domain ranks for your category terms so the authority signal flips. Direct model visibility responds in months, as sustained coverage, reviews, comparisons, and mentions across the open web get absorbed into new model versions. Re-run the scan monthly and after major launches; reports are cached for 24 hours per brand and website, so day-to-day reruns return the same report by design.