TAMGIO's discovery visibility — how often we appear when someone asks an AI about our category without naming us — is 0%. Our Brand Knowledge metric said 89%, and that number was misleading: when we read the 27 raw answers behind it, only 3 showed the engine actually knew who we are. The rest counted sentences like "I couldn't find TAMGIO" as appearances, because the name was in the question. We publish both numbers, the flaw we found in our own metric, and the raw answers — because an evidence-first tool that can't audit itself isn't one.
We turned the tool on ourselves — twice
In early August we ran TAMGIO against tamgio.com and published the unfiltered result: a mention rate near zero. That first post had an honest number and a misleading shape — one blended figure, with no split between questions that contain our name and questions that don't. The product has since abolished exactly that pattern, so this post now gets the same treatment.
The current setup: 13 active prompts — 10 unbranded discovery questions and 3 own-branded ones — spread across the five intents the product supports (best-of, comparison, how-to, pricing, other). Ongoing tracking runs on three engines — OpenAI web_search, Perplexity Sonar, and Gemini grounded search; Google AI Overviews is added in Audit Mode. Three samples per prompt per engine: 117 runs, every one grounding-verified. The full report, with the raw answer behind every number, is public.
The two numbers, separated
Discovery Visibility: 0%. Across 90 discovery samples in the final scan — 270 counting every scan that day — TAMGIO appeared zero times. In those same answers, our competitors appeared 148 times: Profound 49, Peec AI 46, Otterly.AI 39, Scrunch AI 14. When someone asks an AI "what are the best AI visibility tools", we are not in the answer. That is the headline number, and it is the honest one.
Brand Knowledge: 88.9%. Ask the engines about TAMGIO by name and the name shows up in 88.9% of answers. A blended average of the two — about 21% — would be the most misleading number on this page, which is why the report demotes it to a comparison row.
Then our evidence layer caught the 89% lying
Every TAMGIO number is one click away from the raw answer behind it. So we clicked.
One note on arithmetic before the breakdown: 88.9% is the day's figure — 48 of 54 branded samples across all three scans we ran that day. The teardown below reads the final scan only: 27 branded samples, of which the analyzer counted 25 as appearances. Different windows, same story.
We read all 27 branded samples from the final scan and classified what each "appearance" actually was:
| Classification | Answers | What the raw answer meant |
|---|---|---|
| True recognition | 3 | Only OpenAI recognized TAMGIO as an AI search visibility analytics company |
| Generic inference | 3 | The engine guessed from the name what TAMGIO might measure |
| Explicit unknown | 11 | The answer said it could not find information about TAMGIO |
| Confused with something else | 7 | TAMGIO was mapped to another product or a similar name |
| Warned users away | 3 | The answer presented the domain as unrecognized or potentially risky |
The analyzer counted 25 of 27 as brand appearances — technically correct, because the name really does appear in the text. But 19 of those 25 "mentions" are the name sitting inside a sentence that denies knowing us. A brand-knowledge metric that can't tell recognition from denial is measuring name-echo, not knowledge. The metric then said so on its face: every TAMGIO report labeled Brand Knowledge as a name-echo rate and pointed to the raw answers.
Update, September 2026: we went one step further. The analyzer now reads the question too, and an answer that only repeats the name, says it can't find the brand, or confuses it with another company is no longer counted as an appearance — only real recognition is. Brand Knowledge figures from scans after this change are lower, and closer to what the raw answers say. Earlier scans keep their original classification.
This is a new-brand problem — and we're the worst example
We ran the same raw-answer teardown on two established projects we track. The share of misleading "brand appearance" verdicts falls off a cliff as a brand accumulates real citable history: 76% for TAMGIO (launched 2026), 14% for a local IT-repair business with directory listings and reviews, 7% for an image-compression tool engines have actually indexed. Established brands mostly get genuine recognition — engines quote their addresses, their features, their prices. New brands get their name echoed back inside "I couldn't find it." If you're launching something, the branded segment of any AI-visibility tool — ours included — deserves your skepticism until you've read the raw answers.
The honesty rules that made this catch possible
Two measurement rules did the work here. First, only grounding-verified answers enter the metrics: if an engine answers from its training memory without a live search, that's a memory answer — shown openly, counted never, because it isn't evidence of search visibility. Second, no_surface is not absence: when Google AI Overviews simply doesn't render for a query, that's a missing surface, not a missed mention. Both classes are visible in every report instead of being folded into a flattering — or damning — zero.
What we're doing about 0%
The discovery number won't move because we wish it would. Engines surface brands they can ground: consistent entity descriptions, an About page worth citing, Organization schema, third-party mentions in directories and reviews. That's our own work plan, and it's the same one our recommendations engine generates for customers — computed from the evidence, never promised as a percentage. When the number moves, the raw answers will show why. Until then: 0% discovery, 3/27 true recognition, and every answer behind both numbers, one click away.
Correction (): the competitor-appearance count was 151 in the original publication (Peec AI 47, Scrunch AI 16). After adding competitor-name verification to our analyzer — a competitor now counts only when its name, alias or domain actually appears in the answer text or its citations — 3 of those were LLM-invented mentions. The count is 148 (Peec AI 46, Scrunch AI 14). Discovery visibility (0%) and the 3-of-27 brand result are unchanged. The linked report carries a "recomputed" note.
Frequently asked questions
- Why is TAMGIO’s own discovery visibility 0%?
- Across 90 discovery samples in our latest scan — unbranded questions like "best AI visibility tools" — TAMGIO appeared zero times, while competitors appeared 148 times in the same answers. We are a new brand with almost no citable third-party history yet, so engines have nothing to ground us on. That is the exact problem the product measures.
- Didn’t the report say Brand Knowledge is 89%?
- Yes — and that number is why we rewrote this post. 89% was a name-echo rate: when the question already contained "TAMGIO", an answer like "I couldn’t find TAMGIO" still counted as an appearance. We read all 27 raw branded answers: only 3 showed true recognition. Since September 2026 such answers are no longer counted — the analyzer reads the question too and counts only real recognition; earlier scans keep their original classification.
- What is a memory answer, and why is it excluded from the metrics?
- A memory answer is when an engine replies from its training memory without running a live search. It is not evidence of search visibility, so TAMGIO excludes it from the metrics entirely instead of counting it as a miss — it is shown openly as its own class.
- What is the difference between no_surface and not mentioned?
- no_surface means Google AI Overviews didn't appear for that measurement at all — AIO is part of the SERP, and it does not render for every search: whether it shows up for the same question varies with time and context. Not mentioned means a surface did appear but didn't cite the brand. TAMGIO keeps the two separate so absence of a surface is never counted as your absence.
- Will TAMGIO promise a specific mention-rate increase?
- No. AI answers are non-deterministic, so no tool can honestly promise a fixed percentage gain. TAMGIO reports what N-sample scans measure, not guarantees.
- How many prompts and engines were used in this measurement?
- 13 active prompts — 10 unbranded discovery questions and 3 own-branded ones — across five intents (best-of, comparison, how-to, pricing, other). Ongoing tracking runs on three engines: OpenAI web_search, Perplexity Sonar, and Gemini grounded search; Google AI Overviews is added in Audit Mode. With 3 samples per prompt per engine, the final scan was 117 runs, all grounding-verified.
