The model01
2,284 kinds of facts. One company record.
We built an ontology of the facts that can exist about people and companies, plus the temporal model to reconcile changing evidence and derive what is true now or at any date.
We ask AI agents about you cold, with live search, then show you what they said and what they read.
What do you think of [your company] ([yourdomain.com])? Answer in 100 words.Nothing else is sent. No system prompt, no memory of you, nothing from us — each agent has its own live search tool and goes and looks. The filled-in question is printed again with your scores, so you can check it against this.Claude claude-opus-5ChatGPT gpt-5.6-terraPerplexity sonar-proGemini gemini-pro-latestEvery free ask puts that question to all four, once each. A subscription raises it to 3 or 5 asks per agent — the same question, asked again — because one ask is one draw and a spread across several is what shows whether a score is really where it sits. It also opens the other benchmark questions: a buyer, an investor and a candidate ask different things about the same company and get different answers back.
0–3Steers the buyer somewhere else.
4–6Praises you, then tells the buyer to check first.
Describes you accurately and stops there.
Recommends you, no caveat.
One published sentence, the same for every company, printed again beside your scores.
The ones a buyer reaches for, named with their model ids. The id prints beside every score it set.
Every score here was measured when you asked for it and carries the minute it ran.
Deterministic. Sentiment is what you see, reachability is what moves it.
Agents search while they answer, so every ask is its own sample of what they say. A free scan asks each agent once and the reading says so. More asks in a round, and the same round run again over weeks, is a larger sample.
The rubric is public. Every score quotes the clause that set it. Score an answer yourself and disagree with ours.
How the score is derived →Buyers ask ChatGPT, Claude, and Perplexity before they ever ask you. Oomira shows you their answers, verbatim: what's wrong, what's missing, and exactly what each objection is asking you for.
We built an ontology of the facts that can exist about people and companies, plus the temporal model to reconcile changing evidence and derive what is true now or at any date.
Across 197 reports, Oomira scored 1,346 agent answers and captured 8,473 pages those agents opened. Every run adds sourced facts to the company record underneath.
It lets Oomira resolve namesakes, distinguish stale facts from missing ones, measure independent corroboration, find what agents left out, and tell which AI answers you can actually fix.
Most of what a company knows is not missing. Some of it is in people, some of it is in a drive somewhere, and a great deal of it is on the open web. The trouble is that so much of it conflicts, so much of it is never reached for at the moment it is needed, and so much of it you cannot see at all.
I ran into all three building my previous company, SRTX. I wanted one record where every fact carries its source and the date it became true. It did not exist, so I have spent since 2025 building it. Today AI readiness is one way into that record: doing the report builds the memory underneath it instead of asking you to maintain another system.
Oomira is independent. If anything on this page is wrong, that is my problem to fix.
Katherine HomuthFounder, OomiraMore about who is behind Oomira →
That is the sentence that costs the meeting, and it is not a hallucination — it is an agent behaving correctly with too little to go on. Every reservation it raised names something their record can answer.
Still the safest default for most online businesses.
Every agent recommends them outright, and the machine-readable half is still incomplete: twenty checks pass, seven only in part.
[They] look like a strong enterprise SEO and content intelligence platform rather than a lightweight SMB tool.
Their edge served every browser normally and turned away the agent named ChatGPT-User with a 403. Locked out of their own pages, one assistant answered about a different company with a similar name.
So: no reviews, press coverage, or verifiable track record that I can see.
A search on their own name and their own domain returned ten results, none of them about them.
I couldn't load the site directly, so I can only judge it by what it's promoting rather than the design or content quality.
Their ticket page loads as script, so a program asking for it receives JavaScript instead of text.
Honest caveat: I can't judge delivery quality or pricing from marketing copy.
Not one claim they publish carries a printed date, so their performance numbers read as undated marketing copy.
Honestly, I can't form a strong opinion: there's little independent review coverage, and the self-described scale sits oddly with such a tiny headcount.
Perplexity read Trustpilot, Sitejabber and a scam-check page for an unrelated retailer with a near-identical name.
Every figure above carries the date it was measured and the table it was measured against. That is one of the checks this report runs on other companies: whether a page says when its numbers were true. This is us answering it.
SUBJECTS DESCRIBED, NEVER NAMED
A model may have picked up something about your company in training, and for most companies there is not much of it. Either way it is not what decides the answer: every agent we ask holds a live search tool, picks its own queries and opens pages while it writes. So a failing check is not a technical fault, it is a channel you are not supplying, and the answer gets written without it. We grade 45 of them, on two kinds of pages.
One square below is one named check. Your report hands every square back marked, and the marked ones are the reachability score it prints. Open any row to read their names.
An objection built from your own pages is the kind publishing can retire. They read live, so the new page is reachable on the very next ask, which is the part that is in your hands.
Publishing does not reach these. Either your own page becomes the better answer to the same question, or the claim is corrected where it lives.
Not all of them apply to everybody: commerce checks need a storefront, coverage checks need press to grade. One report grades between 15 and 32 of the 45 and leaves the rest out of the denominator rather than counting them against you.
Every query an agent chose for itself, and every page it opened, is recorded. That is what makes a sentence about you addressable: the four answers in our own report rest on these pages, and the ones on our own site are the ones our next edit reaches.
Ask about any company, get your own scores and a sample report on the house, and decide from there.
A report is true on the day it ran. What an agent says about you is written fresh every time somebody asks, out of a web that has changed since — the plans exist so your record keeps up. If nothing about your company ever changes, buy the report once and do not subscribe.
Any company: yours, a competitor, one you advise, or one you are about to invest in or buy from. The scan runs deep and the scores land in your inbox.
Net Promoter®, NPS®, and Net Promoter Score® are registered trademarks of Bain & Company, Inc., NICE Systems, Inc., and Fred Reichheld. Agent NPS applies the Net Promoter method to AI agents and is not affiliated with or endorsed by Bain & Company.