People ask AI to compare and decide before they reach you. Oomira shows what it said, why, and whether your change moved it.
Your first question is free. No account. Two minutes. Then 7 days of Teams free, no card.
“Pivoting. Positioning is unclear.”
What the 4 agents said. Your site had already changed; the pages machines read first had not.
“They describe the current product.”
Same 4 agents, same question, minutes later. Every one opened your pages fresh.
Hypothesis: machines were reading an older site than people see. One change, then the same question, asked again.
Result · supported4 against you, 11 mixed, 5 for you. Every against verdict is a buying decision: the comparison, the API, security, fees.
The answer is an output of specific pages, yours and other people’s. So it can be changed, and the change can be measured.
They work together: Oomira finds the page and runs the test, and the tools you already have show the traffic move.
You pay 12 partners and agents quoted 3 of them. A review roundup, a tech magazine and a founder blog, none of them paid, shaped 5 to 8 answers each. One 2024 comparison carries 6 of the against-you answers on its own.
A ranked list of the sites that decide the answer, what each one says about you, and which of them are wrong.
They are the fastest to set up, but their API is read-only, so most agencies end up elsewhere.
Settled from a 2024 comparison site. Your changelog says the write API shipped in May. No agent opened it.
They list SOC 2, SSO and SCIM on one page. Your /security has SOC 2 in an image.
Two agents quote a per-invoice fee from a 2024 help article that /pricing replaced.
A forum thread leads. Your response exists as a PDF that no page links to.
In every case the accurate version exists somewhere on your site. It is on a page agents do not open, or worded so they do not use it.
$ oomira fetch yoursite.com/integrations fetch 200 · 0.6s body 41 words list rendered by script · 0 names in HTML schema none opened 2 of 4 agents quoted never
/pricing cited 9 times, the 2024 fee article 7, the API reference 5. Buyers hear your prices in support wording.
/integrations is rendered by script: 41 words and no tool names in the HTML. Agents open it and quote none of it.
592 of 624 pages load. The comparison page an agent wants for the head-to-head question redirects to the homepage.
Every item comes with the page address, what the fetch returned, and which agent hit it. Engineering gets a ticket, not a theory.
See your site as a machineAI answers vary, so a prediction says fewer agents will say it, never that one will. Each test ends supported, rejected or inconclusive against the answers before it.
The write API on /api. Current fees in the help article. Security controls as text.
Ask the 2024 comparison site to correct it. Brief the review roundup that shaped the most answers. Put the launch response where agents read.
Render /integrations in HTML. Redirect the 6 moved pages and the 2 dead ones. Restore your own comparison page.
Same questions, same agents. Answers, pages and sources compared with before, so you see which change moved what.
Your first question, put to every model.
One company. Ask, change, re-ask.
For the people who own the pages.
Several brands, markets or a custom setup.
One ask is one question put to one model. A question on all 4 models is 4 asks; a test re-asks only on the models it needs. Spend them when you want, to re-test a change or add a question.
The customer journey now happens before the visit: people ask an AI agent to compare, recommend and decide. Oomira asks 4 named agents (Claude, ChatGPT, Perplexity and Gemini) the questions buyers, investors, candidates, partners and press actually ask about a company, cold, with live web search and nothing supplied by us. It shows each answer word for word, marks its claims for you, mixed or against you, and traces the answer to the pages it opened: the company’s own, its competitors’ and the outside sites that shaped it. Then it writes a hypothesis about what is driving the answer, and when a page changes it asks the same questions again and compares with the answers before.
Web analytics measures what people do once they reach a site. SEO and technical tools measure whether pages are indexed, reachable and structured. AEO and visibility tools measure whether a company appears in AI answers and its share against competitors. Oomira answers a different question: does changing this page change what the agent says. It works in experiments, a change then a prediction then a result, which is closer to A/B testing for the AI funnel than to a visibility dashboard. They are complementary: Oomira finds the page and runs the test, and the other tools show the traffic move.
The answer word for word, annotated: each sentence marked for you, mixed or against you, with the searches that agent ran and the pages it cited. Every page it opened is fetched and kept, yours and other people’s, so a claim can be traced to the page that caused it. From the answers Oomira writes beliefs the agents hold, conclusions about the account, and tests to run: each one a change, a prediction, and the questions to ask again.
Your own assistant holds your history, your earlier questions and often your own website already in the conversation, so it answers about a company it has been introduced to. Oomira asks four agents cold: a blank slate, nothing supplied by us, one live search each. That is the answer a stranger gets, and it comes back with every page each agent opened, so a claim can be traced to the page that caused it rather than argued with.
Yes, by changing what the agents read, and you can see it happen. Every ask is an evidence environment: each search the agent ran, each page it opened and each page it cited is recorded, and each sentence of the answer is traced back to one of them. So a claim is either backed by something that agent just read, in which case the page that caused it is named and can be changed, or it is backed by none of them, which means it came from the model or from something cached, and that is a different problem with a different fix. Change the page or the outside source, ask the same questions again, and compare with the answers before. Answers vary between days, so a test predicts that fewer agents will say the thing rather than that one agent will say a given sentence.
A couple of minutes. Your first question is free and needs no account: 4 agents answer while you watch. The first read of a paid account is 20 questions we generate for your company, which you can edit or replace with your own, each put to every model: 80 asks. After that nothing runs on a clock. Asks and tests run when you press, and new insights keep arriving as those answers land.
Sometimes the very next ask, sometimes not. An agent that opens the changed page can pick it up immediately; a claim that came from an outside site, or from something cached, moves when that source moves. That is why a change is a test rather than a fix: you re-ask the same questions on the same models and compare with the answers before, instead of waiting and hoping.
Paid plans are counted in asks. One ask is one question put to one model, so a question answered by all four models is four asks; a test re-asks only on the models it needs. A monthly plan is $99 for one company, with 160 asks a month: the setup questions put to every model, then the asks spent when you change a page and ask the same questions again. A shared plan for a team is $259, with 320 asks a month. The team plan starts with 7 days free and no card.
At Sheertex, I lived in the dashboards. We grew to tens of millions in revenue and raised hundreds of millions, and I constantly changed the pages that shaped what people believed about us: the homepage, product pages, checkout, investor decks, careers pages, press materials.
If a buyer, investor, candidate or partner was forming an opinion of the company, I wanted to know what they saw and what moved them.
Today, a lot of that happens before anyone reaches your site.
A buyer asks ChatGPT or Claude what to use. The agent reads your pages, competitor pages and outside sources, then hands back a recommendation. Nothing appears in your analytics. Soon the buyer may never visit at all: their agent will do it for them.
I originally built Oomira because I wanted my own AI agent to get the facts about me and my company right. Then I realised the bigger problem was everyone else’s agents getting you wrong, in front of your buyers, with no way to see why or test a fix.
Oomira is the same obsession with the funnel, pointed at where the funnel is now.
Katherine Homuth, founderPreviously founded Sheertex, Female Funders and ShopLocket.Your first question is free. No account. Two minutes. Then 7 days of Teams free, no card.