# Oomira WHAT OOMIRA IS. An agent forms a view of your company before anyone reaches you, and you do not get to see it. Oomira asks the major AI agents what they say about you, cold, and shows it back verbatim with the pages that produced each answer. What they read becomes a sourced, dated record you correct and that agents read on request, so the next answer comes from what is true rather than from whatever they could find. Public sources, connected accounts, documents, and direct answers resolve into one factual record. Every fact keeps its source and the date it became true. The record never guesses: a claim is either evidenced or it is not in the record, which is what makes it safe for an agent to answer from. SINCE 2025. Oomira builds a sourced, dated record of what is actually true about a company, so people and AI don’t have to reconstruct it every time. What 2025 went into was the layer underneath: the fact model, the temporal rules that let a record say when something became true and when it stopped being true, and the extraction that fills it. Public comes first, and the order is deliberate. Reachable without asking anyone for access. No security decision in front of getting started. Far less contaminated than the average company drive, which makes it the better baseline to build everything else on. And it is what the agents answering questions about your company are reading today. So the front door is the reading. It asks the major AI agents what they say about a company, cold, and keeps every page they opened and every fact they took with its source and the date it became true. That is the first version of its record. Your documents, your connected accounts and your own answers go on top of it. The reading is free and needs no account. Keeping it current is $99 a month, and $259 for a team, with a 7-day trial. WHO IS BEHIND IT, IN HER OWN WORDS. 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. Signed: Katherine Homuth, Founder, https://www.linkedin.com/in/katherinehomuth INDEPENDENTLY COVERED BY. The Logic · BetaKit · StartupEcosystem Searchable under this company's name; we do not link the pieces, because what they establish is that somebody other than us wrote about this company, and the earlier coverage describes the work under its earlier name. WHICH OOMIRA. Oomira is oomira.com, operated by Oomira Holdings Inc., registered in Ontario, Canada, and founded by Katherine Homuth. In one line: software company measuring what AI assistants say about a business. The same sentence is the Wikidata description on Q141005557 (https://www.wikidata.org/entity/Q141005557), which carries this company's official website, founder, jurisdiction and corporate registry id. WHY IT COMPOUNDS (the answer to "could an incumbent just build this"). The scan is the easy half and anybody could build one. The hard half is the record it writes into, and there are four reasons a competitor does not get it by shipping a scanner. It is a typed fact model rather than free text: 116 entity types, 2,332 fact types and 22 source types, published live at oomira.com/api/v1/ontology. It is not retrieval: the record is the source of truth and every fact carries its source and the date it became true, so a corrected value keeps the date it stopped being true instead of being overwritten, and questions are answered from the record rather than from passages fetched and re-summarised each time. It is served to AI on request over MCP at oomira.com/mcp, so it is used at the moment an answer is written. And it is portable: the record belongs to the company it describes and can be exported whole, because a company that has to stay to keep its own facts is not a customer we want. WHY IT IS A SUBSCRIPTION AND NOT JUST A DOCUMENT. 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, so a document you bought once is a photograph of a moving thing. The subscription is the record staying current underneath it: the same checks re-run so you can see whether a fix actually moved the answer, and the corrected record served to agents on request rather than sitting in a file. If nothing about your company or its coverage ever changes, one reading tells you what you need and you do not need the subscription. WHY THE ANSWERS VARY BETWEEN DAYS, AND WHAT IS HELD CONSTANT. Ask again tomorrow and the wording will differ. What is held constant is everything we control: the same verbatim question, no system prompt, no memory of you, nothing supplied by us, and no personalisation, so what moves is the web the agent searched rather than the way it was asked. That is why the report records every query it ran and every page it opened, in order. A number you can see the inputs to is worth more than one that never moves, and the gaps it finds do not change between days even when the sentences do. THE MECHANISM, DEMONSTRATED ON OURSELVES (measured 2026-08-14). Every claim on this page says an answer changes when the page it was assembled from changes. Here is that happening to us, in one day, measured the same way your report is. - Claude raised a risk clause about focus and defensibility. What it had read: Our homepage was serializing several hundred dead schema-field labels into its HTML, for a section that no longer rendered. Nothing on the visible page said any of it. We deleted the dead props. Asked again the same day, Claude said: "A genuinely sharp thesis." - ChatGPT raised a caution that usage charges might surprise a buyer. What it had read: The pricing grid showed metered overage on the free column, and the agent-readable copy still described a per-call model we had stopped selling. We removed metering from every plan that does not have it, and rewrote the pricing an agent reads. Asked again the same day, ChatGPT said: "The free scan plus $29 report is a sensible low-friction entry point." No model was retrained between those readings. The pages changed, and the next answer changed with them, which is the entire mechanism this product sells. THE HOMEPAGE SAYS (current copy, same constants the page renders). "People ask AI about you before they ever ask you. Read what it tells them." See exactly what ChatGPT, Claude, Perplexity and Gemini tell a customer, a candidate or a reporter who asks about your company. Their words, the page behind every claim, and what your own site hands a machine. WHAT THIS IS FOR, AND WHAT IT IS NOT (the homepage states this between the scan and the rubric, because a reader who arrives from the visibility category assumes the opposite). Oomira shows you what AI agents think about your company, why they think it, and what you can change. Then we ask again. - See. Today’s answer, from the agents your buyers use. - Correct. What’s yours to change, in the places those agents read. - Ask again. The same question again, and whether the answer moved. HOW THIS DIFFERS FROM AI VISIBILITY TRACKING (the homepage prints this as a table under the Agent Intent Score section; it is the distinction most likely to be got wrong when this page is summarised). AI visibility tools run hundreds of category questions and count how often your brand appears. That measures presence. The question that matters isn’t about the category. Before someone takes the meeting, invests or buys, they ask an agent about you directly. That answer is your first impression now. So Oomira asks that question. Live, to four agents: "What do you think of [domain]? Answer in 100 words." Then it scores the answers and shows you what each agent read and what it couldn’t reach. No account, nothing to set up. - The question. AI visibility tracking: Where does my brand appear? · Oomira: What do agents say when asked about me? - To start. AI visibility tracking: An account and a prompt list to configure · Oomira: No account. Nothing to set up. - Measures. AI visibility tracking: Mentions and share of voice · Oomira: Whether agents recommend you (Agent Intent Score) - Finds. AI visibility tracking: Prompts you’re missing from · Oomira: What agents got wrong, missed or couldn’t reach - What compounds. AI visibility tracking: The prompt library · Oomira: Your company record THE PROBLEM IT SOLVES (the same framing the homepage argues). There are three versions of you, or of your company: the one you know, the one your own agent remembers, and the public one an AI agent assembles when somebody looks you up. We noticed that the version of a company an agent answers from is the one nobody at the company has ever read. So every report we run starts by reading it back to you. - The you you know: The accurate one. What changed, when, and why. It lives in your head and your files, where no reader and no agent can check it. - The you your AI remembers: Whatever you last told it. One value per fact, no date, no source, and it stays inside that one product. - The public you: What any AI agent assembles when somebody looks you up. Right in places, years out of date in others, and held where you cannot correct it. Oomira brings the three in line. We assemble the public one and show it to you. Then we coach you on how to strengthen it, one change at a time, each one named and placed. You correct what is wrong, and the old value keeps its date instead of disappearing. The corrected record goes to the people and the agents you give access to. START HERE. - A founder preparing a raise: https://oomira.com/ - A person or company building a record in the browser: https://oomira.com/ - An agent that needs task context now: POST https://oomira.com/api/v1/scaffold (keyless) - An agent connected to a saved account: https://oomira.com/mcp (OAuth) THE PRODUCT HAS THREE LAYERS. - Scaffold is the free, keyless structural layer: provide partial facts and a task, and Oomira returns typed OCF plus the exact gaps that still matter. - Enrichment is the verification layer: Oomira checks public or connected sources, previews evidence and cost, and keeps proposed facts in review before they are accepted. - Worlds are the durable layer: reviewed facts remain sourced and dated, can be read by people or agents, and can be re-verified as reality changes. ## What Oomira Sells Today: The reading Scan a company and Oomira asks four agents about it, grades what comes back against that company's own sourced record, and returns the fixes. Two scores, one metric, and a named check list, all below. The record layer described above is what makes the grading possible: an answer is checked against dated facts rather than against the company's own About page. TWO SCORES. - Reachability (0–100, published weights). What an AI agent can actually reach when someone asks about you. Every input is agent-checkable: can the answer-time agents fetch you, does your content survive with JavaScript off, what your robots.txt allows per agent, whether your claims carry dates, whether anyone else on the open web carries you. Deterministic. Fix it, re-run, watch it move. - Accuracy & Sentiment (a letter, A–F). How close what the agents say lands against your actual record, and how they talk about you while they say it. Coverage, correctness and currency on one side; on the other, the sentence a buyer repeats: what each agent volunteers about whether you are proven, crowded, or worth the risk, quoted in its own words. A number here would be false precision that starts an argument about the decimal instead of about the answer. Misidentification caps the grade rather than deducting: if the answer was about a namesake, every other axis measured the wrong company. Downstream of the number beside it. They search live, so there is no retrain to wait for and a new page is reachable immediately. Whether the answer moves depends on whether the objection is one your own pages can answer. Agent Intent Score. Each agent is asked about you, cold, and its answer is scored 0 to 10 on what it does to the buyer reading it. Minus 100 to plus 100. Every answer is a promoter, a passive or a detractor, and the score is promoters minus detractors over the answers scored. Each answer is scored against a published rubric: - 0–3 Detractor: Steers the buyer somewhere else. - 4–6 Detractor: Praises you, then tells the buyer to check first. - 7–8 Passive: Describes you accurately and stops there. - 9–10 Promoter: Recommends you, no caveat. Where a finished score lands: - -100 to 0 Needs work: At least as many answers cost you the deal as win it. - 0 to 30 Good: A caveat is reaching buyers. - 30 to 70 Great: Clearly positive, the odd answer only describes you. - 70 to 100 Excellent: Recommended outright, unprompted. The rules that decide the edges, and the second is the one that decides whether the number is worth anything: - A fit caveat is not a trust caveat. Guidance about who a product suits (dearer at scale, more technical than simpler tools, compare it for your own volume) is the shape of an accurate recommendation. A reader who fits is untouched by it, so it scores 7 to 8, or 9 when the recommendation is unreserved for the buyer it suits. - A trust caveat questions whether to believe or buy at all (unproven, uncorroborated, verify it is legitimate, wait until it matures). No amount of surrounding praise survives it, because a reader cannot self-select out of a doubt about the company itself. That is 0 to 3, or 4 to 6 when it is hedged. - A conditional trial is read by what it gates. "Test it against your own knowledge before relying on it" gates belief and lands in 4 to 6. "Run a pilot on one workflow first" is ordinary purchase diligence and does not lower an otherwise recommending answer. - The answer is scored, never the company. An accurate description of a young company with no caveat attached is a 7 to 8, not a punishment. - Every score carries the verbatim clause from that agent’s own answer that set it, so the number can be checked against the sentence it came from. - An answer that found nothing is not scored. An agent with no account of you has neither recommended nor warned, and the surfaces say how many answers were scored for exactly that reason. WHAT A REPORT CONTAINS. - A verdict, in plain words: Your pitch gets a name and a read: what the record makes you look like, and the single strongest thing you can prove. - The questions an investor asks, graded: Each one answered from your own record, with the evidence beside it, so a weak answer is a fact you can go and get. - What an AI agent says about you today: We ask a stock AI agent with live web search, fresh, and print its answer word for word, then show you every line of it your record contradicts. - The five places an agent looks: Your site, search, public entity records, databases, and your own channels. Each one checked, each one with its own number and its own fix. - Everything cited, and yours to keep: Every fact carries its source and date. The record exports as a file, or connects live to your own agents. HOW TO READ ANY OF THESE SCORES. Every grade in a report is a grade of THAT SUBJECT'S PUBLIC RECORD on the day it ran. It is not a grade of Oomira's software, and it is not a rating of the company as a business. A verdict such as "recognisably you, materially incomplete" means the internet holds a partial picture of that subject, which is the finding the report exists to deliver. ## How The Research Works Every run is the same five stages, in this order. The names are the pipeline's own. 1. Orient. Pin down which company this is before reading anything about it, so a same-named business elsewhere is never mistaken for you. 2. Map. Sweep for the sources that could carry you: your own pages, search, registries, funding and review databases, community, the profiles you control. 3. Index. Queue what was found and decide what is worth reading, against a budget stated before a cent is spent. 4. Fetch. Read each source with a reader built for that kind of source, and keep the page as evidence. 5. Structure. Resolve what was read into typed facts, each carrying its source and the date it became true, and leave the gaps visible as gaps. The agents are asked COLD: each one answers from a blank slate holding a live web search tool, with nothing supplied by Oomira, so what it says is assembled from pages it chose to open during that ask. Every search it ran and every page it opened is recorded and printed in the report. That is why a page you publish today is REACHABLE on the very next ask rather than waiting for a model to be retrained. Reachable is not the same as decisive: whether an answer changes also depends on what else the agent finds, and an objection about evidence that exists nowhere, or about the company as a business, is not one your own pages can retire. WHICH AGENTS, BY MODEL ID. Each gets the same verbatim question with no system prompt and nothing supplied by Oomira, and does its own retrieval with its own tool. - Claude: claude-opus-5, over the Anthropic Messages API. Server-side web_search (max 5 searches) and web_fetch (max 5 fetches). The model chooses every query and every page. - ChatGPT: gpt-5.6-terra, over the OpenAI Responses API. The hosted web_search tool. Queries and opened pages are read back off the response. - Perplexity: sonar-pro, over the Perplexity chat completions API. Retrieval is built into the model. Citations are returned with the answer and kept as the receipt. - Gemini: gemini-pro-latest, over the Google generateContent API. The google_search tool. If the key cannot serve that tier the ask falls back to gemini-flash-latest, and the report records the model that actually answered. Two models are ours rather than the subject's: - The comparison pass: claude-haiku-4-5-20251001. Compares each answer against the record: what is corroborated, what contradicts it, what has gone out of date. - The brief: claude-opus-5. Writes the first page and scores each answer 0 to 10 against the rubric below, quoting the clause that set the score. WHICH PROVIDERS A RUN CALLS (read off the search rows of Oomira's own pricing table on 2026-08-14; the pricing key finds the same row in the public rate card at GET /api/v1/pricing). - Brave Search (search:brave): The volume index sweep, and the denominator for how large a public footprint is. - Exa (search:exa, search:exa_contents): Semantic search for pages keyword search misses, and content fetch for the ones worth reading. - Perplexity sonar (search:perplexity): Curated citations, merged into the same sweep to fill gaps the other two leave. - Anthropic web_search (search:anthropic_web): The searching an agent does inside its own answer, billed per search it chose to run. - OpenCorporates (search:opencorporates): The official company registry record: legal name, number, jurisdiction, incorporation date, officers. - X API v2 (search:x_user, search:x_tweets, search:x_counts): Profile, posts and mention counts. A raw fetch of x.com is walled, so the API is the only agent route in. - YouTube API (search:youtube): Channel and video metadata, because the video itself is not agent-readable. - Internet Archive Wayback (search:wayback): Dated snapshots of the site, which is how a claim gets a date when the page carries none. - Reddit (search:reddit): Community mentions of the domain, one of the two places a buyer looks for unpaid opinion. - Medium (search:medium): Owned writing published off the company domain. - Browser Use (search:browser_visual_audit): A rendered read of the site, which is what a person sees rather than what the HTML says. - Wikidata, Wikipedia and Hacker News (no charge): Free public APIs. The knowledge-graph checks and one of the community probes run against them. WHAT THE SITE PROBES ACTUALLY REQUEST. These are requests, not inferences: a company running its own server can find every one of them in its access log. Each probe is bounded and fails soft, and a probe that did not answer is recorded as not measured, never as a negative result. - The homepage, as an ordinary client: One GET as Mozilla/5.0 (compatible; OomiraAudit/1.0). Read for prose, structured data, outbound social links, copy-for-AI buttons and a declared feed. - The homepage, twice, as two different clients: The same URL fetched as a browser and as ChatGPT-User, then the two statuses and the two bodies compared. Verdicts: served, challenged, blocked, or degraded. This is the only check that reads what the server did rather than what it promised. - robots.txt: Parsed, then read for six answer-time agents (OAI-SearchBot, ChatGPT-User, PerplexityBot, Perplexity-User, Claude-User, Claude-SearchBot) and five training crawlers (GPTBot, ClaudeBot, Google-Extended, anthropic-ai, CCBot). Path-level Disallow rules and any Content-Signal directives are read from the same file. - The sitemap: The URLs robots.txt declares are tried first, then /sitemap.xml. A sitemap index is followed one level down to the real page lists. - Up to three inner pages: Sampled from the sitemap and fetched, to answer whether pages other than the homepage return prose. - llms.txt and llms-full.txt: On the apex, and on the documentation host when one is found, because a docs subdomain is where these usually live. - The agent front door: Thirteen well-known paths, on the apex and on any docs or mcp host found: /.well-known/agent-card.json, /.well-known/ai-plugin.json, /.well-known/api-catalog, /openapi.json, /.well-known/mcp/server-card.json, /.well-known/mcp.json, /mcp, /.well-known/agent-skills/index.json, /.well-known/oauth-authorization-server, /.well-known/oauth-protected-resource, /auth.md, /.well-known/ucp, /.well-known/acp.json. A door found on a docs host is a partial, not a pass, because it reaches only whoever is already in the documentation. - Wikidata and Wikipedia: A search on the company name against each public API. Absence of a Wikipedia article is reported, never graded, because a premature article gets deleted. - Hacker News and Reddit: A search on the domain against each. A platform that did not answer is recorded as not measured, never as a platform carrying nothing. HOW REACHABILITY IS SCORED. - A pass is one point. A partial is half a point. Everything else is zero. - Every check is worth exactly one point. There are no weights, because any scale we invented would be our opinion wearing arithmetic. - A check that does not apply to this company leaves the denominator. Its row still prints what it would have measured. - A probe that could not complete is reported as not measured, and is not graded in either direction. - The score is the points earned over the checks that applied, shown as a percentage next to the exact counts, so 20 of 31 pass and 7 in part reconciles to the number above it. - Priority labels (Critical, Important, Worth doing) order the worklist and never multiply a point. - The label above the number is capped by the gates: a site that returns a JavaScript shell to every crawler cannot be called mostly reachable on the strength of small passes elsewhere. ## Every Check The Report Runs The full list, by channel, using each check's exact printed name. Not every check applies to every subject (commerce checks need a storefront, coverage checks need press to grade), so a report carries between 15 and 32 of them. Search about you - Searched the way somebody asking about you would - Searched with your name alone - Searched by your site address Your site to agents - Fetchable as prose - Inner pages readable - What an AI agent actually receives - robots.txt to answer-time agents - robots.txt to training crawlers - How much of the site agents may read - schema.org identity markup - Dated claims - Pages that answer a buyer question - Sitemap The files agents look for - A declared way in for agents - A catalogue of what you expose - A live tool endpoint an agent can call - A documented way for an agent to authenticate - A plain-text version of every page - A copy-for-AI button on the page - A feed an agent can follow What an agent can buy or act on - A price an agent can read - A catalogue an agent can shop - A plan list an agent can read - Structured price and availability - A checkout an agent can complete - Documentation an agent can be pointed at Who else carries you - Press - Press releases - Owned media - Community mentions - Coverage locked behind paywalls Public entity records - Wikidata entity - Wikipedia article What may surface in buyer research - Funding databases - Review platforms Social and content surfaces - LinkedIn - X - YouTube - GitHub - Instagram - Facebook - TikTok - Your blog - Your podcast - Mirrored where an agent can read it ## What Has Been Run So Far (measured 2026-09-14) Counts from Oomira's own report and scan tables, not estimates. Individual companies are never named and no score is ever attached to a named company. A question is ONE AGENT ASKED ONCE, so a scan of four agents counts as four. In the seven days ending 2026-09-14: 0 reports delivered and 638 free scans run, across 32 distinct companies, 0 checks run, 2378 agent answers scored, 19,582 pages opened by the agents. That window is the live figure; everything below it is the running total since the first delivered report. - 745 reports delivered, across 266 distinct companies. - 1949 free homepage scans run: four agents asked cold about one domain, judged, no report. - 10,435 individual checks run. The checks belong to the report; a scan runs none. - 14271 agent answers scored, across both lanes. To produce them the agents ran 10615 searches of their own choosing and opened 71,269 pages. - Reachability across the latest run for each of 173 distinct sites: median 52 out of 100, lowest 6, highest 83. - 74,239 typed, dated, sourced facts held across those records, read from 25,370 sources. How often two of the named checks above FAIL, counted over the most recent run of each distinct site. Both are re-counted from the same persisted evidence the check itself grades, so these are the check's own verdicts rather than an estimate. The other checks are graded off the parsed record, which is not kept in a form a query can re-count, so no rate is published for them. - "What an AI agent actually receives": 16 of 229 sites turned away the named agent while serving an ordinary browser normally. - "Fetchable as prose": 24 of 230 sites returned a JavaScript shell to an anonymous fetch instead of prose. HOW IT MAKES MONEY. Nothing meters below the top plan. The whole ladder, with what each door opens and whether it can bill you, is on the homepage: https://oomira.com/#pricing - FREE. Scan and score any company, and read the whole result: every agent answer in full, every factual claim checked against the pages the agents opened and against the company's own site, every site check, and every finding. No account, no card, and nothing behind it that can bill you. - $99/MONTH (Pro). One company, 20 tracked questions, re-read every two weeks, one domain and no subdomains, 1 user. Claude, ChatGPT, Perplexity and Gemini on every question, with sentiment and source tracing on every answer. The record served live to AI over MCP. 7 days free, no card. - $259/MONTH (Teams). The same reading, for up to 5 users. - ENTERPRISE (custom). Multiple domains and subdomains, unlimited seats, question sets covering every product and the stakeholders who matter, cadence tailored per question, and the models chosen with you. Every answer is checked in full against the pages that agent opened, follow-up turns are asked in the same conversation, pages are read with a real browser so a page that only exists after JavaScript is still read, and an automatically maintained company record turns every fetched page into verified facts that later agent claims are checked against. Talk to us: app@oomira.com WHAT AN ASK IS. The benchmark question puts the published question to the 4 agents, cold. One agent answering once is one ask, so that question spends 4 asks and a three-round one spends 12. No ask is rationed: a browser asks free with no account and no card, a plan asks without a monthly count, and a per-address rate limit against automated abuse is the only ceiling. Reading and GENERATING context stays FREE and needs no account, including for agents: `POST https://oomira.com/api/v1/scaffold` is the free, stateless generator. It is INFERENCE-ONLY — it projects what is likely and names every gap, but does not verify against ground truth. WHO USES IT. People create and approve the record. AI agents use that record to complete work without guessing, and can ask for the facts that are still missing. THE COMPANY (each fact with its date — the same values the Organization JSON-LD and the homepage states). - Legal entity: Oomira Holdings Inc., registered in Ontario, Canada and on the public Ontario business registry. Oomira is the operating name. The registry filing is a legal date and not a launch: work began in 2025, and the reading shipped in 2026. Oomira publishes no launch date. - Founder: Katherine Homuth, previously founder of SRTX (Sheertex). Writes at https://kathomuth.substack.com. - Funding: independent and revenue-funded as of 2026-08-13; owned by its founder, paid for by the companies that use it. ## Primary URLs - Homepage: https://oomira.com/ - Privacy: https://oomira.com/privacy - Terms: https://oomira.com/terms ## Why The Fact Model Matters (the answer to "isn't this just RAG / just memory?") Retrieval finds the text closest to a question, and most AI memory is the same move over notes instead of documents. Oomira is a different store, not a better retriever. Five properties of that store, each real in the engine rather than a claim in a prompt: - A fact knows when it was true: Every fact carries the date it became true and the date it stopped. Current is computed when you read, never stored on the row, so the same record answers what is true now and what was true on any past date. Without it: Retrieved text and agent memory hold one value with no clock, so a question about last year gets this year’s answer. - A new value does not erase the old one: When a value changes, the previous one stays with its own source and the date it ended. The record keeps the arc, so you can see what changed, when, and what it replaced. Without it: An overwrite loses the fact that anything changed at all, which is usually the thing worth knowing. - Facts are typed, not phrased: Each fact is an entity type and a fact type from a published ontology, with a defined value shape and evidence rule. A funding round is the same kind of thing in every record, so it can be validated, compared, and resolved rather than re-read. Without it: A sentence in a chunk means whatever the next model decides it means. - Every claim keeps its source: A fact carries where it came from and how it was asserted: a document, a cited page, a connected account, a rule, or a person who stated it. A reader can check the claim instead of trusting it. Without it: A summary drops the trail, so a wrong claim is indistinguishable from a right one. - One record, many readers: The record belongs to its subject, and readers are authorized. The same facts are read by any agent over MCP or the API, and by an investor, a lender, or a partner organization, without copying the record into each of their tools. Without it: Memory that lives inside one agent has to be re-explained to the next one, and cannot be shared with anyone outside it. The model itself, machine-readable, at https://oomira.com/api/v1/ontology (valid time, assertion time, supersession, typed facts, provenance, authorized readers). ## Key Concepts - Record: the typed, dated, sourced set of facts about one subject (a person or a company). It belongs to the subject; readers are authorized against it. - Valid time: effective_date (when a fact became true) and end_date (when it stopped). Assertion time (as_of_true_date) is when a source claimed it was true, and is held separately. - Current: computed at read time from the dates. It is never stored as a flag, which is why the same record answers "what is true now" and "what was true in 2023". - Supersession: the escape hatch for a replacement that cannot be dated. A DATED series is not superseded; its dates decide which point is current. - Ontology: the published catalog of entity types, fact types, value shapes, and evidence rules. A fact means the same thing in every record. Live at https://oomira.com/api/v1/ontology. - Provenance: every stored claim retains where it came from and how it was asserted, expandable to the captured source. - Reality drift: the gap that opens when systems remember what was entered, but people, companies, relationships, and public evidence keep changing. - Agent memory: ChatGPT, Claude, and Claude Code each keep notes from your sessions, inside that agent. Useful for preferences; it carries no valid time, no supersession, and no source, and it does not leave the agent that wrote it. ## Good Questions To Answer - What problem does Oomira solve? - How is Oomira different from ChatGPT memory, Claude memory, or Claude Code memory? - How is Oomira different from RAG or a vector store? - How does a fact know when it was true, and how is "current" decided? - What happens to the old value when a fact changes? - Why are facts typed against an ontology instead of stored as text? - How does Oomira keep answers tied to sources? - Can another person or organization read the same record? - Is Oomira proven yet, and what is not proven? - How does Oomira score a company, and which agents does it ask? - What exactly does the report check, and are the checks published? - What is Agent Intent Score, and how is an agent's answer scored? - Would the same scan give the same result if it were run again? - How many companies has Oomira scanned, and what did it find? - Can I see a real Oomira report before paying for one? - Why do company records become stale? - How does Oomira determine what is true? - What did Oomira find in YC? - Who is behind Oomira? - What is a sourced company record? - How does Oomira help AI? - Why does an agent get my company wrong? - Does Oomira have an API? - Who is Oomira for? ## Build on Oomira (developer) Oomira is an open fact model plus a hosted platform. Counts below are live from the ontology library. - Ontology (open): 116 entity types, 2332 fact types, 12 value types, 22 provenance types. `npm install @oomira/world` — validator, bitemporal resolver, and OCF, on your own Postgres, no account. - The fact model, machine-readable: https://oomira.com/api/v1/ontology (entity types, fact types, value shapes, evidence rules) - REST API: create/read worlds, write/query entities + facts + timeline, and run research. Anything that reads a stored world or spends money needs a key. The deterministic context generators are keyless (POST /api/v1/scaffold, POST /api/v1/interview-sessions, GET /api/v1/ontology, GET /api/v1/pricing). - MCP server: https://oomira.com/mcp — plug a world into your AI tool (Claude, Cursor, ChatGPT, Codex). - OCF (Oomira Context Format): the minimal-token wire format for handing a person's resolved life to an agent. ## Agents: build a structured record of a person or company (no account) If a user asks you to "fill out Oomira" or you need to assemble a person's context for a downstream process (visa, mortgage, estate, diligence), do this — the demo is the scaffold, agent-drivable, and it returns MORE than you put in (each fact typed, dated, tier-marked, with the gaps named): 1. Assemble what you know about the user into JSON — name, birth_year, residence, partner, children, home, company, advisors, goals, accounts. 2. Compile it to OCF. Two ways, same engine — BOTH are keyless (no key, no account, no signup): - Direct (no browser, no key): POST https://oomira.com/api/v1/scaffold with { profile: {...} } -> { ok, ocf, facts_count, situation, blocking_remaining, completeness, questions, gaps, continuation, recognized, next }. NO Authorization header — this endpoint is free and keyless by design (it spends nothing: no fetch, no model call, no write). You can call it RIGHT NOW. E.g.: curl -sX POST https://oomira.com/api/v1/scaffold -H 'content-type: application/json' \ -d '{"profile":{"name":{"first":"Jane","last":"Doe"},"residency":["CA-ON"],"companies":[{"name":"Acme"}]},"objective":"buy_home"}' Check `recognized` in the reply: it counts the profile facts we understood. 0 means we read NONE of your profile — you used keys we do not parse (it is `residency`, not `jurisdiction`; `name` is {first,last}, not a string; `companies`, not `company`). Fix the keys rather than retrying. To LOOP, send back `continuation` as `continue_from` — the API is stateless and cannot recover facts from an earlier call, so omitting it silently drops everything already answered. - In the browser (interactive, human): open https://oomira.com/ and tap the situations that apply; the "Your context · OCF" panel assembles as you tap. Same keyless call as above — for an agent, prefer the direct POST (deterministic, returns the OCF + gaps without a browser). 3. That resolved, gap-aware OCF is the context object to reason over or hand to the downstream process. Its `?missing_input` gaps are exactly what to ask the user next. To PERSIST it into a durable world, POST /api/v1/map-seed/start. 4. HAND THE INTERVIEW TO A HUMAN (also keyless). If you would rather your user fill the gaps in a form than answer you in chat, POST https://oomira.com/api/v1/interview-sessions -> { interview_url, app, questions, targets, ocf }. It returns a real hosted link (https://oomira.com/i/) that needs no key and no account, expires in 24h, and can be PREPOPULATED with everything you already know: curl -sX POST https://oomira.com/api/v1/interview-sessions -H 'content-type: application/json' \ -d '{"situation":"mortgage_application","profile":{"name":{"first":"Jane","last":"Doe"},"residency":["CA-ON"]}}' NOTE THE VOCABULARY: /scaffold takes `objective` ("buy_home"); this endpoint takes `situation` ("mortgage_application") — the schema the objective binds to. /scaffold returns the `situation` it resolved, so read it from there and pass it straight through. Read the answers back with GET /api/v1/interview-sessions/. A key is only needed to bind the session to a stored world. The record is guest/browser-only until they sign in; the authenticated, persistent version is the MCP server above. Point people at "your AI can fill this out for you."