Mimos wants your firm quoted by the machine, not buried on Google

Ask ChatGPT which personal-injury lawyer to call, or which financial advisor handles inherited retirement accounts, and you get one answer. Not ten blue links. One paragraph, one recommendation, one firm quoted by name. For twenty years the game was ranking on a page of search results. The game Mimos is playing is different: being the sentence the AI says back.

Mimos is a Y Combinator Summer 2025 company built around a single wager - that the shift from search boxes to answer engines is real, permanent, and most valuable in the industries where trust is hardest to earn. Its founders call the category Answer Engine Optimization. Think of it as SEO's successor, aimed not at Google's results page but at the models people now ask directly: ChatGPT, Perplexity, Gemini.

01Compliance stops being a tax

Here is the counterintuitive part. Regulated firms - doctors, lawyers, wealth managers - usually treat compliance as the thing that slows marketing to a crawl. Every blog post waits in legal review. Every claim gets lawyered. Mimos argues that same paperwork is an asset when an AI model is deciding whom to trust. A model wants verifiable authority. A firm bound by regulation already produces it.

So the pitch inverts the usual complaint. The disclosures, the fact-checks, the citations that make compliance slow are exactly the signals that make content credible to a language model. Mimos's job is to get a firm from raw expertise to reviewed, citation-ready content without the weeks of back-and-forth in between.

It helps to be precise about what an answer engine rewards. When a model composes a reply about a health, wealth, or legal question, it leans on sources it can treat as authoritative and specific. Vague marketing copy is easy to ignore. A clearly sourced explanation, written by a firm that is on the hook for what it says, is harder to dismiss. Regulated firms produce that second kind of material by obligation. The problem was never a shortage of expertise. It was the distance between that expertise and anything a model could actually read.

02What the platform actually does

Mimos tracks the high-intent prompts prospects type into AI engines - the real questions before a purchase - and watches who gets cited across those engines, competitors included. It then generates compliance-aligned first drafts, bundles the disclosures and fact-checks a reviewer needs, and measures share of voice: how often the model quotes you versus everyone else. The stated result is a path from insight to visibility measured in minutes rather than weeks.

Read as a sequence, the platform maps to how a marketing team already works, just faster. First, it defines who the firm is trying to reach and what expertise it can credibly claim. Then it listens for the prompts that matter, the questions prospects are actually asking a model rather than the keywords a tool guesses they might. From there it drafts, routes the draft through review with the compliance materials pre-attached, and finally reports back on whether the effort moved the number that counts: citations earned. Each stage is unremarkable on its own. Stitched together, they replace a workflow that usually lives in a dozen browser tabs and a long email thread with legal.

The number the founders point to most is a claimed doubling of AI citation rates against baseline content. It is an early figure from a young company, and worth reading as directional rather than settled. But it captures the shape of the promise. The goal is not more pages or more clicks. It is a higher chance that when a model answers, it answers with your name attached.

03It didn't start here

Mimos began pointed at a single, brutal market: plaintiff-side law firms. Those firms compete in some of the most expensive keyword auctions anywhere, and the path from a click to a signed retainer is a mess of disconnected intake tools, slow callbacks, and spend data that never ties back to which case actually signed. The first Mimos was a growth operating system for exactly that - managed acquisition through Google Ads and Local Services Ads, an intake CRM, follow-up automation, and attribution linking dollars to signed cases.

That work taught the founders how regulated marketing really breaks. The broader product - Answer Engine Optimization for any regulated firm in health, finance, or legal - grew out of it. Same insight, wider aperture: expertise exists, but it stays invisible in the places customers now look.

There is a useful lesson in the move for anyone building in a narrow vertical. The law-firm product forced the team to sit inside the messiest part of a regulated business - the moment a lead turns into a paying client - and watch where trust and attribution leak out. The AEO product is that same wound, treated one step earlier: before a firm can convert a lead, the prospect has to find it, and increasingly they find it by asking a machine. Starting narrow gave Mimos a specific problem to understand. Widening let it sell the understanding to more than one kind of firm.

04Two builders, one very long collaboration

Rohit Sirosh, the CEO, spent his Microsoft years architecting Azure's high-throughput storage - the infrastructure that supported OpenAI's training workloads for the GPT-series and Sora models. There's a neat symmetry to it: he helped store the data these models learned from, and now sells a product to help firms get quoted inside them. Away from screens, he's a competitive junior golfer, a three-time league champion.

Michael Korovkin, the CTO, led AI integration on Amazon Alexa and built petabyte-scale data infrastructure at Coinbase. The two are not new acquaintances who met at a demo day. They have been building projects together since the sixth grade - roughly two decades of shipping things side by side before they got to this one.

The pairing matters more than the logos. One founder spent years on the plumbing that makes large models possible; the other on the consumer surfaces where people actually talk to machines. Answer Engine Optimization sits precisely between those two worlds - it is a data-and-infrastructure problem dressed as a marketing one, or a marketing problem that only yields to people who understand how the models underneath behave. A team that has argued through hard technical calls together since middle school is, at minimum, unlikely to fall apart over a product roadmap.

05Where Mimos sits, and who it's up against

On one side sit the incumbents: law-firm and finance marketing agencies that sell SEO, ads, and content the old way. On the other, a fresh crop of AEO and generative-engine tools racing to measure and influence AI citations. Mimos's wedge is the pairing few of them commit to fully - a narrow focus on regulated industries, with compliance review built into the content workflow rather than bolted on after.

The early customers cluster where you'd expect the anxiety to be sharpest: finance, proptech, wealth management, with a pipeline reaching into health and investing. These are teams that can already feel their prospects asking an AI instead of a search bar, and would rather be the cited answer than the ignored link.

The timing is the interesting part. Category creation usually means educating a market that doesn't yet feel the problem. Mimos is doing something closer to the opposite - arriving just as the behavior it's built for goes mainstream. Anyone who has watched a colleague ask a chatbot for a recommendation instead of opening a browser tab understands the shift without a slide deck. The open question is not whether people will ask machines for answers. It is whether firms will pay to influence those answers the way they once paid to influence a search ranking.

If there is a single idea worth taking from Mimos, regardless of whether you ever become a customer, it is a free diagnostic. Open ChatGPT and ask it the handful of questions your best customers ask right before they decide to buy. Read the answers closely and notice who gets named. The gap between what the model says and what your firm actually knows is the exact space Mimos is trying to sell into. Seeing that gap for yourself costs nothing but a few minutes and a little discomfort.

Whether Answer Engine Optimization becomes a durable category or a phase of the current AI moment is still unsettled. What's clear is the shape of the bet: pick the customers with the most verifiable expertise, remove the friction between that expertise and publishable content, and measure success by how often a machine repeats your name. For a two-person team a year into the work, five paying customers and a $500K seed is a start, not a verdict.