Everyone has access to the same models. The edge is your team’s domain expertise. We capture it through structured interviews with your leaders and SMEs, codify it into skills your AI can use, serve it inside the stack you already run, and keep it current as it decays.
Real estate credit fund. A multifamily deal projects 5% annual rent growth to perpetuity
Screen this deal.
Both outputs come from the same model reading the same material. The difference is not more data. It is knowing how to evaluate what is already there: which assumptions hold in your market, which words fail with your audience, what this buyer actually scores. That knowledge came out of an interview with the person who owns it, and it now runs every time.
Enterprises are consolidating AI onto in-tenant platforms because permissions decide everything: Copilot inherits the document security you already have, and every outside platform starts a procurement fight. That is settled, and it is the right call.
Every department wants to do more with AI. They get access to the models and then they get mediocre output, because Copilot over your SharePoint gives you a generalist intern with keys to the filing cabinet. It has read everything and knows nothing that matters: whether a 5% growth assumption holds in this region, which words fail with hourly employees, what this buyer’s committee actually scores. That is not a data problem. It is a judgment problem.
IT can build an agent; they can’t specify one for a pursuit team, because they’ve never sat in a finalist meeting. Citizen developer programs stall on the same gap: the scarce skill was never prompt writing, it was knowing what the workflow should be and what the answers must contain. 25 years of knowledge-management tools changed nothing, because the constraint was never technology. Experts do not have time, cannot articulate what they know without skilled probing, and nobody inside owns keeping it current.
The models are the same everywhere. The expertise is not. We extract it, codify it, and overlay it on the AI you already run, so it stops answering like the internet and starts answering like your best people.
4 steps. None of them requires access to your documents: the interviews happen with your people, and the codified expertise is served back into your existing stack.
The intersection of what is valuable to your department, what LLMs are good at once they know what you know, and what is tacit: the expertise that lives only in people’s heads, where RAG cannot reach. Out of that comes the scope, the interview plan, and the objective function the engagement is measured on.
Structured interviews anchored on specific past decisions, with follow-ups that chase every adjective to a number. The signature move: we show each expert what the model would have said and ask what they would change. Reaction extracts in 90 seconds what a blank page never does.
Structured instructions and skills, each carrying its owner, approval date, freshness score, and source. Formatted so your AI can ground on it and your compliance team can audit it.
A Metis connector your teams enable in Copilot or whatever you run, hosted by us or deployed inside your tenant. Each department gets the right skills, updates propagate, and a dashboard shows what gets used. Pricing answers rot in weeks, process answers in quarters, philosophy answers in years. Everything we deliver decays on a known schedule, and we maintain it.
Before the first interview, we write down the outcome your department is judged on, a proxy we can measure inside 90 days, the out-of-the-box baseline versus the grounded target, and the guardrails that must never be violated. The gap between baseline and target is the Metis delta, and the delta is the product.
Maximize the delta between grounded and ungrounded output on your proxy metric, subject to the guardrails, inside the pilot window.
Interview agents will be free within 2 years, and capture pipelines already are. None of that touches the reasons expertise stays in people’s heads: experts have no time, cannot articulate what they know without skilled probing, will not sit for anyone without external standing, and nobody inside owns keeping knowledge current.
What compounds instead: the questions that extract depth in each function, learned across engagements; a schema for expert knowledge that carries owner, freshness, and lineage; the maintenance relationship, because stale expert answers served confidently by an AI are worse than none; and the record of where generic models are wrong in your domain, which grows with every interview.
| Asset | Commoditizes? |
|---|---|
| AI interviewer as a capability | Yes, 1 to 2 years |
| Transcribe, extract, structure pipeline | Already has |
| Question architectures per function | No, they come from engagement history |
| Maintenance and decay governance | No, decay never stops and nobody inside owns it |
| Where models are wrong in your domain | No, it accrues with every interview |
Buyers do not buy an engine; they buy the answer to their function’s problem. Each vertical is the Method applied where judgment is most measurable, with its own name and its own front door.
For enterprise pursuit and revenue teams competing through RFPs and finalist meetings. The expertise of your best pursuit people, your SMEs, and your buyers, extracted and served into the AI your team drafts with, so finalist answers and RFP responses read like the people who win, not like the model.
boostwinrate.comFor investment firms: private credit, buyout, growth, and select hedge fund strategies. How your senior people actually evaluate deals, encoded as a House View File your firm owns and loaded into the stack you run. Heuristics, never holdings.
houseviewhq.comInternal communications, partner management, underwriting, servicing, implementation: every function where the AI stack behaves like an intern and 2 people hold the judgment. We scope the next vertical with the leaders who live it.
Propose a function30 minutes. Bring the 3 questions your best person answers better than anyone else in your function. We show you what your AI says today, and what it says with your experts’ judgment loaded.
Metis is run by a founder with 14 years producing high-stakes presentations for Fortune 500 pursuit teams, a CFA charter, a Harvard MBA, and prior years across investment banking, private equity, and hedge funds. Interview content is covered by NDA and written data-handling terms; experts approve their own answers before anything ships; for firms whose judgment cannot leave their walls, the connector deploys inside your tenant. The name is the Titaness of wise counsel whom Zeus swallowed to keep her judgment permanently inside him.