judgment as owned infrastructure
Tom reads the decisions your best underwriters have already made and recovers the judgement behind them. The model is yours*
Under the desk today. Plugged into your stack tomorrow.
* Tom learns only from your own underwriters' recorded decisions. It describes the judgement already in your book and attributes it to the people who made it. It doesn't invent risk appetite, doesn't score anyone, and doesn't replace the underwriter of record.
The model of how your desk decides. Owned by you.
Tacit captures how your most experienced underwriters actually make decisions, turns it into a structured model your institution owns and can interrogate, and serves that judgment to your people and your AI systems at the point of decision.
Decision capture
Tacit instruments the underwriting decision without changing how your people work. Decisions, prices, terms, and outcomes are recorded as a structured stream against the submission that produced them.
Deviation ledger
The single most important and least recorded act on your desk, turned into queryable data: every price away from the technical model, with the reasoning behind it.
Decline capture
The risks your underwriters walk away from are invisible to every pricing model. Tacit records the declines and their reasons — the refusals your actuarial function has never been able to see.
Referral memory
The reasoning exchanged when a junior escalates to a senior is, in most institutions, lost the moment the conversation ends. Tacit turns each referral and its resolution into a permanent node in your judgment model.
Judgment profiles
For each underwriter, a description of their expertise, drawn from their own decisions: where their judgement is most distinctive, which cues they weigh that the technical model has no column for, and where their reading differs from the rest of the desk. Differences get described, not graded. Every underwriter sees her own profile before anyone else does, and can pause capture at any time.
Practice model
How your institution actually decides — where your people converge, where they diverge, where collective practice has moved from the written guideline. Disagreement is preserved, not averaged away, because the disagreement is information.
Structured, not imitative
Not a black box that copies behavior. Every element is decomposable and attributable — an account of judgment you can defend to an examiner, transfer to a new practitioner, and interrogate yourself.
Decision support at the workbench
Your underwriters consult the model in the flow of work: the recommended action, the confidence your institution’s practice supports, the alternatives a dissenting senior would weigh, and the reasoning behind each.
Agent grounding
Your AI systems consult the same model through a programmatic interface at decision time — grounded in your institution’s actual practice, not a generic model’s defaults. Every response carries attribution, confidence, and provenance.
The self-writing file
The documentation your underwriters least want to produce becomes a byproduct they never write. From the captured decision stream, Tacit writes a rationale for every decision that a capacity provider or a regulator can read as it stands.
Bench development
New underwriters develop against the captured practice of your senior desk: shown a real case, asked to commit to a decision, then shown how your most experienced people would have approached the same risk, and why. Judgment transfers through structured practice, not proximity alone.
Deployed in your environment.
Governed to your regulators’ standard.
Built for the security, integration, and governance requirements of the world’s largest regulated carriers.
A house model that carries cues the table cannot.
over time, on your book, a model of your own experts can begin to outprice the generic industry model — because it carries the cues your actuarial table never had a column for. we treat that as a result to prove on your data, not a claim to make on a homepage.
and the underwriter whose judgment the model keeps reusing can earn from contributing it — a recognition mechanism a carrier chooses to run, not a feature we administer. it flips capture from something seniors guard into something they want to feed, which is the difference between a capture product that adopts and one that dies on the vine.
what Tom does not do: invent risk appetite, replace the underwriter of record, or claim a loss-ratio number before it has earned one on your book. the model is only ever as good as the desk it learns from — which is precisely why it is yours, and not ours.
A carrier that loses an underwriter loses a good employee. An MGA can lose the book.
Your capacity provider is, in the end, underwriting your underwriters. The binder gets renewed because the loss ratio holds, and the loss ratio holds because a few people keep making good calls that nobody wrote down. When one of them leaves, you have a conversation to have, and the only evidence you can bring is a results history that may not repeat.
A model of how your desk underwrites is the first asset that survives the person. It belongs to the MGA. Not to the underwriter, and not to us.
Is your desk a fit?
Four questions. If any one of them is a no, we’d rather find out now than three months in.
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Does your underwriter set the price, or work inside a matrix?
We read the distance between the technical price and the price actually written. If your underwriters can only move a rate up, or only inside a fixed band, that distance gets clipped at the edges and we can’t read it cleanly. The method needs full rate-setting authority.
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How many submissions does each underwriter see in a week?
Around five a week over two years is enough to work with. Much fewer and there isn’t enough to learn from. Several hundred a day and the work has usually turned into rules rather than judgement, and something simpler than us will do a better job.
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How far back do the submission documents survive?
Not just the bound price. The application, the file notes, the reasoning. We need roughly two years of them.
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How long has this underwriter been writing this book, here?
The one nobody expects. A brilliant underwriter who joined eighteen months ago left her decision history at her last employer. We can’t read a trail that isn’t there.
See the spread on your own desk.
If you run an MGA, or lead underwriting inside one, write to us with the subject line Operator. The first step is a one-week read of your own book: how far your underwriters move from the technical price, who moves furthest, and on what. The real number, from your own data. No slide deck. We reply within two business days.
Tom is also the engine behind CyberSOC, for CISOs at regulated firms. There it checks the AI’s calls in a security operations centre against what actually happened.