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RFP Submission
Reads a tender, scores your real chance on the issuer's own weights, and says bid or no-bid.
Deterministic rules decide. AI writes the case. You approve, every action logged, and your data never leaves your perimeter.
Onboarding files waiting on a document check. Compliance alerts waiting on a reviewer. Reconciliation breaks traced by hand. Tender responses waiting to be written. High stakes, low judgment, done by hand because getting it wrong is a fine.
95% of enterprise AI pilots delivered no measurable impact (MIT, 2025): generic tools bolted onto real workflows. In a regulated institution a confident wrong answer is a fine, not a typo. So the engine is built the other way around, and every case walks the same four steps.
The published rulebook is encoded as logic, not left to the model's judgment. Same input, same answer, every time. The model never sees what the rules withheld.
rules_engineIt drafts the letter, the summary, the response, grounded strictly in rules-verified facts. It never makes the decision, and a deterministic fallback covers it when no model is available.
llmEvery action has an autonomy setting: draft only, one-click, auto with notice, or silent. Anything that commits money, concedes a case, or reaches a regulator locks to human-only, permanently.
autonomy_gateEach step is tagged rule, AI, or human, with the reason attached. Because the rules are deterministic, the same case reviewed twice gives the same answer. Reproduce any decision from any date.
audit_trailThe engine fails closed. Meet a case it has not been taught, it stops and routes to a human. It never guesses.
Six agents, one engine, grouped by the function each serves. Different departments, different rulebooks, identical shape.
Reads a tender, scores your real chance on the issuer's own weights, and says bid or no-bid.
Reviews a case against the scheme rulebook, decides whether to fight, and drafts the defence.
Business verification and underwriting files, governed end to end with a full audit trail.
Suspicious-activity review, investigation support, and filing, tied to the regional rulebooks.
Finds the break, traces it to the source transaction, and explains the cause, not just the delta.
Watches regulator and scheme publications, diffs them against the encoded rules, flags what broke.
Every rule-bound process is a case, a rulebook, a deadline, a decision. Tell us which one to build next.
Request an agentSoftware you run, not a service you send data to. CaseClear deploys into your own cloud tenancy or data centre, and the model runs there too.
Where does our data go
Nowhere. Everything runs inside your environment.
Can it act without approval
Only where you allow it. Sensitive actions lock to human-only permanently.
Can you prove why it decided
Yes. Every action is logged with source and reasoning, and reproducible.
What if it hits something unusual
It stops and routes to a human. Built to fail closed rather than guess.
What when the rules change
We maintain the rulebooks; an agent monitors regulator and scheme publications.
What if we want to leave
It is your environment and your data throughout. Nothing of yours to extract from us.
Point to one module, share 20 to 30 anonymised cases, and see it run against your rules. If the result is not obviously better than today, we cost you an afternoon.
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