Sweat does the work. You make the call. The worker assembles the case, applies your written policy through a deterministic rules engine, and hands your reviewer one recommended action — with citations and a full audit trail.
The worker does the file. Your reviewer makes the call. Every case replays, line by line.
The worker pulls the records, transactions, prior alerts, and documents into one evidence file — every item cited to a source a human can open. No hunting across systems.
Your written rules run through a deterministic engine, not a black-box model. The same input gives the same output every time, and you can read exactly which rule fired.
The model extracts and assembles the evidence, each item cited to a source. The deterministic engine applies your policy to those facts. Your reviewer verifies both before releasing.
The reviewer gets a single recommended action, the citations behind it, and the full trace. Approve, edit, or override in one screen.
The human signs. Sweat never closes a regulated case on its own.
A reviewer’s correction becomes a proposed rule that is reviewed, versioned, and approved before it can fire. Your policy only changes when you say so — and the rule is yours to keep.
Sweat deploys the worker inside your environment where that's the requirement. Skipify runs a model we deployed and tuned on their own hardware today. For the review product we run single-tenant now, with in-environment deployment on the roadmap — and we tell you exactly what runs where before you sign anything. D360, a Saudi digital bank, won't move their data off-site, so we brought the worker to them.
Before a single live case, the worker runs your historical cases and publishes how often it agreed with your experts — on your data, your policy.
You see the numbers before you trust it. Autonomy is earned one decision-type at a time, and it steps back down to human review if outcomes slip.
Pull any case, from any date, and replay it exactly as it ran — the evidence, the policy version and the rule that fired, the citations, the recommended action, and the human who released it.
The explainable, independently validated, fully documented posture your model-risk (SR 11-7) and BSA/AML reviews ask for.
Detection tools flag the case. Case-management tools file it. Sweat does the work in between — it assembles the evidence, applies your policy, and hands your reviewer one recommended action to release.
Runs Sweat in production. Their expert went from about two hours of casework to about ten minutes of review, and the rent math was checked cell-by-cell against the regulator's own workbook.
Runs a model we deployed and tuned to run fast on low-cost hardware, inside their environment. Our worker, their infrastructure.
Running a live fraud-review demo with a pilot in scoping. They won't ship their data off-site — which is exactly why on-prem is the ask.
550 tasks auto-closed on external-truth checks, zero false passes.
Roadmap cells are stated, not hidden.
Book a demo and we'll show you a live review of one case — evidence, policy, recommendation, release — and scope a shadow run over your historical files.
Every deployment starts with a shadow run on your own historical cases — measured before trusted.
