What these have in common

In every one of them the decision itself is not the hard part. Defending it is. Somebody eventually asks why a claim was fast-tracked, why a member was found ineligible, why a request was pended, or what the appetite was on the day a risk bound. If the answer has to be reconstructed from memory and free-text notes, the process carries a risk nobody priced.

AI Rule Engine makes the decision with rules you wrote and can read, then keeps the trace, the version of the logic, the approver who published it, and a signed record proving that logic has not changed since. AI reads unstructured text into facts. It does not make the call.

Start with the one that costs you the most

Insurance operations

Claims triage and routing

When a claim arrives, does it fast-track, go to standard handling, or get referred?

Fast-track, route, or refer every claim on submission using deterministic rules, with an inference trace, the ruleset version, and the approver recorded for each decision. Runs in your own Azure tenant.

See how it is proved

Underwriting referral and appetite

Does this submission bind automatically, go to an underwriter, or fall outside appetite?

Auto-bind submissions inside appetite, refer the rest to an underwriter with the reasoning attached, and keep a versioned, provable record of the guidelines in force on every bind.

See how it is proved

Health plans and benefits

Eligibility and benefit determination

Is this member eligible for this benefit on this date, and what is their cost share?

Turn plan documents into rules that determine eligibility and cost share the same way every time, with a traceable reason for every determination and versioned logic tied to each plan year.

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Prior authorization review

Does this prior authorization request meet medical policy criteria, and if not, who needs to look at it?

Approve, pend, or refer prior authorization requests against encoded medical policy, with clinical review preserved as a first-class step and a traceable reason recorded for every determination.

See how it is proved

Not one of these four?

The engine is not specific to insurance or benefits. Any decision that is governed by written policy, made repeatedly, and questioned afterwards fits the same shape: lending exceptions, grant eligibility, vendor onboarding, trade surveillance disposition, entitlement reviews.

There is also a wider library of workflow examples across support, revenue, and document operations if you want to see the range before narrowing.

Talk through your decisionBrowse the use case library

Bring us one decision you make by hand

In one working session we will model it, run it against your own examples, and show you the trace it produces. If it is not a fit, we will tell you.

Book a working session