AI Rule Engine - Auditable Decision Automation

Automate the decision. Keep the proof.

Some decisions get made hundreds of times a day, follow written policy, and cost you when they come out differently depending on who handled them. AI Rule Engine runs those decisions and records exactly why each one went the way it did.

The proof is the product

• Every decision shows its work. A run records which rules fired, in what order, and the values each condition matched on. Not a log line. The reasoning that got there.
• Ask why an answer is missing. Name the answer you need and the engine works backwards to establish it. When it cannot, you get a proof tree naming the fact that was absent, not a confident guess.
• Conclusions withdraw themselves. When a fact behind a conclusion changes, the conclusion retracts rather than going stale.
• Caught before it runs. Static verification flags rules that can never fire, duplicates, conflicting writes, and input combinations no rule covers.

Where AI fits, and where it does not

AI turns unstructured input into facts: it classifies a loss description or summarizes a clinical history. Rules you wrote and approved decide what those facts mean. No model applies a threshold, interprets a policy, or picks the outcome.

Four decisions to start with

Claims triage and routing. Eligibility and benefit determination. Prior authorization review. Underwriting referral and appetite.

Runs in your cloud, not ours

This managed application installs into your own Azure subscription. Rule data, execution history, uploaded files, and AI prompts never leave your tenant. Pair it with Azure OpenAI and the model calls stay inside your boundary.

Built for the security review

SSO over OIDC and SAML 2.0, SCIM provisioning from Entra or Okta, custom roles, access review, and IP allowlisting. Append-only audit events, exportable and streamable to your SIEM. Every publish produces a signed record proving the logic in force on a given date has not been altered since. Our posture, subprocessors, and remaining gaps are stated plainly at ruleengine.ai/security.

Changed by your policy owners

Rules are decision tables a policy owner can read. Test cases gate every release, and changes reach production through a pipeline with approval gates, not direct edits.

When an agent acts, it acts through a rule

Each AI agent gets its own Model Context Protocol token, scoped to the rulesets, files, and log entries you allow. What it triggers runs through the same rules, approvals, and audit trail.

Post the inputs to one endpoint and get the decision back in the same call. Bring us one decision you make by hand. In one session we will model it, run it against your own examples, and show you the trace. If it is not a fit, we will tell you.