Flex-Check Pilot

For criminal-justice reviewers who need AI but can’t send data to the cloud

AI inside the boundary.
Not outside it.

Every AI tool that could help criminal-justice reviewers sends data to a vendor’s cloud. CJIS policy prohibits it. Flex-Check Pilot runs on the agency’s own hardware, inside the boundary — supplementing the expertise of experienced reviewers, not replacing it.

The Problem

Criminal-justice AI promises speed, but almost every product that could help is incompatible with the environment where the work is done.

Flex-Check Pilot is the only AI platform built to operate inside a CJIS security boundary. The model runs on the agency’s own hardware. No data leaves. No external inference calls. No vendor cloud in the path.

Every other AI tool that could help reviewers work faster requires sending data outside the state’s environment. CJIS policy prohibits that. Reviewers have seen the demos. Every time a tool gets close to procurement, the security officer kills it, and rightfully so. Flex-Check Pilot is built for the environment as it actually exists.

Built for CJIS first, not retrofitted

The model runs on your hardware. Inside your boundary. No external API calls.

Flex-Check Pilot is a self-hosted AI platform. The model runs on the agency’s own infrastructure, inside the CJIS boundary, with no data leaving the state’s environment. That architecture is not a configuration option. It is the only way AI can legally operate in this environment. Every other AI tool that reaches this conversation will ask you to send data to a vendor’s cloud. Flex-Check Pilot does not. That is the difference.

Three ways Flex-Check Pilot helps

Ask. Plan. Classify.

Ask

Plain-English answers to questions over live system data and the agency’s own indexed policy documents.

“How many firearms cases are pending over 3 days?”

“What does our SOP say about out-of-state conviction equivalency?”

Plan

Flex-Check Pilot breaks a complex operational goal into a reviewable, phased plan for reviewer approval before anything is executed.

Every step is visible. Nothing runs without explicit human sign-off.

Classify

Reviews a rap sheet against applicable denial rules and returns a structured verdict — approve, deny, or needs review — with a confidence score, the matched rule, and inspectable reasoning.

Approve

Deny

Needs review

The reasoning behind every classification is not a summary. It is logic you can read and verify, step by step. The same input always produces the same verdict. Auditors and courts can inspect the actual decision, not a narrative of it.

Why It’s Different

The architecture is the whole point.

SaaS AI — What Doesn’t Work

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Data sent to vendor cloud for processing. Prohibited for CJIS records.

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Audit trail is opaque.

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Vendor dependency on cloud availability.

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A non-starter for procurement.

Flex-Check Pilot — What Does

Model runs on the customer’s own hardware, inside the CJIS boundary. No data leaves.

Full audit trail on every decision.

Vendor-independent inference.

CJIS-aligned by architecture, not by exception.

Agency in Control

AI that earns its place one rule at a time, with the agency in control of every threshold.

Flex-Check Pilot is deployed on the agency’s own infrastructure. The model runs on the agency’s hardware. No data leaves the CJIS boundary.

The agency defines the denial rules Flex-Check Pilot evaluates against. TSC holds no rule database. Rules are passed in per request. The agency owns its statutory decisions.

Flex-Check Pilot tracks its own performance: calibration, agreement with human reviewers, and a readiness scoreboard showing which rules are safe to auto-decide and which still need human review.

Rules earn automation gradually, based on the agency’s own measured results. Nothing is auto-decided until the agency’s data says it should be.

Every AI action inherits the signed-in reviewer’s permissions. The AI cannot access, retrieve, or act on anything the reviewer couldn’t do manually.

Why This Matters

Most AI tools are all-or-nothing: you trust them or you don’t. Flex-Check Pilot earns trust rule by rule, backed by the agency’s own data. A rule doesn’t automate until the agency’s measured results say it should. The agency stays in control of its statutory decisions. The vendor never quietly owns the policy.

The Cost of the Status Quo

An operation that runs on knowledge that retires, review that varies, and AI tools that can never legally enter the environment.

Experienced reviewers retire, taking decades of interpretive knowledge with them. New reviewers make decisions the experienced ones would have caught.

Decision consistency varies by reviewer, by day, by volume. The same record produces different outcomes in different hands.

AI tools that could help are blocked at the CJIS perimeter. This prohibition is legitimate, permanent, and unsolvable by a SaaS vendor.

Thousands of straightforward, rule-clear cases consume the same reviewer time as genuinely hard ones, leaving no capacity for complex judgment.

The Outcome

Reviewers who spend their time on the cases that genuinely need human judgment.

When Flex-Check Pilot is running at scale, well-proven cases are projected to be decided in seconds rather than minutes. Reviewers handle only the cases where the record is ambiguous, the rules are contested, or the confidence score says a human eye is warranted. Every decision is backed by inspectable, replayable reasoning. And the system keeps improving: every reviewer override explains itself and becomes ground truth that makes the next classification better.

* These are projected outcomes based on the classification capability as built and tested. Flex-Check Pilot does not yet have a production deployment to measure against.

AI inside the boundary. Not outside it.