FlatClaw, Private AI Platform
All use case spotlights
Compliance & ApprovalsCollectionsHealthcare

Healthcare receivables agency

≈ $10M revenue (approx.) · ~60 employees · regulated healthcare receivables

Protected-health-information send gate

Every outbound file that carries protected health information is checked against the placement it belongs to and held for human approval, with an audit trail of who released what, and when.

Organization
Healthcare receivables agency, on-premise, regulated
Risk
Outbound files carrying protected health information
Runs on
The agency's own hardware
Scope
Send verification with human approval and audit
The situation

Where they started.

A file sent to the wrong recipient with protected health information inside is the agency's worst day. The manual control was attention: a person checking a highlighted list before hitting send. It worked until the day it did not, and the compliance team that followed needed a control that could not be skipped.

What FlatClaw does

What was built.

  • Built on FlatClaw's approval engine: the agent composes every outbound send that carries protected information, but never executes it.
  • Before the file reaches the queue, the agent checks it against the placement it belongs to, the recipient, and the expected shape of the data, and attaches the evidence and any anomalies.
  • A compliance reviewer approves or denies from the queue; on approve, the send replays with the reviewer's own credentials; on deny, nothing happens.
  • Both outcomes, with the approver's identity, land in the audit log.
Results

What changed.

  • The control is structural: a send cannot happen without a recorded human decision.
  • Reviewers see evidence, not a highlighted spreadsheet.
  • Audit questions are answered from the log, not from memory.
  • The same engine gates every other consequential action the agent can take.
Why private

The files being checked are the sensitive data itself. Checking them with a hosted model would create the very exposure the control exists to prevent.

Runs on

On-premise hardware

The stack

Components involved.

Approval engineFlatClaw Portal approvals queueAgent harness (Pi core)Audit logPrivate inference on an on-prem GPU
Your workflow

Have one like it?

Every spotlight started as a conversation about a process nobody liked doing, under a data-locality constraint.