Where AI-assisted work has to be provable.
Proofhouse is built for consequential, regulated operations where AI can reduce manual effort but the business still needs proof: source evidence, review states, exception handling, and an audit-grade record that stands up later — across claims, lending, insurance, KYC, healthcare, and other document-heavy work. We usually begin with a guided implementation of one controlled workflow slice.
Regulated Document Operations
Continuous QC for document intake, data entry, exception routing, and review evidence across claims, lending, insurance, billing, KYC/KYB, and similar workflows.
ACTIVE PATTERNReview and Escalation Workflows
AI-assisted work where routine cases can move faster, but missing context, policy-sensitive decisions, or low-confidence outputs still need human review.
ACTIVE PATTERNWorkflow Readiness Diagnostics
Operational assessments that separate automation-safe steps from review-required, escalation-required, and blocked workflow steps before scale.
ACTIVE PATTERNIncident and Failure Learning
Structured memory for recurring workflow failures, evidence gaps, reviewer disagreements, and exception patterns that should improve future operations.
EARLYGovernance and Evidence Operations
Review-required decisions, approvals, redaction posture, rights checks, and controlled use of workflow-derived learning assets.
Proofhouse stays organized around workflow context, readiness, Analyst, Forge, and Governance rather than becoming a set of disconnected vertical apps.
Regulated document operations are the first deep dive because the pain is concrete: manual entry, missing information, review burden, and evidence pressure.
The same operating pattern extends to other workflows where AI assistance needs evidence, escalation discipline, and controlled improvement.