Proofhouse is the durable, audit-grade record of how consequential AI-assisted work actually happened — the evidence, review states, exceptions, and decisions that have to stand up to an auditor, a regulator, or a customer later. Built for regulated operations where a plausible answer isn't enough.
For claims, lending, insurance, KYC, healthcare, and other document-heavy work where the output has to be trusted long after it ships.
Six capability layers around one durable operating record.
Model the operational workflow: inputs, source evidence, destination systems, owners, review states, and escalation paths.
Give operators a workflow-scoped interface to ask what happened, what needs review, where evidence lives, and which cases are blocked.
Score which steps are automation-safe, review-required, escalation-required, or blocked before the workflow is scaled.
Open-core incident memory for workflow failures, routing misses, missing evidence, reviewer disagreements, and recurring exception patterns.
Policy lifecycle, deterministic control evaluation, rights and redaction posture, approval records, and audit-packet evidence operations — the governance kernel.
The path that turns approved workflow activity into private evaluation and improvement assets — governed by rights, redaction, and use-approval. Designed to make the operation compound, not to create a training-data marketplace.
One interface organized around workflow context, evidence, review states, incidents, controls, and readiness - not separate products.
EXPLORE THE PLATFORMPROOFHOUSE IS THE PROOF LAYER UNDER AI-ASSISTED OPERATIONS — NOT A GENERIC AGENT WRAPPER, AND NOT A REPLACEMENT FOR YOUR GRC OR SYSTEM OF RECORD.
Every Proofhouse engagement follows the same operational arc: map the workflow, preserve source evidence, score readiness, route exceptions, learn from failures, and build the evidence base needed for review and oversight.
The first Proofhouse use-case deep dive focuses on guided implementation for document-heavy workflows where teams still read source files, enter data into systems, chase missing information, and preserve review evidence. It is a proof path, not the full category.
VIEW DEEP DIVEReview and escalation workflows
Workflow readiness diagnostics
Incident and failure learning
Governance and evidence operations
We work with teams where AI-assisted work needs ownership, QC-style checks, review states, exception handling, and an evidence trail. Engagements begin as a guided implementation of the smallest useful workflow slice, then expand only when the controls are working.
Start with one operational workflow, one owner, one normal path, one exception path, and a clear reason the workflow is worth improving.
Connect the source inputs, required fields, destination handoff, validation checks, review states, and case-level operating context.
Move clean cases forward and route missing, conflicting, low-confidence, or policy-sensitive cases to the right human review point.
Preserve decisions, incidents, and evidence packets so teams can improve the workflow without turning production work into uncontrolled training data.
Capture how the workflow actually runs — inputs, source evidence, owners, review states, and exception paths — as a live operating record, not a pile of files and Slack threads.
Turn that record into audit-grade evidence: what was used, which checks passed, which cases needed review, who approved what — traceable long after the work ships.
Capture failures, routing misses, and recurring exceptions as institutional memory, so the operation improves with use instead of repeating the same mistakes.
Research is where we go deeper on the operating thesis behind Proofhouse: readiness, governance, failure analysis, and a durable proof layer underneath the application surface.
Proofhouse does not treat workflow context, readiness, failure learning, and governance as one monolithic capability. They are distinct jobs — tightly integrated through shared workflow context, but with clear boundaries.
AI is making workflow-specific applications easier to build. The harder problem is proving what happened inside consequential work: the evidence used, the reviews performed, the controls applied, the actions approved, and the failures learned from.
USMI runs on the discipline Proofhouse sells. This is the first entry in a weekly operator log: real artifacts from a company run by one human and a fleet of AI agents, published under the same evidence gates that govern the platform.
RESEARCH IS THE THESIS LAYER BEHIND PROOFHOUSE
View All ResearchWe help scope the first AI-assisted workflow slice, then use Proofhouse to preserve source evidence, route exceptions, and keep the workflow owner in control.
ASSESS A WORKFLOW