Proofhouse
The operational trust platform for AI agents.
Every organization deploying AI agents faces the same set of questions: What are these agents actually doing? Are they ready to scale? What happens when they fail? How do we prove compliance? Proofhouse brings those concerns into one platform — mapping AI-enabled workflows, scoring operational readiness, capturing and learning from failures, and providing the architecture path toward runtime governance.
START A CONVERSATION
Policy frameworks and risk registers define what should happen — but they don't connect to production agent behavior.
Sits between policy and production. Maps what agents actually do, scores operational readiness, learns from failures, and enforces governance at runtime.
Model monitoring and bias testing track ML performance — but they miss operational governance, workflow behavior, and compliance.
WORKFLOW CONTEXT
ACTIVECaptures how work actually runs so the rest of the platform has a grounded, queryable model of operational reality.
WORKFLOW OBJECTS
Create a live model of the workflow, not just a pile of source files.
OWNERSHIP + ESCALATION
Keep named owners, escalation paths, and approvals attached to the work.
RUNBOOKS + PLAYBOOKS
Store the instructions, exceptions, and fallback paths teams actually use.
EVIDENCE + APPROVALS
Preserve review history, supporting records, and decision evidence.
SIGNALS + EXCEPTIONS
Track drift, weak handoffs, and exceptions before they compound.
CONNECTORS + GROUNDING
Pull context from the systems and documents the workflow already depends on.
ANALYST
ACTIVEMakes the platform's operational intelligence accessible to anyone — operators, compliance teams, and leadership who cannot write queries or navigate technical interfaces.
ANALYST Q&A
Ask grounded workflow questions in plain language and get answers rooted in live context.
BRIEFINGS + REPORTS
Generate summaries and review-ready outputs without rebuilding context manually.
RISK SURFACING
Surface operational risks, compliance gaps, and anomalies through conversational queries.
READINESS
ACTIVEAnswers whether a workflow is ready to scale with AI — with a structured, evidence-based score and actionable remediation guidance.
WORKFLOW STABILITY
Measure repeatability, reversibility, and exception pressure inside the workflow itself.
DEPENDENCY RESILIENCE
Expose fragile integrations, vendor concentration, and cascade risk early.
OVERSIGHT + OWNERSHIP
Check for named owners, escalation paths, override rights, and fallback modes.
CONTROL READINESS
Assess whether approvals, logs, and decision records exist where they need to.
AUTOMATION FIT
Separate safe, repeatable tasks from high-impact work that still needs tighter controls.
TRUST-GAP DIAGNOSIS
Turn weak spots into a clear explanation of where the workflow is most exposed.
REMEDIATION PRIORITIES
Rank the fixes that most improve readiness before the next stage of scale.
READINESS ROLLUPS
Roll workflow findings into a broader operating picture without losing local detail.
FORGE
EARLY RELEASEEnsures the organization and the platform learn from every failure. Failure patterns are systematically captured rather than lost in Slack threads and post-mortems.
INCIDENT RECORDS
Capture structured incident data tied to the workflows and agents involved.
FAILURE TAXONOMY
Classify failure modes into a standardized, evolving taxonomy.
PATTERN ANALYSIS
Identify recurring failure modes across incidents, workflows, and time.
PLAYBOOK GENERATION
Generate response playbooks from analyzed patterns and prior resolutions.
GOVERNANCE
PLANNEDThe architecture direction for runtime policy enforcement, auditable compliance operations, and regulatory reporting. Proofhouse is being built so that every capability feeds into a governance layer that can prove AI operations are compliant, safe, and governed.
POLICY ENFORCEMENT
Monitor agent behavior in production and enforce policy boundaries at runtime.
COMPLIANCE OPERATIONS
Maintain auditable records that map to regulatory frameworks and internal controls.
REGULATORY REPORTING
Generate reporting aligned to EU AI Act, NIST AI RMF, ISO 42001, and sector-specific requirements.
Integrated through shared workflow context
BOUNDARY RULE: IF ANY CAPABILITY BEGINS STORING ANOTHER LAYER'S CANONICAL TRUTH, BOUNDARY DRIFT IS HAPPENING.
Proofhouse is built by someone who has designed compliance frameworks in regulated industries — mortgage servicing, consumer lending, federal compliance. The platform's governance roadmap is positioned against specific regulatory forcing functions:
Mandatory compliance obligations including major deployer requirements landing August 2, 2026.
Voluntary but increasingly referenced in procurement and compliance. Proofhouse maps to RMF functions.
Emerging international standard for AI management systems. Proofhouse supports operational evidence and control requirements.
Financial services (Freddie Mac AI/ML governance), healthcare, insurance — sector requirements that demand operational proof.
Ready to make AI agent trust an engineering problem instead of a hope?
We work with organizations deploying AI agents in consequential workflows. Every engagement starts with one workflow, one owner, and a clear review loop.
START A CONVERSATION