FactVault is reliability infrastructure for AI and decision-critical systems. It makes data trust measurable and enforceable — per source, per field — so enterprises can govern AI confidently and defend automated decisions.
It does this by ensuring that data used in automated decision-making processes is reliable, traceable, and supported by structured evidence. In addition, it provides explicit usage context, indicating how data may be used within decision processes based on its origin, reliability, and classification. While it does not govern the behavior of AI models themselves, it enables clear attribution of responsibility by distinguishing between data-related and model-related errors, supporting transparency, accountability, and compliance with emerging regulatory frameworks like the EU AI Act. By quantifying and governing data trust, FactVault helps close the AI trust gap.
The trust gap is usually data, not the model.
A quick way to see what you’ve already read — and what to open next.
What is FactVault?
Product overview: reliability engine, trust ceilings, versioned trust timeline, executive report.
What is Data Trust?
Definition: trust as defensibility at decision time (not accuracy, not confidence).
The AI Trust Gap
Why AI reliability is downstream of data reliability — and how to close the gap.
Architecture
Where FactVault sits: reliability control plane between data platforms and decision systems.
Microsoft AI Trust
How FactVault complements Fabric & Purview to gate Copilot and automation by reliability.
AI Scare Trade: Defensibility
Why markets reprice trust — and why reliability infrastructure becomes the moat.
Demo Reliability Report (PDF)
Executive view: reliability, fill rate, approvals, source dominance + drill-down.
Authority Document (PDF)
Vendor-independent framework on data trust, decision boundaries, and governance.
Most AI systems assume data is correct. In reality, data is incomplete, inconsistent across sources, and trusted without evidence — leading to wrong conclusions, compliance risk, and loss of confidence.
FactVault is not a system of truth, but a system of defensible truth.
FactVault addresses the root cause — not the symptoms.
FactVault measures reliability and provenance at the same granularity decisions are made: field by field.
Measure where each field came from, how reliable it is, and how often it is missing.
Keep a full audit trail: source system, source field, reliability, and approvals.
Reports show where trust breaks, which sources dominate outcomes, and where to invest first.
At a glance: completeness, average reliability, approvals, and source dominance — with drill-down to source → record → field.
If data drives decisions, FactVault belongs underneath it.
A vendor-independent framework for understanding, assessing, and governing data reliability in decision-critical environments — including AI-driven systems.
FactVault runs as a distributed service. Each instance represents a fully isolated environment.
Production-grade instance hosted in Azure West Europe. Used for live demos and real-world workloads.
Open instanceWant a short walkthrough tailored to your pipeline and sources?