AI Trust • Data Trust • Reliability Infrastructure

AI Trust cannot exceed Data Trust.

FactVault is reliability infrastructure for AI and decision-critical systems. It makes data trust measurable and enforceable — per field, per source — so enterprises can govern AI confidently and defend automated decisions.

Field-level provenance Trust ceilings Versioned trust evolution Human approvals Executive reporting

The trust gap is usually data, not the model.

Reliability control plane
FactVault sits between your data platform and decision systems, enforcing policy-based trust boundaries. See the architecture.

Defensible decisions require evidence.
Field-level provenance, reliability scoring, trust ceilings, and a versioned trust timeline — so you can answer what was known at the moment a decision was made.
Explore

Pages & documents

A quick way to see what you’ve already read — and what to open next.

AI fails silently when data fails first

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.

Incomplete
Missing fields, partial coverage, late updates.
Conflicting
Different sources disagree — without accountability.
Outdated
Old records overwrite newer reality.
Opaque
No field-level provenance, no measurable reliability.
How it works

Reliability as a first-class data primitive

FactVault measures reliability and provenance at the same granularity decisions are made: field by field.

Quantify reliability — per field

Measure where each field came from, how reliable it is, and how often it is missing.

Preserve provenance across sources

Keep a full audit trail: source system, source field, reliability, and approvals.

Turn trust into management insight

Reports show where trust breaks, which sources dominate outcomes, and where to invest first.

Reporting

The FactVault Reliability Report

At a glance: completeness, average reliability, approvals, and source dominance — with drill-down to record → field → source.

Executive questions answered
  • Can we trust this data enough to automate decisions?
  • Which fields are empty or low-confidence?
  • Which sources dominate our outcomes (and why)?
  • Where do we still need human review?
One line that reframes AI trust
Once you can quantify data reliability per field and per source, many AI trust problems simply disappear.
This is not monitoring. This is decision confidence.
Who it's for

Built for enterprise data + AI teams

If data drives decisions, FactVault belongs underneath it.

Data leadership
Steer trust investments with measurable impact.
AI product owners
Reduce AI risk by governing trust upstream.
Compliance & risk
Audit trail down to field-level provenance.
Platform engineering
Azure-native primitives, minimal moving parts.
Authority document

Data Trust & Reliability — the foundational guide

A vendor-independent framework for understanding, assessing, and governing data reliability in decision-critical environments — including AI-driven systems.

Get the document
Free — used in enterprise & AI governance discussions

Live instances

FactVault runs as a distributed service. Each instance represents a fully isolated environment.

West Europe

Production-grade instance hosted in Azure West Europe. Used for live demos and real-world workloads.

Open instance

Ready to see what your data is really worth?

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FactVault — created by Martijn Wiggers, with enterprise delivery support from Ariqt .
© FactVault2 · Reliability infrastructure for defensible AI and decision-critical systems