AI Trust • Data Reliability • Field-level Provenance

Make AI decisions on data you can actually trust

FactVault makes data reliability measurable, auditable, and actionable — per field, per source, across your entire pipeline.

Decision confidence Multi-source conflict resolution Explainable down to the field
View the Reliability Report How it works

If you can’t measure data quality, you can’t trust AI.

Executive-ready reporting
Reliability, fill rate, approvals, and source dominance — with drill-down to field-level provenance.
The trust gap is usually data, not the model.
Once you can quantify data reliability per field and per source, many AI trust problems simply disappear.
Talk to us

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 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Outdated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 confidence.
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 quality into management insight

Reports show where trust breaks, which sources dominate decisions, 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.

Open demo report
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 quality investments with measurable impact.
AI product owners
Reduce AI risk by measuring trust upstream.
Compliance & risk
Audit trail down to field-level provenance.
Platform engineering
Azure-native primitives, minimal moving parts.

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?

Want a short walkthrough tailored to your pipeline and sources?

Get in touch
© FactVault2 • Azure-native reliability reporting for AI trust