FactVault makes data reliability measurable, auditable, and actionable — per field, per source, across your entire pipeline.
Most organizations talk about data quality or AI transparency.
What’s missing is data trust:
the ability to justify decisions under scrutiny.
If you can’t measure data quality, you can’t trust AI.
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 decisions, and where to invest first.
At a glance: completeness, average reliability, approvals, and source dominance — with drill-down to record → field → source.
If data drives decisions, FactVault belongs underneath it.
A practical, decision-oriented document for organizations operating in
decision-critical environments.
Most organizations know they have a data trust problem.
Few can define it. Almost none can govern it.
This document provides a vendor-independent framework for understanding, assessing, and governing data reliability in decision-critical environments — including AI-driven systems.
Inside the document:
This is not a sales document.
It is an authority reference intended to support governance, accountability, and defensible decision-making.
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?