What Is FactVault? | Reliability Infrastructure for AI and Decision-Critical Systems

FactVault in one sentence

FactVault makes reliability a first-class data primitive: it tracks provenance and reliability per field, enforces trust boundaries by policy, and outputs versioned records that remain defensible over time.

Reliability per field

Each output field carries evidence: source system, source field, reliability score, and approval state — at the same granularity real decisions are made.

Policy-enforced trust boundaries

Externally claimed trust can be capped. Sources can be whitelisted. Automation can be gated behind thresholds and approvals — before downstream systems act.

We version trust

FactVault does not overwrite reality. It preserves history and trust evolution so organizations can answer: what did we know, and how reliable was it, when the decision was made?

FactVault is not a “system of truth.” It is a system of defensible truth: inspectable evidence + enforceable trust boundaries.

How it works (conceptually)

FactVault ingests multi-source records, resolves conflicts deterministically, and produces versioned outputs. Reliability is computed and propagated so every consumer inherits the true trust level of each field.

1) Ingest SourceRecords

Systems send SourceRecords into FactVault (CRM, OCR, APIs, partners). Inputs are normalized at the edge so comparisons remain deterministic.

Note: values are stored as strings to keep ingestion generic and schema-agnostic.

2) Cleanse and enforce source boundaries

Before linking, a cleansing step can filter SourceRecords by policy: allowlists, reliability caps, and externally claimed trust ceilings.

3) Link & resolve conflicts

Linking strategies determine which records can merge and under what conditions. Conflicts do not “break” the system — they become inspectable, scoreable, and governable.

4) OutputRecord + trust timeline

Outputs are versioned. Reliability changes are tracked over time. Human approvals (or disapprovals) trigger recalculation, producing a visible trust evolution.

Architecture diagram The AI trust gap What is Data Trust?

What you can see in the product

FactVault is built to make reliability visible to both engineers and executives: outcomes, decision logic, trust evolution, and operational metrics — aligned to real governance questions.

Executive Reliability Report

Reliability

Average reliability, fill rate, approvals, source dominance with drill-down.

Outcome & Logic

Explainable

Record → field → source evidence, linking strategy, and constraints.

Trust Evolution

Versioned

Reliability progression and rating progression over time (“trust timeline”).

Governance

Enforced

Human-in-the-loop approvals, disapproval impact, policy thresholds.

See the Reliability Report

The report answers executive questions: where trust breaks, which fields are empty or low confidence, which sources dominate outcomes, and where human review is still required.

Open demo report Get a walkthrough

What FactVault is not

FactVault is designed to sit underneath AI and automation — not to replace your data platform or governance suite.

Not a data warehouse — FactVault is not your analytics store.
Not a catalog-only tool — it doesn’t just document lineage; it enforces trust boundaries.
Not traditional MDM — it does not claim a single “truth”; it preserves defensible truth and versions trust over time.
Not model transparency — it addresses the upstream layer: whether data is reliable enough to automate decisions.

If you can’t measure data reliability, you can’t trust AI. If you can enforce it, AI becomes defensible.

Microsoft AI Trust AI scare trade: defensibility What is Data Trust? Architecture