In 2024, 79% of FDA Warning Letters
cited data integrity failures

85% of safety and efficacy issues in Complete Response Letters
are never disclosed by companies.

With AI making more decisions, the audit trail matters more than ever.

Why this keeps happening

  • Your data warehouse stores the current answer. Sources disagree.
  • When a study is retracted, which decisions did it affect?
  • AI recommended X. Can you reconstruct why?

"Black box" is a documented rejection reason. Traditional databases overwrite history.

StemeDB

A knowledge graph that stores claims, not facts.

Append-only. Auditable. Built for regulated industries.

Every claim has a source

StemeDB stores assertions with provenance, not overwritten facts.

  • When sources disagree, you see the disagreement
  • When a source is retracted, you know what's affected in seconds
  • History is preserved. Nothing gets silently overwritten.

What this enables

Conflict Visibility

See when sources disagree. Confidence scores show you how much.

Cascade Invalidation

Retract a source. See every downstream decision affected.

Complete Audit Trail

Every query logged with provenance. Export for regulators.

Time-travel queries: "What did we believe on January 1st?"

Here's what it looks like

FDA guidance now requires audit trails for all AI-enabled devices. This is what compliance looks like.

Query:

subject: semaglutide:gastroparesis_risk
predicate: risk_level

FDA labels say low incidence. Patient reports say otherwise.
Watch how StemeDB surfaces that conflict.

Questions

What you saw:

  • Conflict visibility — FDA vs patient reports, with confidence scores
  • Cascade invalidation — Source retraction, instant impact assessment
  • Audit trail — Every query logged, export-ready
  • Time-travel — Point-in-time queries
  • Trust & Safety — Quarantine queue, circuit breakers
StemeDB