Verita.
Trust is a system, not a disclaimer.
An independent verifier model and reproducibility engine that checks claims, citations, statistics, methods, provenance, and confidence before scientific output is accepted.
A confident scientific error is worse than no answer.
AI-generated research can fail through invented citations, invalid statistics, unsound protocols, weak provenance, or irreproducible analysis. Regulated teams also need proof of how each result was produced and approved.
Verita separates generation from judgment. A dedicated verifier challenges the executor, assigns confidence, enforces abstention, re-runs analyses where possible, and creates a durable validation record.
Independent checks across the scientific record.
Citation grounding
Map each material claim to a source and flag unsupported or contradictory evidence.
Statistical validity
Check tests, assumptions, units, sample handling, and reported interpretation.
Method review
Challenge protocol soundness, missing controls, and methodological inconsistencies.
Reproducibility runs
Re-execute analyses in controlled environments and detect meaningful discrepancies.
Confidence policy
Score outputs, enforce thresholds, and abstain or escalate when evidence is insufficient.
Compliance evidence
Produce signed validation records and evidence packs for regulated review.
Every output must earn acceptance.
Capture the full trace
Take the output, sources, tools, data, model versions, and approvals.
Run independent checks
Test grounding, methods, statistics, provenance, and policy.
Accept, abstain, or escalate
Apply confidence thresholds and route ambiguous cases to a scientist.
Capture the failure mode
Return caught errors to Cortexa's private alignment dataset.
Continuous verification without bottlenecking the platform.
One trust layer across every product.
Make verification part of the workflow.
Define what evidence your organization requires before an AI-generated result can move forward.