Sentinel
Sentinel audits quality and validation across the chain, benchmarking results against versioned reference datasets. It detects methodology drift and surfaces it, rather than letting it pass silently.
Inputs
- Results and provenance from Novora and Omnix
- Selected benchmark reference datasets
- Methodology consensus baseline from Lumen
Outputs
- Quality and validation audit report
- Public benchmark comparison against versioned references
- Methodology-drift flags with reasoning
How the AI suggests, and where you decide
Sentinel reports benchmark results and drift signals with the reasoning behind them. It recommends next actions; the researcher decides whether to re-run, expand the corpus, or accept.
Citation grounding
Benchmarks are reported against named, versioned reference datasets so results are independently checkable. Drift is measured against the methodology consensus baseline.
Downstream connections
Sentinel produces user-facing deliverables at the end of the chain. Its outputs carry the full provenance accumulated across the closed loop.
For the technical reader
Technical details
- Public benchmark reporting against versioned reference datasets with drill-down to methodology.
- Methodology-drift detection against the Lumen consensus baseline.
- Uncertainty surfaced explicitly with structured strength indicators.
Researchers, Clinical research, Pharma enterprise. The decision-support boundary is surfaced consistently: the platform suggests; the user decides.
Each capability is one link in the closed loop. See the whole chain end to end.