The governed evidence architecture for scientific AI.
Six integrated software modules designed to author bounded claims, hash validation datasets, issue verifiable Trust Records, and enforce runtime deployment gates across your enterprise AI portfolio.
One evidence chain. Seven objects the platform maintains.
Each link constrains the next. A break anywhere leaves the decision at the end unsupported, however strong the model at the start.
Model
Architecture, training lineage, weights, feature embeddings, and base assumptions of the computational or surrogate model.
- ▸Model Architecture Dossier
- ▸Training Lineage Manifest
- ▸Deterministic Seeds
Six modules. The method, made operational.
The software keeps the evidence chain versioned, auditable, and connected to the decision it supports across your enterprise AI portfolio.
Claim Builder
Replaces vague aspirational claims with structured, machine-verifiable Context of Use definitions specifying user, decision, biological test articles, conditions, and replacement boundaries.
- ✓Structured CoU ontology for pharma, diagnostics, and NAMs
- ✓Explicit boundary and exclusion perimeter definition
- ✓Decision consequence and risk tier categorization
- ✓Automated study design hypothesis framing
Evidence Capture
Captures comparator justifications, multi-lab reproducibility artifacts, applicability domains, and subgroup performance directly indexed to the original claim.
- ✓Defensible comparator selection and historical baseline indexing
- ✓Versioned code, data splits, and model checkpoint hashing
- ✓Subgroup stratification and equity performance evaluation
- ✓Out-of-distribution (OOD) stress testing archives
Trust Record
Issues and maintains the definitive Trust Record: what was empirically demonstrated, at what statistical confidence, under what exact conditions, and where it must not be applied.
- ✓Immutable versioning with cryptographic evidence provenance
- ✓Explicit 'Where NOT to Use' boundary enforcement
- ✓Reviewer-ready credibility dossier compilation
- ✓Traceable link back to raw validation datasets
Deployment Gate
Controls deployment into production scientific or clinical workflows with formal approval sign-offs, human-in-the-loop oversight assignments, and change controls.
- ✓Role-based clinical and scientific sign-off gates
- ✓Runtime input-boundary verification (rejection of out-of-scope inputs)
- ✓Escalation workflows for edge cases and low-confidence predictions
- ✓Audit-logged model version switches and configuration changes
Assurance Engine
Keeps credentials honest after go-live by continuously monitoring population drift, upstream vendor model weight changes, assay shifts, and triggering automated revalidation.
- ✓Automated covariate shift and clinical population drift detection
- ✓Vendor-change alerting (foundation model API updates, reagent shifts)
- ✓Configurable revalidation triggers and expiration timelines
- ✓Continuous performance degradation monitoring
Decision Utility
Measures whether the governed evidence actually reduced decision uncertainty and what that was worth in cycle time saved, development risk retired, and costs avoided.
- ✓Decision uncertainty reduction tracking vs baseline
- ✓Portfolio-wide time-to-decision and cycle acceleration metrics
- ✓Failed asset cost-avoidance modeling
- ✓Executive ROI and scientific governance reporting
Built for computational teams and regulatory reviewers alike.
Seamless programmatic integration with your ML pipeline paired with immutable GxP audit packages for regulatory reviewers.
Python SDK & CLI
Integrate Context-of-Use checks, dataset hashing, and trust verification directly into PyTorch, MLflow, and GitHub Actions CI/CD pipelines.
pip install celbridge-core21 CFR Part 11 Ready
Cryptographic hashing, timestamped electronic approvals, and tamper-evident audit dossiers structured for FDA electronic submission packages.
Customer VPC Isolation
Deployable directly into your AWS, GCP, or Azure VPC, or air-gapped on-premise infrastructure. Proprietary model weights and patient data never leave your boundary.
Ready to operationalize the 7-Link Evidence Chain?
Deploy against a single priority AI system in an 8-week structured sprint. 100% of pilot fee is credited toward annual enterprise licensing.