Opening new frontiers in scientific AI credibility.
A governed evidence platform and senior advisory practice for AI and emerging methods used in critical scientific, clinical, and regulatory decisions.
Operated across an active enterprise healthcare and clinical AI portfolio.
Deterministic evidence lifecycle maintained from model intake to decision utility.
Supported on translational readiness within NIH Common Fund Complement-ARIE.
Why scientific AI fails at the decision gate.
Billions are invested in AI discovery and diagnostic models. Yet when models reach pipeline progression gates, clinical deployment, or FDA filings, four recurring failures prevent adoption.
The Claim is Unbounded
Broad claims without formal operational perimeters.
Models deployed without explicit boundaries on chemical space, target classes, or patient cohorts fail catastrophically when applied outside narrow training sets.
The Comparator is Inherited
Benchmarked against legacy convenience data.
Validation studies benchmark candidate AI against convenient retrospective data rather than the active clinical controls that regulatory reviewers demand.
The Evidence Decays
Static validation for dynamic predictive systems.
Foundation model weight changes, reagent batch variance, and patient population shifts silently degrade performance without automated continuous detection.
The Decision is Unprotected
Subjective trust instead of cryptographic lineage.
When internal governance boards or FDA reviewers demand evidence, the absence of an immutable Trust Record leaves decision-makers exposed to scrutiny.
Evidence architecture engineered for three high-stakes domains.
Explore how the 7-link evidence chain and bounded Context of Use operationalize trust for computational biology, translational non-animal models, and clinical diagnostic AI.
Better therapeutics come from bounded models.
For computational chemistry, lead optimization, and biomarker discovery teams who need defensible evidence packages for internal portfolio governance and FDA regulatory filings.
Advancing an enterprise portfolio of governed AI systems.
True assurance requires both the software platform to maintain the evidence chain and the senior methodological judgment to challenge assumptions.
We deploy our platform alongside our advisory practice to ensure complete evidentiary and regulatory defensibility at every milestone.
Software You License
Deterministic evidence infrastructure to author Context of Use, hash validation datasets, issue verifiable Trust Records, and enforce continuous drift triggers across your AI fleet.
Machine-verifiable ontology binding user, decision, and biological perimeters.
Multi-lab reproducibility artifacts, code splits, and model checkpoint hashing.
Immutable Trust Records with SHA-256 evidence hashing and exclusion limits.
Real-time covariate drift triggers, GxP release sign-offs, and audit exports.
Judgment You Cannot License
Senior life-sciences expertise, study design rigor, and regulatory credibility review to set defensible evidence bars, challenge weak comparators, and stand up institutional AI review gates.
Authoring claims narrow enough to design an empirically definitive validation study.
Challenging convenient historical baselines to prevent silent decision bias.
Reviewing credibility dossiers through the rigorous lens of regulatory adjudicators.
Standing up cross-functional review boards, risk tiers, and escalation paths.
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
Trust Pilot: One System. Eight Weeks. A Live Evidence Chain.
Start with one high-priority AI or computational method. Deploy the platform, populate the 7-link chain end-to-end alongside our advisory practice, and issue your first Trust Record.
Claim
Context of Use authored in the platform. Decision, population, input constraints, and boundaries made mathematically and clinically explicit.
- ▸Structured Context of Use specification
- ▸Explicit boundary and non-indication perimeter
- ▸Risk tiering and regulatory precedent review
Evidence
Existing evidence, study data, and benchmarks mapped against the claim. Comparator, reproducibility, and applicability gaps registered.
- ▸Comparator defensibility audit
- ▸Reproducibility gap assessment
- ▸Applicability domain & subgroup performance mapping
Record
First Celbridge Trust Record issued. Deployment gate configured with human oversight roles and continuous revalidation triggers set.
- ▸First verifiable Celbridge Trust Record™
- ▸Configured deployment gate with escalation logic
- ▸Drift monitoring & revalidation schedule established
Utility
Baseline decision utility measures established, risk retirement quantified, and the multi-system portfolio expansion path priced.
- ▸Decision utility and uncertainty reduction scorecard
- ▸Executive governance presentation
- ▸Priced portfolio expansion blueprint (Pilot fee credited)
At the close of Week 8, you hold a complete, defensible evidentiary asset for your selected AI system.
Commercial Structure
Structured for immediate single-model evaluation with clear institutional scale paths across your AI fleet.
Trust Pilot
Platform deployment against a single critical method, with senior advisory practice alongside. Fixed fee, fully credited against a first-year portfolio license.
- Single AI or computational system
- End-to-end 7-link evidence chain populated
- First official Celbridge Trust Record™ issued
- Deployment gate & drift monitoring configured
- 100% of pilot fee credited toward annual license
Portfolio License
Platform access deployed across your full enterprise scientific or clinical AI portfolio. Named product owner, roadmap influence, and standing advisory allocation.
- Multi-system portfolio evidence governance
- Enterprise Claim Builder & Evidence Capture modules
- Role-based deployment gates and SSO integration
- Dedicated Celbridge advisory practice allocation
- Regulatory submission dossier exports
Continuous Assurance
Monitoring operated as a high-touch managed service: drift reviews, vendor-change triage, revalidation scheduling, and quarterly executive evidence briefings.
- Active drift and covariate shift monitoring service
- Upstream vendor API and model-weight change triage
- Automated and scheduled revalidation runs
- Quarterly executive credibility & risk briefings
- Reviewer audit support on demand
Our Mission
Our mission is to establish rigorous, repeatable, and verifiable evidence standards for computational intelligence and emerging methods in life sciences and healthcare.
Operated, then Productized
Designed and ran enterprise AI governance across an active 180-tool portfolio — managing intake, risk tiering, evidence standards, deployment gates, continuous monitoring, and retirement.
Federal Translational Science
Performer on the NIH Common Fund Complement-ARIE program, providing translational readiness assessment and methodological support within the NAMs Reduction to Practice Challenge.
Published Method
Author of two books and creator of the published seven-condition non-compensatory governance framework for predictive risk, plus active research on translational readiness.
Regulatory & Clinical Fluency
FDA risk-based AI credibility framework, FDA–EMA joint principles, DDT qualification, NIST AI RMF, ISO/IEC 42001 & 23894, OECD GD 34, ONC HTI-1, and Joint Commission standards.
Patrick R. Hogan, DHA
Dr. Hogan has designed and operated enterprise AI governance across complex healthcare portfolios and served as a performer on the NIH Common Fund Complement-ARIE program, providing translational readiness assessment and methodological support within the NAMs Reduction to Practice Challenge. Author of two books on risk governance and creator of the published seven-condition non-compensatory evidence framework.
NIH program experience informs Celbridge’s methodological expertise; it does not imply NIH endorsement of Celbridge products or services.
Let’s build trustworthy use together.
We help biopharma enterprises, health systems, federal consortia, and technology builders define the boundary, build the evidence, and maintain continuous trust.