Judgment you cannot license as pure software.
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.
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.
The Seven-Condition Non-Compensatory Model
Created and published by Dr. Patrick R. Hogan, this non-compensatory framework ensures that models cannot mask critical safety or scientific deficiencies behind aggregate performance metrics.
Explicit Context of Use
No model is valid in the abstract. Boundaries on chemical space, target classes, patient cohorts, and assay types must be mathematically defined.
Defensible Comparator Baseline
Performance must be measured against active controls and real-world standards, not convenient retrospective benchmarks.
Non-Compensatory Evidence Rules
High accuracy in one metric cannot compensate for catastrophic failure on clinical safety, toxicity, or fairness criteria.
Cryptographic Evidence Lineage
All training data splits, hyperparameters, and evaluation code must be hashed and immutably linked to the Trust Record.
Human Oversight & Intervention Protocol
Explicit clinical or scientific escalation paths, override telemetry, and named accountable individuals must be assigned.
Continuous Covariate Drift Gates
Automated statistical triggers must force re-validation when real-world distributions drift from the original Context of Use.
Quantified Decision Utility
The model must demonstrably reduce uncertainty, accelerate milestone velocity, or save capital compared to traditional methods.