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Applied AI Architect · HealthTech Strategist · Independent Researcher

AI systems built to survive contact with reality.

DeNovo Innovations Group architects, builds, and evaluates applied AI and healthcare technology systems where evidence, provenance, human judgment, and reliability matter.

Explore the workView research & evidence

Bringing a specific problem? See how an engagement starts →

Proof

Three things to look at first

Healthcare policy intelligenceWhich policy revision applied, and when — change-aware retrieval over manuals with hundreds of revisions.Read more →
AllMyMascotsBuilt end to end by a human-AI hybrid system: a production generative-AI platform with multi-provider inference, failover, preference learning and evidence-gated release.Read more →
The DeNovo toolkit
Diagram of the DeNovo human-AI engineering system: human direction, role-separated agents, repository context, verifiers, hooks and retained evidence; solid borders mark implemented parts and dashed borders planned ones.
The DeNovo toolkit. Solid border = implemented, dashed = planned.View full toolkit diagram ↗See it on Evidence & Provenance →
Public comments to NIST and FDAEvaluator integrity, dated evidence and action-level assurance — DOI-archived contributions on AI evaluation.Read more →

Selected work · Healthcare & MedTech

Technology grounded in physiology

Vital Intelligence · Co-founder / CTO (contract + equity) · Mar 2020 – Mar 2021Contactless vital signs from videoContactless sensing systems designed, integrated and deployed: computer vision, RGB/IR imaging and signal processing for physiological measurement.Computer visionRGB/IR imagingEdge computeHistorical · assets acquired by Draganfly (2021)Read the case →betweenMD · Chief Medical Technology Officer (founding member) · Mar 2021 – Aug 2023HypnOS nightmare detection and remote monitoringLed the invention, intervention design and algorithm research for HypnOS, a nightmare-detection approach that adapts throughout each night to the sleeper's own physiology; prepared its Class II strategy; built the reimbursement models for a remote-monitoring program.Nightmare detectionWearablesClass II planningHistoricalRead the case →

Engagements

Where I come in

For founders raising a seed or Series A round, investors doing technical diligence, and healthcare, MedTech and AI leaders facing a decision that has to hold up.

  • Technical architecture & implementationA system that has to work on the day: the architecture, the build plan and the build.
  • Fractional technical and AI leadershipSenior technical judgment at the table without a full-time hire.
  • Healthcare & MedTech commercialization strategyProduct, clinical, regulatory and reimbursement decisions made as one plan.
  • Technical diligenceWhat a system actually does, measured against what is claimed, before capital is committed.
  • Evidence & evaluationEvaluation design and provenance for AI claims you will need to defend.
Start a conversationBring the problem, the decision it supports and the evidence you have.

How I think

From electrons to evidence.

Medicine first, then every layer of infrastructure it runs on, then the data, the models and the AI systems that reason over them. Failures live at the seams between layers, and that is where I look first.

  1. 01Medicine & clinical research
  2. 02Electrical & physical systems
  3. 03Networks & connected environments
  4. 04Sensing & edge
  5. 05Data & cloud
  6. 06Machine learning
  7. 07AI systems & evidence

The seven layers and the four habits that travel between them →

Three questions before an AI answer is trusted

My architecture asks eleven questions in order, from who may ask to whether the answer would change under different assumptions. Three of them:

  • 03Authority resolutionWhich source is authoritative for this question, and why.
  • 04Temporal resolutionWhat was true at the time, and what has replaced it since.
  • 07VerificationWhether the check actually tests the intended system, and could fail.

These questions guide how I design and evaluate systems; the answers depend on the system and its evidence. All eleven questions →

Continuous Behavioral Access Management

CBAM — Continuous Behavioral Access Management: concentric trust spheres from the untrusted web through tools and MCP servers, the Internet of Agents with private agent clouds, agent networks and memory networks, to a governance and proof layer.
Proposed concentric trust spheres and system architectureResearch vision: Continuous Behavioral Access Management and the Internet of Agents.

Research agenda: four programs, twenty-five topics →Publications and public comments →

Work With Me

Selective collaboration where applied AI, healthcare technology, evidence, and real-world implementation intersect.

Healthcare is the focus, not the boundary. Same problems, every discipline; let's solve them together.

Engagements run through DeNovo Innovations Group. Write with what you are working on and the decision it supports.

Engagement lanes and contactcontact@denovoinnovationsgroup.com