Strategic technology guidance for medical research programs — including sovereign AI infrastructure for compliant, institution-controlled research environments where IRB, HIPAA, FDA Part 11, and grant terms are non-negotiable.
Schedule a CallMedical research programs now operate as data-intensive organizations. Clinical cohorts, laboratory assays, imaging, genomics, and real-world evidence all generate large, interconnected datasets. These must be secure, reproducible, auditable, and compliant.
Most research groups were not designed for this level of technical complexity.
As a result, Principal Investigators and their teams spend significant time working around fragmented systems, manual data transfers, inconsistent analysis environments, and unclear governance. This slows insight generation, introduces compliance risk, and creates operational bottlenecks that reduce the pace and impact of discovery.
These challenges do not come from lack of scientific rigor. They come from a mismatch between modern research demands and the technical infrastructure that supports them.
I work with medical research leaders who are responsible not only for scientific outcomes, but also for the systems that make high-quality research possible.
If your research involves data infrastructure, analytics workflows, regulatory compliance, or scaling your digital systems to match scientific ambition, this work is intended for you.
Research programs are under pressure to adopt AI for cohort analysis, multi-omics integration, imaging review, literature synthesis, and trial operations. Most consumer and SaaS AI options fail HIPAA, IRB, FDA Part 11, and grant compliance tests simultaneously. Sovereign AI is how you bring AI capability into your research program without breaking the regulatory framework that lets your program exist.
Most existing informed consent forms do not cover third-party AI processing of patient data. Using consumer or SaaS AI on study data can place your program out of compliance with its own approved protocol — an IRB violation regardless of de-identification.
Re-identification attacks on "de-identified" data fed to commercial AI are well-documented. PHI leakage through prompts is a notifiable HIPAA incident. Once data is in a third-party model, it cannot be retracted.
Closed-weight models with vendor-controlled versions mean published results cannot be reproduced. Journals are starting to reject AI-assisted analyses without provenance. Vendor model deprecations destroy reproducibility.
NIH, NSF, DARPA, DOE data governance terms are increasingly incompatible with consumer AI. Institutional review of research AI usage is intensifying. A grant violation can mean clawback and ineligibility.
Train AI across collaborating institutions without sharing raw data. The standout pattern for rare disease consortia — honor HIPAA, IRB, DUA, and consent boundaries while still capturing multi-site statistical power.
Institutional HPC, sovereign-cloud research enclaves (AWS GovCloud, Azure for Research), or on-prem GPU. Compatible with existing IRB-approved data handling environments.
Open-weight foundation models plus biomedical-tuned variants you can inspect, fine-tune on your data, and version. No black boxes in your research pipeline.
Model registry, version lineage tied to published results, FDA Part 11 compliance, audit trails integrated with institutional compliance reporting.
Every prompt, response, model version, and data input traceable. When the IRB, the journal, or the auditor asks "what did the AI do?" — you have the answer.
Current-state assessment, IRB and grant compliance review, build-vs-buy-vs-host recommendation, briefing for IRB and grants office.
Reference architecture tailored to your research program, infrastructure design, model selection, integration with REDCap/LIMS/EHR/imaging.
Hands-on coordination across PI, IRB, IT, security, grants office, and data team. Governance program build-out with FDA Part 11 and HIPAA-aligned audit trails.
My role is to remove technology as a source of friction so your team can focus on science. The outcomes below reflect what research teams need to operate with confidence and scale.
Standardized and auditable workflows that allow your team to reproduce results, validate findings, and support publication or regulatory review.
Cohesive data pipelines that connect instruments, clinical systems, analytical tools, and storage in a repeatable and governed way.
Clinical, laboratory, imaging, and omics data connected into a unified environment rather than spread across disconnected tools.
Architecture and processes that align with HIPAA, IRB requirements, and grant reporting criteria without adding manual overhead.
Less time spent on routine technical challenges and more time turning results into publications, grant deliverables, or clinical readiness. Reduced technical delays and fewer handoffs so your team can move more quickly from raw data to analysis and interpretation.
AI and ML capabilities deployed under your institution's governance, with data residency, model auditability, and reduced dependence on external cloud AI providers.
Technology solutions that grow with your team, supporting expanded cohorts, additional modalities, additional study sites, or future collaborations without re-engineering your environment.
Technology plans that align directly with your scientific and funding goals, including milestones that support grants, publications, and clinical translation.
Each of these outcomes contributes to a stronger foundation for research productivity and more predictable execution across your programs. These outcomes are not theoretical. They are the result of hands-on leadership, technical expertise, and a deep understanding of the research environment. When you have the right technology foundation, your team can focus on what matters most: advancing science and delivering impact.
A 30 minute call helps identify your top technical risks and whether fractional CTO leadership can deliver immediate, measurable benefit.
Schedule an introductory callSovereign AI means your institution retains full control over the AI models, the patient and study data they consume, and the audit trails they produce — instead of routing sensitive research data through external AI providers. For research programs, it is the difference between adopting AI responsibly and creating an IRB violation, a grant compliance failure, or a publication crisis. Regulators and funders are catching up fast (NIST AI RMF, FDA AI/ML guidance, NIH data policies, IRB protocol updates). A sovereign AI strategy lets you say yes to AI without saying no to the framework that funds and authorizes your work.
Enterprise tiers narrow the risk but do not eliminate it. Patient and study data still leaves your institutional perimeter. BAA coverage is incomplete. Audit trails are partial and vendor-controlled. Model versions change without your consent — which destroys reproducibility for any published findings. Most existing IRB consent forms do not cover third-party AI processing. Enterprise SaaS AI is a reasonable choice for low-sensitivity tasks (literature search, drafting non-protocol documents) and a wrong choice for anything touching study data, PHI, or regulatory submissions.
A policy without architecture is hope. PIs and research staff are pasting de-identified study data into consumer chatbots every day — your policy did not stop it. Sovereign AI is the technical answer that makes the policy enforceable: sanctioned tools that PIs actually want to use, blocking or monitoring for unsanctioned ones, and an inventory of where AI is actually being used in your research program. Policy + sovereign infrastructure + governance is the complete picture.
I coordinate with your institutional IT and compliance teams while creating a clear boundary for research-specific infrastructure that supports scientific workflows and audit requirements.
Yes. I translate technical deliverables into grant-aligned language and provide realistic effort estimates for inclusion in budgets — including sovereign AI infrastructure as a grant-allowable data governance investment.
Both. Engagements can be advisory, hands-on implementation, or hybrid depending on your team's capacity.
25+ years of experience in mission-critical, highly regulated environments.
Proven track record: 99.99%+ system uptime, 95% downtime reduction & zero HIPAA audit findings
Trusted advisor to cross-functional teams, IT, clinical, legal, and research, bridging the gap between technology and science.
Specializing in: Translational and clinical research infrastructure, rare disease and metabolic disorder studies, and scalable, compliant data platforms.