Deterministic SDLC Guardrails for Healthtech

I deliver a single, highly opinionated service for software engineering organizations operating in Healthtech, SaMD, and highly regulated industries: The Regulatory Guardrail Build.

Instead of open-ended advisory retainers or generic prompt engineering, I deliver a fixed-scope, 14-day architectural build. I establish the deterministic regulatory domain vocabulary required to safely govern your internal AI coding agents (like Cursor, Claude Code, GitHub Copilot, or specialized agentic harnesses). This shifts your architecture from probabilistic post-generation testing to pre-generation deterministic verification, eliminating "rogue compliance logic" and massively reducing PR review bottlenecks.

My focus is exclusively on software engineering leadership (CPTOs & VPs of Engineering) at Healthtech and SaMD enterprises who are scaling AI-assisted SDLCs across their organization.

The core bottleneck: Your product squads are leveraging AI coding agents without machine-enforceable boundaries. These agents are confident but statistically probabilistic. They guess at unwritten clinical invariants and ePHI isolation boundaries, inventing parallel clinical workflows, unauthorized state transitions, and unapproved data paths. This instantly introduces regulatory drift, risks compliance breaches, and shifts a massive cognitive load onto your senior engineers to untangle hallucinated clinical logic during code review.

This build does not replace your internal harness; it deterministically grounds it in your actual regulatory constraints.

Most internal AI harnesses rely on probabilistic LLM validation or spreading compliance rules across dozens of disparate system prompts and context files. As you scale, FDA, MDR, and EU AI Act auditors will never accept a probabilistic LLM prompt as a system safety guarantee.

The Regulatory Guardrail Build introduces an executable single source of truth. By centralizing your regulatory invariants into a formal, machine-readable domain model, you create a strict baseline. We plug this directly into your existing agentic development workflow to trigger verification checks with mathematical determinism, forcing development agents to self-correct against HIPAA and MDR reality before a single line of code is written.

Absolutely not. Attempting to map an entire enterprise architecture is a multi-year trap that usually dies in committee. I do not boil the ocean.

Instead, we isolate a single, high-value regulatory slice (a Bounded Context). We do not map the universe; we pinpoint high-leverage targets and existing ground-truth rules (e.g., FHIR contracts, clinical data schemas, ePHI boundaries) where AI hallucinations are currently threatening compliance or clogging your pull requests.

During the build, I independently extract and formalize those specific clinical rules into a formal domain model. We let the LLMs guess the generic boilerplate. But for the critical ePHI state transitions and regulatory constraints, namely the 20% of your domain logic that carries 80% of the existential legal liability; we lock those down in the executable baseline.

The argument is Immediate Talent ROI and Intercepting Regulatory Tech Debt:

1. The Senior Engineer Tax (ROI): Stop forcing your most expensive asset to act as manual AI compliance-checkers. If a €150k+/year senior engineer spends just 5 hours a week untangling hallucinated compliance logic, they are wasting over €18,750 of capacity annually. This €19,500 fixed-scope investment pays for itself entirely within weeks through reclaimed developer velocity.
2. The Compliance Illusion (Mitigation): AI-generated code often looks structurally perfect while being semantically non-compliant. If a subtle, AI-generated compliance error slips past manual review and reaches production, the implications, including ePHI leaks, broken functional safety, or failed FDA/MDR audits, create unacceptable patient-safety risks and existential legal liability.

You are making a discrete Operational Velocity Investment to systematically block agents from drafting a non-compliant pull request in the first place, automatically producing a formal Compliance Bill of Materials (CBOM) to prove it.

The Regulatory Guardrail Build is designed with adaptability in mind. When regulatory requirements evolve, you only need to update the relevant portions of the formal domain model or invariants specification. This ensures that your AI-assisted SDLC remains compliant without requiring a complete overhaul of your internal harness or agentic workflows. The system continuously enforces the updated rules, maintaining deterministic compliance even as regulations shift.