Deterministic AI Guardrails for Regulated SDLC

I deliver a single, highly opinionated service for software development organizations operating in Healthcare, Pharma, and Mission-Critical industries: The Domain-Grounded Guardrail Build.

Instead of open-ended advisory retainers or generic prompt engineering, I deliver a fixed-scope, 2-week architectural intervention. I establish the deterministic domain vocabulary required to safely govern your internal AI coding tools (like Cursor, Claude Code, GitHub Copilot, or SpecKit). This shifts your architecture from post-generation testing to pre-generation deterministic constraints, eliminating "rogue regulatory logic" and massively reducing PR review bottlenecks.

My focus is exclusively on software engineering leadership (CPTOs & VPs of Engineering) at MedTech, Pharma, and regulated enterprises who are scaling AI-accelerated software development across their organization.

The core bottleneck: Your product squads are leveraging AI coding agents without centralized regulatory boundaries. These agents are confident but statistically probabilistic,they invent parallel clinical rules, unauthorized state transitions, and unapproved data models. This instantly compounds technical debt, risks compliance breaches, and shifts a massive cognitive load onto your senior engineers during code review.

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

Most internal AI harnesses rely on post-generation testing or spreading compliance rules across dozens of disparate system prompts and context files. As you scale across multiple squads, managing that "prompt drift" becomes an impossible compliance burden.

The Domain-Grounded Guardrail Build introduces an executable single source of truth. By centralizing your regulatory invariants (e.g., HIPAA, 21 CFR Part 11) into a formal, machine-readable domain model, you create a strict baseline. We wire this directly into your workflow to intercept AI hallucinations at the specification level.

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, I apply the Bounded Context principle from Domain-Driven Design (DDD). We do not map the universe; we isolate a single, high-risk platform slice where AI hallucinations are currently threatening compliance or clogging your pull requests.

During the sprint, we extract 8 to 12 core operational rules for that specific boundary. We let the LLMs guess the generic boilerplate. But for the critical state transitions and regulatory constraints, the 20% of your domain logic that carries 80% of the risk, we lock those down in the executable baseline.

The argument is Senior Engineering ROI and Catastrophic Risk Mitigation:

1. The Senior Engineer Tax (ROI): Every hour a senior engineer spends debugging AI-hallucinated clinical logic in a Pull Request is an hour stolen from shipping high-value roadmap features.
2. The Runtime Risk (Mitigation): AI-generated code often looks structurally perfect while being semantically wrong. If a subtle, AI-generated compliance error slips past manual review and reaches production, the implications, ePHI leaks, broken functional safety, or failed FDA audits, are catastrophic.

You are making a discrete, one-time Operational Velocity Investment to systematically prevent unauthorized domain logic from ever reaching code review.