AI coding tools let you build a working demo in days. But in healthcare, a working demo is not a shippable product. Whether you need to fix a vibe-coded app that's breaking under real usage or harden a prototype before launch, we close the gap — from vibe-coded prototype to compliant, resilient, production-grade system.
Book a 20-Min AssessmentClaude, Codex, Gemini, Cursor, and Copilot can generate code fast. But production healthcare systems need more than working code. When things start breaking, you need a consultant who can fix vibe-coded apps — not just explain what went wrong.
HIPAA, HITRUST, BAAs, audit trails, encryption at rest and in transit. Your demo didn't need them, but your production system does.
Ad-hoc queries and local databases don't survive real patient volumes. You need governed pipelines, a warehouse, and clean APIs.
Uptime SLAs, disaster recovery, observability, and incident response aren't features. They are table stakes for healthcare.
A five-step path that keeps your momentum while adding what healthcare demands.
We audit your prototype for production gaps: security posture, data flow, infrastructure maturity, and compliance readiness.
We design the target steady-state system: cloud topology, data models, service boundaries, and a compliance framework. Every subsequent decision has a blueprint.
Most good ideas require a robust data solution under the hood. We stand up pipelines, a governed warehouse, and clean APIs before touching the application layer.
Auth, RBAC, PHI handling, error budgets, integration tests. We retrofit production-grade concerns into the existing codebase without a rewrite.
CI/CD, observability, runbooks, and on-call handoff. We stay through the first production release and make sure your team can run it from there.
Before you can harden an application, you need to know where the data lives, how it moves, and who can see it.
We see it every engagement: teams build impressive front-ends on top of fragile, ungoverned data. Queries hit production databases directly. PII sits in plaintext logs. There is no single source of truth, so every dashboard tells a different story.
Our first move is always to stand up the data platform: ingestion pipelines, a governed warehouse (BigQuery, AlloyDB, or both), transformation layers with dbt, and clean, versioned APIs. Once this foundation exists, the application layer becomes dramatically easier to secure, scale, and certify.
This isn't a detour. It's the prerequisite. See our full engineering capabilities.
Some vibe-coded systems don't stop at the demo stage. They ship. They take on real users. And then things break: the prompts that generated the code are gone, the engineer who deployed it has moved on, and nobody can explain why the database schema looks the way it does. When you need to fix AI-generated code that's already in production, reverse engineering is step one.
We trace data flows end-to-end, map undocumented service dependencies, and reconstruct the implicit architecture the AI generated but never explained.
Cloud resources provisioned through AI-generated IaC (or worse, click-ops) get cataloged, tagged, and assessed for cost, security, and compliance posture.
We identify PHI exposure, auth gaps, missing audit trails, and unencrypted data paths that an AI coding tool would never flag on its own.
You get architecture diagrams, data flow documentation, and a prioritized remediation roadmap your team can actually execute against.
Reverse engineering isn't a failure state. It's the responsible next step when AI-generated code is already running in production and the team needs to understand, own, and harden what's there before building further.
We're not a staffing agency. We're a small, senior team that embeds with yours and stays until the system is live.
Common questions about taking AI-built prototypes to production in healthcare.
Vibe coding is the practice of using AI tools like Cursor, Copilot, or Claude to generate working software from natural-language prompts. The resulting code often works as a demo but lacks the security controls, compliance frameworks, and operational resilience that healthcare systems require — things like HIPAA-compliant data handling, audit trails, encryption, and proper authentication.
AI coding tools can accelerate development, but they don't produce HIPAA-compliant systems on their own. Compliance requires intentional architecture decisions: encrypted data stores, access controls, audit logging, BAA coverage, and proper PHI handling. We help teams take AI-generated code and retrofit these production-grade concerns without starting over.
Typical engagements run four to eight weeks depending on the complexity of the prototype and the compliance requirements. We start with a two-day assessment to map the gaps, then execute a structured hardening plan. Some teams have gone from demo to HIPAA-compliant production system in as little as six weeks.
Almost never. Our approach is to harden what you have, not rewrite it. We retrofit production-grade concerns — auth, compliance, data architecture, observability — into your existing codebase. A full rewrite wastes the speed advantage that AI coding tools gave you in the first place.
Yes. Fixing a vibe-coded app is what we do. AI-generated code often has subtle issues — race conditions, missing error handling, insecure defaults, brittle integrations — that aren't obvious until real users hit them. We diagnose the root causes, fix what's broken, and harden the system so the same class of problems doesn't recur. You don't need to start over; you need engineers who understand both AI-generated patterns and production healthcare requirements.
Tell us where your prototype stands and we'll map the shortest path to production.
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