AI coding tools let you build a working demo in days. But in healthcare, a working demo is not a shippable product. We help you 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.
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 the original context disappears: 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.
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.
Tell us where your prototype stands and we'll map the shortest path to production.
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