AI Services — Run & Scale
Everyone bought the AI coding tool licenses. Output didn't actually change.
AI-assisted development can meaningfully speed up a team — but only if it changes how the team actually works: what gets reviewed differently, what gets automated, where a developer's judgement is still the thing that matters. Handing engineers a Claude Code or Copilot license without changing workflow around it usually produces marginal gains and a new set of code-review headaches.
How we run it
Assess, build, scale
AI Readiness Review
- Codebase and workflow audit — where AI-assisted development would genuinely help versus where it would introduce risk, specific to your PMS/HIS/IoT stack.
- Tool evaluation matched to your team's languages, frameworks, and existing CI/CD setup.
- Adoption roadmap — a realistic plan for rollout, training, and the review process changes that need to go with it.
Rollout & Enablement
- Team onboarding to AI coding tools with real project work, not generic tutorials.
- Updated code review practices — what an AI-assisted PR needs that a manual one didn't, particularly around security review for patient-data-adjacent code.
- Prompt and workflow playbooks specific to your codebase and conventions, so output is consistent across the team rather than developer-by-developer.
- Guardrails for what AI tools should and shouldn't touch — compliance-sensitive modules get different rules than internal tooling.
Ongoing AI Engineering Support
- Quarterly workflow review as tools and your codebase both evolve.
- New team member onboarding as you hire.
- Advisory access for architecture decisions involving AI-assisted development at scale.
What actually changes
What actually changes
- Code review shifts from “did they write this correctly” to “did they specify and verify this correctly” — a different skill, worth training for explicitly.
- Junior engineers move faster on boilerplate and integration work, freeing senior engineers for architecture and the genuinely hard problems — like device integration edge cases or HIS data model quirks.
- Security review gets more deliberate around AI-generated code touching patient data or payment flows, not less.
FAQ
Common questions
Will this reduce the size of our engineering team?
The goal is throughput on the backlog you already have — faster delivery of the integration, dashboard, and feature work that's queued up, not headcount reduction. That's a conversation specific to your situation, not a default outcome.
Which AI coding tools do you recommend?
It depends on your stack and existing tooling — we evaluate against your actual codebase during the Assess phase rather than defaulting to whichever tool is trending.
Is AI-generated code safe for compliance-sensitive systems?
With the right review process changes, yes — but those changes need to be deliberate, not assumed. That's a core part of what the Build phase sets up.