AI Product Engineering
Production AI applications, built end-to-end by the same team: frontend, backend, AI, infrastructure.
Most AI features ship as a side project bolted onto a real product. We build the AI product as a real product (auth, infra, evals, on-call runbook), so it gets the same engineering rigor as the rest of your stack. The on-call engineer at 3am can read the runbook. The compliance reviewer can read the audit logs. The user doesn't notice anything weird.
Who AI Product Engineering Is For
- Founders shipping a V1 of an AI-integrated product and need a team that can own frontend, backend, and AI together.
- Companies extending an existing product with AI features and need engineering parity, not a side project.
- Healthcare and regulated teams who need HIPAA-grade build practices from day one, not retrofitted.
How AI Product Engineering Works
- Step 01
Spec and architecture
Translate the product requirement into a buildable spec, decide the integration boundaries, and pick the stack with operating cost and team familiarity in mind.
- Step 02
Build in shippable slices
End-to-end vertical slices (frontend, backend, AI, infra), so something real is in production by week three or four, not just scaffolding.
- Step 03
Production hardening
Auth, observability, evals on AI surfaces, deployment pipeline, and the runbook the on-call engineer will need at 3am.
- Step 04
Hand-off or operate
Either transition to your team with documentation and pairing, or stay on as an extension of the team for ongoing build.
What you get
- Production application across the full stack: frontend, backend, AI, infra.
- Deployment pipeline, auth, observability, and runbook.
- Eval coverage on AI features with regression gates.
- Hand-off package or ongoing partnership, your choice.
Where we've shipped this
All case studies →AI Scribe & Telehealth Platform
Healthcare Startup · AI Scribe & Telehealth
Built an AI scribe, voice agents for patient intake, and a HIPAA-compliant telehealth platform from the ground up.
- V1 shipped in 4 weeks (web, iOS, iPadOS)
- 500+ consultations/month
Radiology Auditing Case Management Platform
Radiology Auditing Service · Case Management
Full-stack case management platform with intelligent case distribution, audit workflows, and hospital dashboards.
- 50% faster audit turnaround
- 1,500+ cases processed/month
Frequently asked questions
How is AI product engineering different from hiring AI engineers?
Most AI features ship as a side project bolted onto a real product. We build the AI product as a real product, with auth, infra, evals, runbooks, and the same engineering rigor as the rest of your stack. Hiring an AI engineer gets you AI code. Hiring us gets you a shipped, on-call-ready product.
Do you build the frontend too, or only the AI parts?
Frontend, backend, AI, infrastructure, deployment, observability. The whole stack. Most AI work breaks at the seams between the model and the rest of the product, and a team that only builds the AI parts cannot fix those seams. We are full-stack on purpose, not because we ran out of specialists.
Who owns the code at the end of the engagement?
You. Source code, infrastructure-as-code, deployment pipelines, and runbooks live in your accounts and repos from day one. There is no proprietary platform you have to keep us around to maintain. Handoff is a defined window, typically 30 days of post-handoff support, so stabilization is normal work, not a cliff.
Can you work with our existing engineering team, or do you take it over?
We work alongside the existing team in almost every engagement. The build is shared work, with code reviews going both directions. Your engineers learn the AI-specific patterns by reading and reviewing real production code, not from a workshop. By the end, your team can run the system without us.
Let's build something that actually works.
Tell us where you are and what you need. We'll come back with a clear, honest plan within 48 hours.