Product Engineering
Full-stack teams shipping AI-native SaaS — from data pipelines to interfaces — under our own playbooks.
Most AI demos die between Jupyter notebook and production. Our engineering practice exists to close that gap: data, model, API, UI, deployment, observability — one squad, one cadence, one production system.
What we mean by AI-native
An AI-native product treats the model as a runtime, not a feature. That means the schema, the UX, the error states, and the failure modes are all designed around something probabilistic. We've shipped enough of these to know where it breaks — and we've written the playbook for the parts that always do.
The stack we typically deliver
Data pipelines
Ingest, validate, transform, and version the training and prompt data the system depends on.
Model integration
Provider-agnostic routing, retry policy, structured outputs, and graceful degradation.
Web & API surface
Next.js or Remix front ends, FastAPI or Node back ends, typed end-to-end.
Auth, billing, audit
The boring parts every SaaS needs, wired right so they're not the bottleneck.
Eval-driven shipping
No deploy goes out without passing the offline eval suite tied to the live benchmark.
Observability
Traces, latency budgets, and cost dashboards from the first week, not the second incident.
Our shipping cadence
- 01
Week 0
Joint product spec. UX wireframes. Eval contract.
- 02
Weeks 1–2
Vertical slice in production. Internal users only. Eval running.
- 03
Weeks 3–8
Weekly demos. Weekly deploys. Customer interviews every Friday.
- 04
Week 8+
External beta. On-call rotation handed back to your team.