AI you can actually run.
LLM applications, RAG pipelines, agent systems, custom ML. Engineered for production load, with evals you can rerun after every prompt change.
Concrete deliverables.
- LLM-powered features
- Chatbots, copilots, document Q&A, with evals.
- RAG pipelines
- Retrieval that is actually relevant, not just embedded.
- Agent systems
- Tool-using agents with guardrails and observability.
- Custom ML
- Classification, forecasting, ranking, when an API is not enough.
The same five phases, every time.
AI / ML work runs on the identical schedule as everything else we build. Nothing about this discipline gets a special process.
- 01
Discovery
We map the problem with you and leave you a written brief you keep either way.
1 week · fixed-bid - 02
Scoping & architecture
A statement of work you approve before anyone writes code.
1–2 weeks - 03
Build
Two-week sprints, a demo every Friday, commits landing in your repo.
2-week sprints - 04
QA & handoff
Real-device QA, automated checks, and docs your team will actually read.
1–2 weeks - 05
Post-launch
Hand it off cleanly, or keep us on a retainer. No lock-in either way.
Optional
Boring tech.
Picked on purpose.
Not married to any of it. We use what fits, and what your team can take over later.
When to engage us. And when not to.
Good fit
- You have a real problem and real data
- You want the behavior measured before it ships
- You want production-grade serving, monitoring, and cost controls
Not a fit
- You want a research lab, not a deliverable
- You want to train a frontier model from scratch
- You have no idea what your data looks like
Questions, answered.
Depends on cost, latency, evaluation, and how much you trust them. We have shipped with all three.
Ready to ship AI / ML?
Tell us about it. We respond within one business day. No demo decks, no discovery surveys, just a real conversation with engineers.