03AI / ML← All work
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.
- 01
LLM-powered features
Chatbots, copilots, document Q&A, with evals.
- 02
RAG pipelines
Retrieval that is actually relevant, not just embedded.
- 03
Agent systems
Tool-using agents with guardrails and observability.
- 04
Custom ML
Classification, forecasting, ranking, when an API is not enough.
02Stackboring
boring
on purpose.
- Python
- PyTorch
- LangChain
- LlamaIndex
- OpenAI
- Anthropic
- Pinecone
- FastAPI
- Modal
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
04Phases
Full process →same five phases.
- 01
Discovery & audit
1 week · audit
- 02
Scoping & architecture
1–2 weeks
- 03
Build
2-week sprints
- 04
QA & handoff
1–2 weeks
- 05
Post-launch
Optional
asked often.
Depends on cost, latency, evaluation, and how much you trust them. We have shipped with all three.
When it earns its keep. Often prompt engineering + RAG + evals beats fine-tuning at one-tenth the cost.
We treat evals as first-class. No production LLM ships without a regression-test set.
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