AI Systems
& Agents.
Autonomous agents, RAG pipelines, and custom LLM workflows that actually ship to production. With eval harnesses, observability, and a sane cost ceiling.
Real software, not
another POC slide deck.
Agent orchestration
Multi-step agents built with LangGraph. Branching logic, retries, human-in-the-loop checkpoints.
RAG over your data
Document ingestion, chunking, embeddings, hybrid search, re-ranking. Benchmarked against your real queries.
Tool-use & function calling
Typed, tested, instrumented. When it goes wrong, you know which call broke.
Eval harness & CI
A test suite for prompts. Every change runs the eval set. You see regressions before users do.
Hand-off to humans
When the model isn't confident, the question goes to a person. With context, not a dump.
Cost & latency dashboards
Token counts, latency, cost ceiling that pages someone if you cross it.
Built to be useful,
not impressive.
The demo-able AI agent is a different animal from the useful one. The first one gets your team excited; the second one quietly removes a real cost from the business.
Our default is to start with the smallest possible agent that solves one named problem, instrument it carefully, and grow it from there. No giant orchestration graphs on day one.
Most projects ship a first agent in two to three weeks, then iterate weekly. The eval set comes first.
The complete set
of deliverables.
- § 01
Discovery document
A written read of where the agent should sit.
- § 02
Eval set
50–200 real queries from your team.
- § 03
Working agent
In your stack, in your cloud.
- § 04
Admin dashboard
Token usage, costs, conversation logs.
- § 05
Documentation
For your team to maintain it. Plus a video walkthrough.
- § 06
Retainer for the oak
Optional. But most clients keep us on for the long arc.
§ Sapling
A single agent or workflow. Discovery + build + eval + deploy in 3 to 5 weeks.
§ Young oak
A full AI product: agents, RAG, dashboards, API. 8 to 12 weeks.
§ Tending the oak
Monthly retainer for tuning, new features, eval expansion.