What We Do
Four Practices, One Engineering Standard
We work across the whole path from problem statement to production system. Most engagements touch more than one of these, and all of them are held to the same bar: it ships, it is measured, and somebody other than us can run it.
Practice 01
Multi-Agent Systems
Orchestrated pipelines where each step is a specialised agent with a defined contract, a budget and a trace. We design the topology, then build the plumbing that makes it debuggable at three in the morning.
Orchestration & routing
Tool and API contracts
Tracing & cost control
Failure and retry design
Practice 02
Retrieval & Knowledge Systems
Getting the right context in front of the model is most of the work. We build the ingestion, chunking, indexing and reranking layers — and measure retrieval quality separately from generation quality, because they fail for different reasons.
Hybrid & semantic retrieval
Document ingestion pipelines
Reranking & context assembly
Domain knowledge bases
Practice 03
Applied Machine Learning
Model work that starts from the decision it has to support. Fine-tuning where it earns its cost, classical models where they beat an LLM, and honest analysis of when neither is the answer.
Fine-tuning & adaptation
Structured extraction
Forecasting & classification
Data & feature pipelines
Practice 04
Evaluation & Reliability
The part most teams skip. Golden sets, offline evals, regression gates in CI and production tracing — so a change to a prompt is a measurable engineering decision instead of a guess.
Eval harnesses & golden sets
Regression gates in CI
Production observability
Latency & spend budgets
Everything We Build, You Own
No wrapper platform, no per-seat licence, nothing that stops working when the engagement ends.
01
Running code, in your repositories
Source, infrastructure and deployment land in your accounts as we go. Nothing depends on us being here next quarter.
02
An evaluation set you keep
The cases that define whether the system is working, written down and runnable — the thing that makes the next change measurable.
03
The write-up
What we built, what we tried that failed, and where the design will need attention as load grows.
Three Ways to Start
Most work begins with a pilot. It is deliberately small, ends in a written recommendation, and you keep the evaluation set whichever way it goes.
Pilot
2–3 weeksIs this worth building at all?
- One use case
- One measurable KPI
- Evaluation set you keep
- Written go / no-go
$1,000 – $3,000
Build
4–6 weeksMake it survive real users.
- Production deployment
- Tracing and monitoring
- CI regression gates
- Handover documentation
$3,000 – $8,000
Partner
OngoingKeep it improving after launch.
- New workflows
- Retraining and evals
- Cost and latency tuning
- Embedded with your team
$8,000+
Indicative ranges. Scope, data access and integration surface move the number more than duration does — we quote against a written brief.
Tell Us What Isn't Working Yet
Send the shape of the problem, the data you have and what a good outcome would look like. We will reply with an honest read on whether it is worth building — including when it is not.