Artificial intelligence that reaches production.
We build LLM applications, retrieval systems, and ML pipelines that survive contact with real traffic — instrumented, evaluated, and operated. Demos are easy; we ship the version that holds up.
Evals before features.
Most AI projects stall because nobody can say whether they got better. We start from a measurable outcome, build the evaluation harness first, and only then ship features against it. Retrieval, agents, and fine-tuning are means — the eval is the contract.
- [ 01 ]Outcome and eval defined before a line of model code
- [ 02 ]Retrieval and grounding over raw generation
- [ 03 ]Human-in-the-loop where stakes are high
- [ 04 ]Cost and latency budgets treated as first-class
What we build.
- LLM & agent applications
- Retrieval-augmented generation
- Semantic & hybrid search
- Document intelligence
- Computer vision
- ML pipelines & training
- MLOps & evaluation harnesses
- Model serving & inference
From prompt to operated model.
- [ 01 ]
Frame
We define the outcome, the eval that measures it, and the smallest slice worth shipping.
- [ 02 ]
Ground
Retrieval, data pipelines, and guardrails — so the model answers from your reality.
- [ 03 ]
Ship
Production inference, instrumented for cost, latency, and quality from day one.
- [ 04 ]
Operate
We watch the evals in production and harden against drift, then hand over or run it with you.
What the work returns.
Numbers from AI systems we run in production today.
AI in production.
AI document intelligence
Extraction and human-in-the-loop review over a million-plus shipping documents.
NFC payments for 80,000 attendees
Wristband top-up, offline-tolerant terminals, and real-time settlement across a 3-day festival.
Have an AI system to build?
Tell us the outcome you need to move. We'll come back with an eval, an architecture, and a plan.
Start a project