AI systems your business can actually run on.
We do the unglamorous 80% — context, harnesses, evals, ops — that turns a model into something that ships and keeps shipping. And you own all of it.
Systems engineering around frontier models.
Strip away the positioning: the real work isn’t training models — it’s everything that turns one into a system that runs every day. In rough order of where the value goes.
Context & data engineering
Getting your data into the system — RAG, vector stores, document processing, knowledge graphs, connectors. This is most of the work, and most of why pilots fail.
Agent & harness engineering
The orchestration layer: tool-use, multi-step planning, memory, routing, retries, guardrails, human-in-the-loop. The harness is the product — the model is a component.
Evals & reliability
The test, monitoring and observability layer that turns an impressive demo into something that runs every day. The single biggest thing separating teams that ship from teams that don’t.
Model selection & routing
The right model for each task, model-agnostic, with fallback. Usually frontier API models — Claude, GPT, Gemini — or your own open models where it matters.
Deployment, security & ops
The production system: scaling, access control, secrets, audit. Built to survive contact with the real world, then handed to you to run.
Fine-tuning — selectively
Not the headline. We fine-tune when it earns its keep: cost/latency distillation, structured-output reliability, voice and persona, a real data moat, or sovereignty — open models you host, tuned on your data.
Do we fine-tune models? Selectively — and we’ll tell you when not to.
- Frontier API models + good RAG, prompting and tool-use beat a fine-tune for most tasks — cheaper and faster to iterate.
- Fine-tunes go stale every time the base model improves, which is constantly.
- It’s expensive, brittle, and needs real data volume.
- Cost & latency — distilling a frontier model’s behaviour into a small, cheap one for high-volume tasks.
- Structured-output reliability, or a narrow repetitive task.
- Voice, style and persona.
- A real proprietary-data moat you own.
- Sovereignty — an open model you host, fine-tuned on your data, so nothing leaves your infrastructure.