How we work
A fixed-scope path from a real business problem to a system your operations team runs. No open-ended discovery phases.
Four phases, fixed scope
Assess
We map the workflow, the data, the risk tier and the economics. You get a build/no-build decision with a business case.
Governed pilot
A working agent on your data with an evaluation set, approval gates and a measured baseline — not a chatbot demo.
Deploy
Integration with your systems, hardening, observability, runbooks and training for the team that will own it.
Operate & improve
Managed service or handover. Weekly eval review, drift and cost monitoring, model upgrades without regressions.
How we think about the stack
Model-agnostic by design. We pick the model per task and keep the switching cost low, so you benefit from every release instead of being trapped by one.
- Models
- Frontier models through enterprise APIs, open-weight models on your cloud or on-prem, and fine-tuned small models where latency, cost or data residency decide.
- Orchestration
- Graph-based agent runtimes, tool calling over standard protocols, durable workflows for long-running tasks and retries.
- Retrieval
- Hybrid lexical and vector search, re-ranking, structured knowledge where the domain needs it, and citation-first answer generation.
- Evaluation
- Golden sets built with your domain experts, LLM-as-judge with human calibration, regression gates in CI before any prompt or model change ships.
- Observability
- Full traces per run, cost and latency per step, drift alerts, and dashboards your risk team can read without an engineer.
- Deployment
- Microsoft Azure, AWS, Google Cloud or your own data centre — with the same evaluation and audit discipline on each.
Start with the two-week assessment
You get a build/no-build decision with a business case — and you keep it either way.