A model call is easy. An AI system — with latency budgets, evaluation suites, fallbacks, cost ceilings, and an upgrade story — is engineering. That part is no different from any other discipline the firm practices.

• Retrieval and context pipelines

  Embedding and reranking services, chunking strategy, and evaluation harnesses, so answer quality is measured rather than sampled by vibes.

• Model serving and gateways

  Open-weight serving on owned GPUs, API gateways with per-team budgets and full request logging, and routing policies that survive a provider outage.

• Agents and workflow automation

  Model-backed automation wired into real operational systems — tickets, documents, messaging — with typed decisions where a decision is all you need.

• Evaluation and calibration

  Task-specific eval suites and threshold calibration against client data, so a model swap is a decision, not an incident.

Hosting → App development →