Production AI engineering
on Claude, OpenAI, and Gemini.
Model providers we ship on
Claude
Long-context reasoning, tool use, and structured outputs. Where output quality has to clear an eval gate, this is usually the call.
OpenAI
GPT models for breadth and routing, embeddings for retrieval, fine-tuning where a narrow task earns it. The default for the hot path on most engagements.
Gemini
Million-token context, multimodal inputs, and Vertex deployment. The right call when the workload is GCP-native or context lengths are extreme.
We pick the provider per engagement — sometimes per route — based on your data, latency budget, and compliance posture. No off-the-shelf templates; every system is purpose-built around the constraints that actually bind.
Service pillars
By the numbers
Featured case study
All case studies →Multi-tenant inference platform: 71% cost reduction at 4× the traffic
An internal inference platform for a SaaS company. Continuous batching, semantic caching, KV-cache reuse, model routing, per-tenant cost attribution. Built so the CFO can read the dashboard.
Tell us about the problem.
Two paragraphs is enough. We come back within a business day with whether we’re a fit and what we’d look at first.