Capability, for us, means: a system we have built end-to-end, operated at scale, and iterated on long enough to know where it breaks.
What follows is the public-facing description. The underlying implementations are internal.
Working directly with frontier foundation models — understanding their capabilities, failure modes, and the operating envelope at scale. We select, route, and combine models based on what the application actually needs — not based on brand preference or benchmark scores.
The layer where foundation models become products. Context engineering, retrieval systems, evaluation pipelines, tool orchestration, agentic workflows — the engineering that determines whether an AI feature survives contact with real users.
What runs underneath: inference optimization, cost accounting, observability, deployment patterns, the guardrails and telemetry that turn a prototype into a system your organization can depend on.
Each layer compounds the next. We build all three.