Provider routing, fallbacks, continuous evals. Your product stops depending on a single vendor — and stops breaking when that vendor changes the model.
LLM-native infrastructure for teams that need it to actually work. Multi-model architecture, productive agents, RAG, platform & infra.
Provider routing and fallbacks so your product survives model deprecations, price hikes, and silent regressions.
Guardrails, retries, observability and traces — the unglamorous layer that decides whether your AI feature actually runs at 3am.
Continuous evaluation pipelines so quality is measured, not assumed. We ship when the numbers say so.
Token budgets, caching, and routing decisions tuned to your unit economics — not the model vendor's roadmap.
Provider routing, fallbacks, continuous evals. Your product stops depending on a single vendor — and stops breaking when that vendor changes the model.
Orchestration with tools, retries, observability. They run real tasks — not guided demos. With guardrails that matter in production.
Hybrid search, freshness, evals. Built for the long tail — not just the demo's happy path.
Queues, workers, traces, deployments. The boring layer that decides whether your AI feature actually scales beyond the launch tweet.
We sit with your team, read your code, and separate the AI feature you want to ship from the AI feature your investors retweeted.
Routing, fallbacks, evals, observability. Decisions made now that you won't have to reverse in six months.
Sprints with measurable evals at the end of each one. No demos behind a feature flag forever.
Retainer, embedded engineer, or full handoff with runbooks. Your call — we don't sell lock-in.