What it covers
Building AI features that survive contact with real users and real data. We work inside your codebase and your cloud, with your engineers, and we leave behind code your team can maintain.
- LLM-powered product features: summarization, extraction, drafting, classification, search
- Agent workflows that take actions in your systems, with checkpoints and audit trails
- Retrieval over your own data (RAG), including chunking, indexing, and freshness strategies
- Integration with existing APIs, databases, queues, and internal tools
- Evaluation harnesses so you know whether a change made things better or worse
- Monitoring, tracing, rate limiting, and cost controls in production
How we build
Thin slice first, against real data, in front of real users. Then harden: evals, observability, failure handling, and the runbook your on-call will thank you for. We prefer boring, well-supported infrastructure and we're comfortable with whatever model providers you're already using.
What you get
Working software in your repository, an evaluation suite, deployment and monitoring in your environment, and documentation written for the engineers who inherit it.
Common questions
Which models and frameworks do you use?
Whatever fits. We've shipped on the major hosted providers and on open-weight models, with and without orchestration frameworks. We'll recommend the simplest thing that works and explain why.
Can you work with our existing engineering team?
That's the default. We pair with your engineers throughout so the knowledge stays with you.