AI Implementations
We build AI features that ship, and the infrastructure to run them. We don’t sell strategy decks; we deliver working systems.
What we build
AI features in client applications
LLM-powered features inside your existing products: RAG over your knowledge base, document understanding and summarization, intelligent search, drafting and editing assistants, content generation, agent-based automation. We integrate with the tools you already use and ship to production.
Workflow and process automation
Replacing manual work with LLM-driven automation: connecting models to Slack, ticketing, CRMs, and internal tools; processing documents and unstructured data at scale; building agents that handle real business workflows end-to-end.
AI infrastructure and MLOps
The infrastructure side of running AI in production: self-hosted model deployment, GPU orchestration, vector databases, model serving, evaluation and observability, prompt management, cost controls. The same operational discipline we bring to cloud infrastructure, applied to AI workloads.
How we work
We pick the right tool for the job: proprietary frontier models when they make sense, open-source models when they’re a better fit, and the connective tissue that makes either approach reliable. We measure what matters and ship iteratively.