We build AI that works on your real data and systems: agents that take action, MCP servers that connect assistants like Claude to your tools, and RAG pipelines that answer from your knowledge. Traced, evaluated and cost-tracked from day one.
Which customers churned last month, and what did they say in support?
14 accounts churned. 9 of them raised slow exports in support, usually in the last two weeks before cancelling.
Six ways we help teams put LLMs to work, each built on the same production-grade stack.
Agents that reason over a task, call your tools and APIs, and finish real work, built with LangChain and LangGraph.
Custom Model Context Protocol servers that expose your systems, data and actions as tools for assistants like Claude.
Retrieval-augmented generation over your documents and records, with embeddings, vector search and answers that cite their sources.
Assistants that answer plain-English questions from your live application data, so teams stop waiting on reports.
Add LLM features to your product on AWS Bedrock, OpenAI, Anthropic or OpenRouter, with structured output your code can trust.
Tracing, evaluation and token cost tracking with LangSmith and Langfuse, so you know what your AI is doing and what it costs.
Have a workflow or a pile of data you want AI to handle? Tell us about it.