AI Engineering
We design and build AI-powered applications, from LLM-based features and retrieval-augmented generation to agents and Model Context Protocol (MCP) integrations, and we help engineering teams adopt AI-augmented development with confidence.
What we build
Our AI engineering work covers the systems that make AI useful in production: LLM-powered features inside existing applications, retrieval-augmented generation over your own documents and data, single and multi-agent systems, MCP servers and integrations that connect AI assistants to your internal tools, and the evaluation, guardrails, and observability that separate a demo from something you can ship. Where a problem calls for classic machine learning rather than a language model, we build that too.
AI adoption for engineering teams
Beyond building AI systems, we help teams change how they build software. We guide organizations rolling out agentic coding tools such as GitHub Copilot, Claude Code, and Cursor: establishing workflows, review discipline, project context, and governance so the productivity gains arrive without the quality regressions. This is where our practice began, and it remains the heart of it.
How we engage
We deliver AI features and applications end to end, embed AI engineers alongside your developers, and review architectures and AI adoption plans already in motion. As with everything we do, we are teachers as well as practitioners, so every engagement transfers the thinking along with the code.
Related training
Teams building these skills in-house can draw on our AI Application Engineering courses, the AI coding assistant courses, and the AI Academy, our multi-week program for AI-augmented software engineering, all of which pair well with mentoring.
Contact us to talk about your AI project or adoption plans, or see our other services.