AI Development Services
We build AI into products that already have real users and real constraints, not into demos. That means LLM features, AI agent systems, retrieval-augmented generation (RAG) over your own data, and Model Context Protocol (MCP) integrations, designed to hold up under production load, cost limits, and review discipline, not just work once in a notebook.
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What's included
- LLM feature development: chat, generation, extraction, and classification wired into your product
- AI agent systems: single-agent and multi-agent orchestration, tool use, and guardrails
- Retrieval-augmented generation (RAG): chunking, embeddings, retrieval, and evaluation, tuned to your data
- Model Context Protocol (MCP): tool and data integrations for agentic workflows
- Model selection and cost tuning across OpenAI, Anthropic, Google, and open-weight models
- Evals: a test harness so an AI feature can be trusted to ship, not just demoed
Who this is for
- Products that need an AI feature that works on real user data, not a scripted demo
- Teams that have run a pilot and need help getting it to production
- Companies evaluating build vs. buy for an AI capability
Related work
- Kayak-style clinical search for prescription options — A clinical search platform that uses AI agents and RAG to recommend and compare prescription options against ICD-10 code indications.
- A production AI agent platform, zero to live in a month — A custom, production-grade AI agent platform built from scratch and shipped to production in one month — after a previous team spent 8+ months and shipped nothing.
Want to talk through your project?
Send a couple of sentences about what you're building. We'll reply with how we'd approach it.
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