AI Development Services

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

How we approach the work

1. 01

   Define the job and the failure bar

   Start with the user task, the inputs the feature will actually see, and the answers it must never give. That determines whether an LLM, retrieval, an agent, or a simpler rule is the right tool.

2. 02

   Connect real data and permissions

   Work inside the existing product boundary. Retrieval and tools need to respect authentication, tenant scope, and source freshness, with enough traceability to explain an answer.

3. 03

   Measure before rollout

   Build an evaluation set from representative inputs, then check output quality, retrieval, latency, and cost as the system changes. A release decision should come from those results rather than a good demo.

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.

Related engineering notes

• How to add AI to an existing product without a rewrite — Most AI work is not greenfield. A practical guide to bolting AI onto a product you already have: where to start, the integration patterns that are safe, and the failure modes to avoid.
• Shipping RAG to production: what actually matters — A RAG demo takes an afternoon. A RAG system real users trust is a different animal. The things that decide whether retrieval-augmented generation works in production: retrieval quality, evals, grounding, and guardrails.
• Evals for LLM apps: why you cannot ship a reliable AI feature without them — You cannot ship a reliable AI feature on vibes. How to build a small, honest eval set, run offline and online evals, and turn argument into measurement.
• RAG chunking strategies that actually work — Most RAG problems are chunking problems. A deep dive on chunking strategies, when each fits, the size and overlap tradeoffs, and how to measure it.
• MCP explained for product teams: how it differs from plain tool calling, and how to get auth right — MCP (Model Context Protocol) for product teams: how it differs from plain tool calling, the architecture, and a security-forward take on auth.
• Prompt injection in agentic products: what actually needs defending — Prompt injection is not a bug you patch, it is a structural property of how LLMs read context. What direct and indirect injection actually are, why filters fall short, and the layered defenses that limit the damage.