AI engineered into real products, not demos

What we actually do

The distance between an impressive demo and a dependable product is where most AI projects die. We close that distance: production-grade retrieval, evaluation criteria tied to your acceptance standards, and observability so you know what the system did and why.

• LLM application and agent development - tool use, multi-step workflows, and human-in-the-loop controls where the stakes demand it
• RAG systems - chunking, embedding, and retrieval tuned on your corpus with measured answer quality, not vibes
• Applied ML - forecasting, classification, and anomaly detection models built against your business metrics
• Evaluation harnesses - domain-specific pass/fail criteria wired into CI so regressions are caught before users see them
• LLM ops - cost and latency budgets, fallback chains, prompt versioning, and drift monitoring in production

Where this earns its keep

Internal copilots that actually get used, customer-facing assistants that don't hallucinate your pricing, and decision-support ML where a wrong answer costs real money. If a generic chatbot wrapper would do, you don't need us - when it wouldn't, you do.

How an engagement runs

We start by writing down the success metric and the single most expensive assumption, then test that assumption first. Fixed-price plan, senior-only execution, and everything - weights, prompts, pipelines - handed over clean under your IP.