Ideas on building production AI.
Essays and field notes from the team that ships it — on AI agents, retrieval, and the unglamorous engineering that separates a system you can trust from a demo that dazzles once. No hype, no fluff — just what actually works in production.
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Ask anything about building production AI — you'll get a straight answer drawn from our essays, then can go deeper with Aria, our AI assistant.
Field notes on shipping AI.
Practical, opinionated writing on the problems we solve every week — building agents you can trust, grounding models in your own data, and getting from prototype to production without the wheels coming off.
What separates a production AI agent from a demo
Guardrails in code, role-based access, risk-gating, replayable audit trails, evals as a release gate, and designing for graceful failure — the difference between a toy and a system.
What is an AI digital employee (and what does it cost)?
Not a chatbot — a role-scoped agent that owns a job end to end. What a digital employee actually is, where it fits on your team, and what it realistically costs to build.
How much does it cost to build an AI agent in 2026?
An honest breakdown of what drives the price of a custom AI agent — scope, integrations, guardrails and run-cost — with realistic 2026 ranges you can plan around.
RAG done right: grounding AI in your own data
Why most "hallucination" is really bad retrieval — and how chunking, hybrid search, reranking, citations and permission-aware retrieval ground a model in your data without leaking it.
From POC to production: shipping AI that lasts
The canyon between "it's possible" and "it's still running a year later" — observability, cost control, iteration on the loop, and owning the prompts and IP that actually matter.
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