The SMB guide to putting AI in production (not just demos).

Every business has now seen the demo: a chatbot answers a question impressively, everyone nods, and three months later nothing is in production. The gap between demo and deployment is not model quality — it is engineering that demos skip.

Why pilots stall

Three reasons dominate. First, hallucination without grounding: a model answering from general knowledge will eventually invent your prices, your policies, or your legal obligations. Second, no evaluation: without a test set of real questions and expected answers, nobody can say whether the system is safe to expose to customers, so it never ships. Third, no integration: an assistant that cannot see live order status or write to the CRM is a toy, and users abandon toys.

The production checklist

What separates deployed systems: retrieval over your own documents and data (RAG) so answers are grounded and citable; an evaluation suite that runs on every change; guardrails that keep the system inside its lane — no medical advice, no pricing commitments, clean escalation to humans; monitoring that tracks accuracy and cost per conversation weekly; and honest scope: launch on the 40 questions that make up 60% of volume, not on everything.

What it costs and returns

A grounded, evaluated assistant is typically a 6-12 week build, not a year. The running cost is the part teams overestimate: with model routing and caching, customer-support assistants usually run far cheaper than the staff time they replace — the economics are visible within a quarter in first-response time and tickets resolved without a human.

Our rule for clients: if you cannot write down the 40 questions, the data sources, and the escalation rule, you are not ready to build — and a good vendor should tell you that. When you can, the build is honest engineering, not magic. That is the standard we hold on our AI practice, and you can try the result live — the assistant on this site runs exactly this architecture.