What is AI integration (and what it isn't)

Most "AI" demos look magical, then fall apart the moment they touch your real data. The gap between a slick demo and something you can run a business on is exactly where AI integration lives.

What AI integration actually is

It's connecting an AI layer to the tools you already use — your CRM, your database, email, Slack — so it can read context, make a decision, and write the result back. The model is one part. The integration, the data access and the guardrails are the rest.

Where it earns its place

• Repetitive, language-shaped work: triaging tickets, drafting replies, summarizing, classifying, extracting fields from messy input.
• Glue between tools that don't talk: read from one system, decide, write to another — with a human approving the parts that matter.
• Search over your own knowledge: answers grounded in your docs and history (RAG), not the open internet.

Where it doesn't

If a rule or a simple script does the job reliably, use that. AI is worth it when the input is fuzzy and the judgment is repetitive — not as a more expensive way to do something deterministic.

How I build it

Grounded in your data, gated with validation and approval steps, logged so you can see every decision, and shipped in a small working version first — then hardened. You work directly with the engineer who builds it.

Have a process that might fit? Tell me about it.