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The clear winner: product content at scale
If you have 30,000 SKUs with a spec table and no prose, an LLM generating first-draft descriptions from structured attributes is an immediate, measurable win. The key word is draft — a human still approves, and the model never invents a technical value.
The quiet winner: support triage
Not a chatbot answering customers — a classifier reading incoming emails and routing them, with the order already looked up and attached. It removes a genuinely boring hour from someone's day and fails safely: a misrouted ticket costs a minute.
Where RAG earns its keep
Retrieval-augmented generation over your own datasheets, manuals and past quotes lets sales answer a technical question in seconds instead of an afternoon. The retrieval quality — not the model — decides whether this works.
What we would not build yet
An autonomous agent that changes prices or places orders. The failure mode is not an embarrassing sentence — it is a wrong invoice sent to 200 customers. Keep AI on the draft side of anything that touches money.
- Drafts, not decisions — especially near money.
- Product content and support triage pay back fastest.
- In RAG, retrieval quality beats model choice.
We do this for a living — Shopware, Node.js, React, ERP integration and automation for B2B.
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