Every week there is a headline claiming AI will replace X. In the field, I more often see oversized expectations + unready data + unmonitored API spend.
I take on LLM integration when the use case is specific and success can be measured. Otherwise, your money and time are better spent tightening the process first.
Three Conditions Where LLMs Make the Most Sense
1. Repetitive text with clear patterns. Summarize long docs to bullets, classify tickets into fixed categories, extract fields from semi-structured text, with gold samples to test accuracy.
2. Internal assistance, not automatic public authority. Operators who want a fast second opinion on a draft, not a bot that signs contracts with no human.
3. You have a product owner willing to kill the feature. Without someone accountable for weekly quality review, models drift quietly.
Signs You Are Not Ready Yet
- No source of truth. The model will polish text that is wrong and still wrong.
- Fully automated high-stakes decisions. Without extra validation, reputational and legal risk rises.
- No budget for monitoring. Logging, quality sampling, and prompt updates are ongoing cost, not a one-time install.
Cost Is Not Just the Provider Invoice
| Category | What people forget |
|---|---|
| Engineering | Integration, retries, observability |
| Operations | Prompt review, model updates, edge cases |
| Risk | Misclassification, data sent to model vendors |
| Vendor | Price changes, model deprecation |
That is why LLM work on my site is listed with a starting from anchor on the services page, not an all-features bundle. Scope follows the use case and the failure modes you can accept.
Architecture Patterns I Prefer (Keep It Simple)
- Shrink context: send only what the model needs, not the whole database.
- Structured output: JSON/schema when possible so the next step is not wild-text parsing.
- Fallback: if the model fails or confidence is low, route to a manual queue or deterministic rules.
- Useful minimal logging: enough to audit and improve, without hoarding sensitive data.
Integration vs “A Chatbot on the Website”
A public homepage bot can be a use case, but it is often not the first ROI. Sometimes internal automation saves more hours than a widget visitors barely use.
I will be direct in discovery: if the issue is site navigation or weak content, an LLM is not the cure.
For operational context before AI: When spreadsheets start hurting operations
If you have one specific use case (not “we want AI”), send it via contact. I will tell you whether model integration makes sense or another phase should come first.