"Add AI to our product" is a request we hear constantly, and it usually turns out to mean something specific once we dig in: summarize this data, answer questions about this document, draft this type of message, or classify this type of record. Good LLM integration starts by naming that specific job, not by bolting on a generic chat box.
The basic architecture
Most integrations follow the same shape: your application sends context (the relevant data, not your whole database) to a language model through an API, the model returns a structured response, and your application validates and acts on it. The skill is in what context you send, how you constrain the output, and what happens when the model gets it wrong.
Decisions that actually matter
Which model, and why. Bigger and more expensive isn't automatically better for a narrow, repeated task — often a smaller, faster model tuned with good prompting and examples outperforms a general-purpose flagship model on cost and latency for the same job.
Where your data lives. For anything touching customer or business data, you need to know whether it's used for training, how long it's retained, and what your contractual protections are. This is a five-minute conversation that saves a compliance headache later.
What happens on a wrong answer. Every LLM integration needs a plan for when the model is confidently wrong: validation rules, confidence thresholds, and a human-review path for anything above a risk threshold you define.
Where we usually plug in
Support inboxes (drafting first-pass replies for a human to approve), internal search over documents and knowledge bases, and structured extraction from unstructured input like emails, PDFs, and call transcripts. These are narrow enough to get right and valuable enough to matter.