AI & LLM Integration for Existing Products

It means connecting a product you already have to models you do not have to train. Document processing, classification, intelligent chat, and automation triggered by what a model reads — wired into your existing backend, with the plumbing and guardrails that separate a working feature from a convincing demo.

I do not train models from scratch. The interesting engineering in most commercial AI work is not the model; it is everything around it — the prompt pipeline, the retry and fallback behaviour, the cost ceiling, the audit trail, and the decision about which actions a model is allowed to take without a person agreeing first.

One production system, and it is unusually well documented because the process was the point.

A catalogue-enrichment service was built for the operator of a scheduled music-broadcast platform. Roughly 85,000 catalogue songs had no cover artwork and no safe automated way to acquire it. The service was built AI-native — from a 1,675-line specification and zero existing code — on Java 21, Spring Boot 3, the Spotify API, MySQL, Flyway and Docker.

That middle number is the one worth dwelling on. 39% of consequential decisions were made by a human, deliberately — and the result was zero wrong writes to production. The system was not autonomous and was never meant to be. It was fast where speed was safe and gated where it was not.

In work that is high-volume, judgement-light and currently done by a person who resents it. Reading unstructured documents into structured fields. Classifying inbound requests. Matching records across two systems that never agreed on a schema. Drafting text a human then approves.

It pays poorly when the task needs to be right every time and there is no cheap way to check. If a wrong answer is expensive and verification costs as much as doing the work, an LLM is the wrong tool and I will say so before you spend money finding out.

The same way you keep any integration from writing bad data, plus one extra rule. Validate before persisting, make writes idempotent so a retry cannot duplicate, keep a record of what was changed and why, and stage everything reversible.

The extra rule is the permission boundary: decide up front which actions the system may take unattended, which need a person to agree, and which it may never take at all. On the catalogue service that boundary is why 85,000 records could be processed with zero wrong writes.

Whichever fits the task and your constraints — OpenAI, Anthropic and others. Provider choice is usually less consequential than people expect, and worth keeping loose: the integration should sit behind an interface you can repoint when pricing or capability moves, which it will.

Send a brief describing the workflow you want to improve — not the AI feature you think you want. You get a fixed-bid quote within 48 hours, and a straight answer about whether AI is the right instrument at all.

Projects typically start within one to two weeks, as fixed-bid work, a retainer, white-label delivery for agencies, or team augmentation.