Guide
AI integration for existing software products
AI integration is the work of adding capabilities like chat, search, summarization, or document understanding to an application you already run, grounded in your own data so the answers are real. This guide covers where it pays off, how to ground it, and what production actually demands.
Where AI actually moves a metric
The fastest way to waste an AI budget is to add a feature because it looks impressive rather than because it changes a number. Start from the metric: a support team drowning in tickets, a search box no one can find anything with, a document pile no one reads. AI earns its place when it shortens one of those loops measurably. Everything in this guide assumes you have picked a target worth hitting.
Grounding: why retrieval beats a bare model
A bare language model answers from its training, which means it will confidently invent a price, a policy, or a fact that was never true. Retrieval-augmented generation (RAG) fixes this by fetching the relevant passages from your own data first and asking the model to answer from them. The model becomes a reader of your sources rather than a guesser, and it can cite where an answer came from. For most integrations into an existing product, grounding through retrieval is the difference between a demo and something you can ship.
RAG vs fine-tuning
Teams often reach for fine-tuning when they mean grounding. Fine-tuning changes how a model writes and reasons; it is the right tool when you need a particular style, format, or a narrow task done reliably. It does not teach the model your latest data, and retraining every time the data changes is slow and expensive. Retrieval teaches the model your data at answer time. Most products want retrieval first and fine-tuning only for the parts where tone or format genuinely matter.
Evaluation and the production tax
An AI feature without evaluation is a vibe, not a feature. Build a small suite of real questions with known-good answers so quality is a number you can watch, and a regression is something you catch before users do. Then pay the production tax: cost ceilings, latency budgets, rate limits, logging, and a graceful path when a model is slow or down. These unglamorous parts are what separate a feature that survives contact with real traffic from one that quietly breaks.