Most teams considering AI integration assume it requires a rebuild. It usually doesn’t. The fastest, lowest-risk way to ship AI in an existing product is to treat the LLM as another service your application talks to — not as a replacement for your stack.
Here’s a pattern we’ve used repeatedly to ship AI features into mature codebases without touching the architecture.
1. Start with one workflow, not “AI”
“Add AI to the product” is not a project. “Suggest a reply when a support agent opens a ticket” is. The first version should be small enough that you can ship it in two weeks and measure it.
Good first workflows usually share three properties:
- The input is text or a small structured payload. No vision, no audio, no PDF parsing in the first iteration.
- The output is a draft, not a decision. A human reviews and edits it. This dramatically lowers the accuracy bar.
- You already have ground-truth examples. Past tickets, past contracts, past replies — anything you can use to evaluate.
2. Add a single thin service in front of your model provider
Don’t sprinkle fetch() calls to OpenAI or Anthropic across your codebase. Put one service — a function, a small package, or a microservice — between your application and the model provider. Everything else in your codebase calls that.
That service handles four things:
- Building the prompt from your application’s structured data;
- Selecting the model and parameters;
- Logging inputs, outputs, latency, and cost;
- Stripping or hashing personally identifiable information before it leaves your network.
Once that service exists, swapping providers, adding caching, or routing easy queries to a cheaper model becomes a single-file change.
3. Treat the prompt as a config file
Prompts change far more often than code. Move them out of your source files and into a versioned config (database, JSON file, or a prompt management tool). That way your prompt engineer — who may not be a developer — can iterate without opening a pull request.
4. Ship behind a feature flag, then measure
Even for a “draft” output, you want to compare it to the status quo. Roll out the feature to 10% of users behind a flag, log how often the AI suggestion is accepted, edited, or discarded, and compare the time-to-completion against the control group.
This is the moment most teams underinvest. AI features look magical in a demo and reveal their flaws only at scale. A good evaluation harness will save you from a lot of post-launch surprises.
5. Keep the human in the loop until the data tells you to take them out
“Human-in-the-loop” sounds like a compromise, but it is your safest position. It lets you ship faster, lowers your liability, and gives you free training data: every time a user accepts or edits a suggestion, you’ve labelled an example. Use that data to build evaluations, fine-tune later, or simply tune the prompt.
The architectural payoff
None of this requires rewriting your application. You’re adding a service, a feature flag, and a logging table. Your existing authentication, billing, permissions, and UI all stay the same. The “AI feature” is a thin layer on top of a system that already works — which is exactly how it should be when you ship version one.
If you’re trying to figure out where AI fits into your existing product, get in touch — we help enterprises and startups do exactly this.