Most teams do not need an AI product. They need one or two AI features inside the product they already have — and they need them to ship without destabilising everything around them. That constraint changes how you build.
Start with a narrow, boring use case
The best first LLM feature is one where a wrong answer is cheap and a right answer saves real time: drafting a reply, summarising a thread, classifying an incoming ticket. Avoid anything that writes to the database or moves money on the first iteration.
Treat the model as an unreliable third-party API
It will be slow sometimes, wrong sometimes, and down sometimes. Design for that from day one:
- Wrap every call with a timeout and a deterministic fallback path.
- Validate the output against a schema before it touches your app — never trust free text.
- Log every prompt, response and cost so you can debug and price it later.
Ship evals before you ship features
A small set of example inputs with expected outputs turns "it feels better" into a number. Run them on every prompt change. Without evals you are not engineering, you are guessing — and you will regress silently.
Where we start with clients
We usually spend the first week mapping one high-value workflow, wiring a single guarded LLM call behind a feature flag, and standing up the eval harness. Boring, measurable, reversible — then we expand. If that sounds like the pace you need, get in touch.