We Embed AI Into Real Products. Not Demos. Not Pilots.

The demo trap

Anyone can get an LLM to do something impressive in a notebook. Paste in a prompt, call an API, screenshot the result, ship it to the slide deck. That is a demo. A demo is not a product.

The gap between the two is where almost every AI integration we see has failed. The models are fine. What is missing is the system around the model. Retrieval, routing, fallback, evaluation, observability. The unglamorous part. The part that decides whether the feature still works in November.

WTM has shipped production AI features across fintech, healthcare, e-commerce and SaaS. A few lessons keep repeating.

What makes production AI different

Evals before anything else

In normal software, a function either returns the right value or it does not. In an LLM system, the output sits on a spectrum from "great" to "subtly wrong." Subtly wrong is the one that hurts you, because nobody notices until a user escalates.

So we build evals first. Not last. Before we pick a model, before we tune a prompt, before we ship anything.

An eval is a structured test set with expected outcomes, scored either programmatically or by a judge model. When it passes, we know the feature is shippable. When we swap a model six months later and the eval regresses, we catch it the same day. Without evals, you are not shipping AI. You are gambling.

Observability from day one

Models drift. Prompts that worked in January produce different outputs in June. Users surface edge cases nobody anticipated in scoping.

We instrument every LLM call on the first commit. Token usage, latency, cost per call, output-length anomalies, refusal rates, sampled quality checks. That data is what tells you the feature is degrading before your customers do. It is also what tells you which prompt changes are actually worth shipping, not just which ones felt better in a demo.

Failure handling before the happy path

LLMs fail. They time out. They hallucinate. They get rate-limited. They refuse requests that should have been fine. Every single one of those failure modes will happen on your busiest day.

So we build the failure path first. A fallback rule. A cached response. A graceful error that routes the user somewhere useful instead of a red toast. The happy path is easy. The system that degrades well is the product.

How we actually work

When a client brings us an AI use case, the first question is never "which model." The first question is "what does good look like, and how will we know we have it?"

We define the eval criteria before we write a prompt. We instrument before we deploy. We treat the LLM as one component in a system, not the system itself.

That discipline is why our AI features are still running six months after launch. Not six minutes after the demo.