Most teams can get an AI feature to demo quality in an afternoon. Getting it to production quality — reliable, affordable and safe to put in front of users — is where the real work starts. This report distils what the founders and engineering teams across the Tech Barcelona ecosystem have learned doing exactly that.
It is deliberately practical. Rather than surveying the model landscape, it focuses on the decisions that determine whether an AI feature survives contact with real usage — and the checks that catch problems before your customers do.
A demo proves the model can work. Production proves it works when you are not watching.
Start with the evaluation, not the model
The most common mistake is choosing a model first and worrying about quality later. Flip the order. Before you compare models, build a way to measure whether any of them is good enough for the job:
- A representative test set built from real user inputs, not synthetic examples.
- Clear pass/fail criteria agreed with the people who own the workflow.
- A baseline — often a simple heuristic — so you can prove the model adds value.
- A way to re-run the evaluation automatically whenever the prompt or model changes.
With an evaluation in place, model selection becomes an experiment instead of a guess — and you keep that safety net every time you change a prompt or upgrade a version.
Design for cost and latency
In a demo, a three-second response and a few cents per call are invisible. At scale, both become product decisions. Treat cost and latency as first-class requirements: measure them per feature, set budgets, and make the trade-offs explicit.
Often the answer is not a bigger model but a smaller one used well — caching repeated work, shortening prompts, and reserving the expensive path for the requests that genuinely need it.
Plan for failure
AI features fail differently from ordinary software — quietly, and often plausibly. The teams that ship well assume failure and build for it:
- Wrong but confident answers — design a review step before anything is acted on.
- Latency spikes — set timeouts and a fast fallback path.
- Cost runaway — cap tokens per request and monitor spend per feature.
- Silent quality drift — sample and score live outputs on a schedule.
None of this requires a large team or a research budget. It requires treating an AI feature like any other part of the product: measured, monitored and owned. That is the difference between a feature that demos well and one your users can rely on.