Movida: +40% in bookings

The challenge

In car rental and mobility, the booking flow is where revenue is won or lost. Every extra step and every moment of hesitation at the point of decision leaks conversions that marketing already paid to acquire. The opportunity was not more traffic. It was the decision happening inside the flow.

What Studio Labs built

We mapped the exact points in the booking flow where users hesitated or dropped, then shipped a production AI agent that reads behavior and acts at that decision point. It runs under the same SLA, auth, rate limits, and guardrails as the core product, with continuous evaluation on real traffic.

Not a demo and not a pilot left in a drawer. An agent live in production, owned by the team that designed it.

The result

Bookings up 40%, with the agent running in production inside the flow where the decision is actually made. The number showed up where it counts: the funnel.

How it runs in production

The part that decides whether this holds is not the model, it is everything around it. The work runs inside the stack Movida already operates, not beside it as a separate system, because a decision that lives in a parallel tool is a decision nobody acts on.

That means auth and row level security so the system only ever sees what the requesting user is allowed to see, rate limits and audit trails so every action is attributable, and continuous evaluation against real traffic rather than a fixed test set. Observability is wired in from the first day, because a system you cannot inspect on a specific request is a system you cannot debug at two in the morning.

What the team owns

The code belongs to the client from day one, in their repositories. We are not a dependency you rent indefinitely, and the engagement is not designed to make you need us next quarter.

We embed with the internal team while the work is built, so the people who will run it afterwards are the same people who watched it get made. We stay until it holds in production and the team can move it forward without us, which is a harder standard than shipping and walking away.

Where this pattern applies

The shape generalizes beyond mobility and car rental. Wherever a decision is made repeatedly, at volume, with a real consequence you can read in a number, the same approach applies: find the decision worth automating, build the system that runs it, wire it into the flow where renters actually are, and hold it to a measurable standard.

What does not generalize is skipping the diagnosis. The booking flow was the right place to work here because that is where the decision lived. Somewhere else, it will be a different step entirely, and starting from the technology instead of the decision is the most common way this work fails. That is the reasoning behind how we build production AI, end to end, and why we wrote about how to prioritize AI use cases.