Every application records what happens: logins, errors, abandoned steps, features nobody opens. AI reads the logs and databases of one or more applications, produces a regular report on user behaviour and proposes where to act on flows and experience.
The data on how people use an application is almost always there: server logs, events, database tables. But there is a lot of it, scattered across systems and written for developers, not for decision-makers. So it only gets looked at when something breaks.
Decisions on what to improve end up based on the latest complaint or on whoever speaks loudest. And the problems nobody reports, like a step where half the users stop, stay invisible.
Application logs, events, database tables, from several applications at once. Read-only, with personal data minimised or anonymised before the analysis.
From single events to sessions: where users come in, where they stop, where they go back, which features nobody uses.
Every week or month, written for decision-makers: what changed, where people drop off, which errors weigh most. Every claim links back to the query or the numbers behind it.
Concrete proposals on flows and interface, ranked by estimated impact. They are hypotheses, not verdicts: decided together and measured after release.
These are the numbers we set in discovery, before writing any code. The starting value is measured on your data, and the prototype is judged against it.
Where users stop during sign-up, purchase or the steps that matter, before and after the changes.
How many become a change, and how many of those actually move the number they were meant to move.
From a question about user behaviour to the data that answers it. It used to take someone writing the query.
If the application does not record useful events, or the logs only say the server is up, the right tracking has to be added first. And if there are only a few dozen users, ten interviews teach more than any report.
An idea with AI at its core, a market to validate and no technical team. A real product in a few weeks, with real users, built to be able to change.
Closed tickets, commits and pull requests become the end-of-sprint report and the release notes, in two versions: technical for the team, readable for clients and users.
A free 30-minute call, with no commercial follow-up unless you want it.