Fast Web LabsWhat we test in hoursbefore selling it in weeks.

What we were testing

If a mechanic is simple enough and lands in time, is somebody else’s trend enough to carry it?

What happened

Zero to production in an afternoon: domain, database, voting, battles with their own URL and a finished look. No viral traction.

What we learned

The speed is real and demonstrable. Somebody else’s trend is not distribution: with nobody to show it on day one, a good mechanic never starts.

What we were testing

Can a catalogue business handle its enquiries without the agent inventing products or prices?

What happened

In testing it resolved 85% of enquiries with sub-3-second answers, escalating to a human when unsure. Never reached production.

What we learned

Retrieval over the real catalogue is what separates a useful assistant from one that makes things up. The bottleneck was not technical: the project lost its owner.

What we were testing

Can paper be removed from a field operation without asking the technician to learn anything new?

What happened

Working end to end: ID login, location lookup, signature and media capture, and a PDF generated and archived on its own. Never reached production.

What we learned

Adoption is not decided by the app: it is decided by how many taps it takes one-handed, wearing gloves.

What we were testing

Can a small business stop retyping data from documents without buying an ERP or changing how it works?

What happened

In testing, extraction under 5 minutes per batch and 98% accuracy on the defined fields. Never reached production.

What we learned

The value is not in extracting: it is in going back and pulling a new field out of everything already processed without redoing the work.

What we were testing

Is giving away an automated audit enough for a business that does not know us to hand over its details and start a conversation?

What happened

It sat on the home page’s primary button for eight months. Nobody used it: not one report requested from cold traffic.

What we learned

We were asking before proving. Nobody tells you their biggest revenue problem ten seconds after landing on a site they do not know. And it was the one piece of the site that looked like every other AI agency. The same mechanic in reverse — a sum with your own number, no sign-up, nothing given in exchange — is what now lives at /tu-canal.

What we were testing

Can the idle capacity of a health system in crisis (beds, appointments, maternity wards) become revenue without building anything new?

What happened

The prototype is complete: an occupancy map, four flows, screens for members, centers, treasury and leadership, and a revenue-split calculator. We showed it to people in the sector in Argentina. It did not move forward: nobody could pay for it, and no insurer wants to send its members to another.

What we learned

The blocker was not technical, it was about power: referring a member means losing them. The version that fits is the reverse: not “share your patients” but “fill your empty capacity”.