From prototype to production product.
- Solution
- Scale
- Effort
- 1.5 FTEon average
- Together for
- 2 yearssince 2024
- Status
- Active
01 · The client
A company bringing AI into the enterprise, with a product that answers questions by drawing on a client's internal documents. A small team, deep domain expertise, and development outsourced.
- Type
- AI software company
- Region
- Italy
- People
- Under 20
- Stage
- Growing
02 · The trigger
They had a prototype that proved the idea: it ingested documents, answered questions, and won people over in a meeting.
Then came the moment to sell it to someone who would use it every day, and the gap between the two became the problem.
03 · The technical problem
A prototype like this works because whoever demos it knows which questions to ask and which documents to load. In production, badly formatted documents arrive, questions come in unexpected shapes, and volumes are unknown in advance. The document-processing pipeline has to become reliable and traceable: when an answer is wrong, you need to be able to trace back what produced it.
04 · What we did
We kept what worked in the prototype and rebuilt everything around it: document handling, user access, infrastructure. The load-bearing decision was separating the experimental side from the part serving customers — so the product's evolution wouldn't put existing users at risk. And infrastructure that scales itself, because nobody knew how many users would show up.
05 · The partnership
- 2024
From prototype to product
Documents, access and infrastructure rebuilt.
- 2024
Experiments kept apart
Evolving the product never touches the people using it.
- 2025
In the hands of end users
Infrastructure that sizes itself.
- Today
Ongoing partnership
Before
After
- Only what is ready moves into the live product
06 · Where things stand now
The product is in end users' hands and keeps growing. The relationship is active, and it came through a referral from another client.
Lane: Scale
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