Medidesk driver patient-klinik-kommunikation för 1000+ vårdenheter, använder redan AI inuti produkten. De ville ha AI inuti företaget också. Vi tog in extern data och signaler. Säljteamet lägger nu tid på ICP-anpassade kunder redo att prata, inte manuell prospektering.
more time selling to ICP
AI absorbed the repeatable pre-sales work
from kickoff to a working first version
prototypes in hand, not decks
vendors pitched, one got the work
picked on behaviour, not promises
Daniel Nowocin ran an open call for an AI partner. Thirty vendors pitched, three made the shortlist. What decided it was not the strongest deck but how the conversations went: responsive, concrete, small steps forward. Within days of starting, he had a clickable prototype in his hands.
If you are choosing an AI partner right now, his filter is worth copying. A deck can promise anything, so Daniel judged behaviour instead: how fast the answers came, how a vendor handled a change of direction, whether the conversation showed people who build or people who sell. Experience and references counted, but behaviour decided.
Medidesk already runs an AI-first product, patient-clinic communication for more than a thousand medical facilities. The decision at the start of the year went further: bring AI into the running of the company itself, with every step proven as a small POC before committing to a full build.
The first system landed in sales. Before a first call, the brief is already there, built from outside data and signals: how big the clinic network is, who runs it, what they use for patient contact today, and which signals say they are ready to buy. Two minutes of reading, then the call. Selling time goes to those conversations, not to manual prospecting.
None of it started with a model. All of it started with ordered data.
That is Daniel’s own rule, and ours. Before any build, we defined the scope of input and output data together and got it structured, so the AI reasons over the company’s reality instead of guessing at it. It is the unglamorous step most AI projects skip, and the reason these systems fit how the business actually works.
From there the pattern repeats: a small stage, shipped in days, verified on the market, then extended. Each working system opened the way for the next.
“For me it matters that the partner, beyond the technical knowledge, feels the business, feels the logic of building a business. That join of technology and business understanding is the key for me.”
Daniel drew the line himself: technology takes what needs automation, people keep what needs a person, and the team puts its time where it earns the most.
In Daniel’s words: what used to be done by a person is now done by AI that supports the whole process. People focus on quality and on the contact that has to be human. The technology takes the rest.
Daniel’s advice to another founder is the playbook, and it has less to do with buying AI than with how you build.
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