
Protolyst
Atomise Your Knowledge Base
After 4 months of a significant tech stack migration to ReactJS and Firestore, the app was more powerful than ever and ready for the public to see, test and enjoy. We're staying lean and constantly iterating to try to carve our niche in the market.
Protolyst was officially live!
At this point we had onboarded 500 users on to the platform without releasing the app to the public.
We amassed this number through consistent user trials via our existing business stream, scouting forums and asking for Onboarding calls, and helping friends, colleagues and family assess their business ideas using the app.
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We started having conversations with universities around the U.K. Eventually we found an incubator from Maynooth University looking for a system to help asses the businesses they work with.
After weeks of developing the relationship, we had won our first Enterprise Contract!
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One day when working with an academic to assess her business idea, she asked how much it would cost to keep access to the platform. We didn't have any payment infrastructure set up so we pulled £60 / year from thin air. She still uses the platform today!
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We started with a vanilla Javascript implementation tacked on to our existing Squarespace website. The result: a success.
When clients started asking for access to it, I developed it into a standalone version that became Protolyst.
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Our team had spent the past 3 years working together to support Deep Tech start-ups translate their academic discoveries into start-up companies.
We had become experts in quantifying abstract ideas and finding market application for them, but the system of Google docs and spreadsheets we used to organise our research was becoming cumbersome.
We asked ourselves if we could build an in-house system to enable us to parallelise the management of market research and insight development across multiple businesses.
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About
The traditional approach to managing information is to process it into documents. This is effective for consolidating information, but individual learnings are hard to extract or recall to prime further synthesis.

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