Cases/06 · Productisation
ProductNemo was a B2C AI assistant for esports — a statistical and analytical companion for serious players who wanted to understand their game, their opponents, and themselves at a deeper level than the in-game UI exposed.
Challenge
The technical core was tractable: AI/ML reading match data and turning it into useful guidance. The harder problem was reaching serious esports players — an audience with high signal-to-noise standards. The product would only land if the technology, the omni-channel approach, and the user-journey design were good enough to earn attention and keep it.
What was done
The product itself was built: computer vision for in-game detection — reading motion in the player's match in real time and issuing tactical instructions back to the user. A real-time data pipeline was engineered, capturing match data at the volume needed to train a neural network, so the assistant's instruction quality improved with every match it observed. An omni-channel approach to user-journey adoption was built — from first install to embedded-in-routine use — supported by guerilla marketingtactics that earned attention in a community where attention is the scarce resource. A thriving online community grew up around the product itself.
Outcome
Unique downloads in the first six months
Online community of users; 210K visitors; 15% customer conversion rate
Acquisition talks with 100 Thieves, the largest US esports organisation.
Practitioner
Simon — Product, AI/ML implementation, Commercial development.
Where this fits
This case maps to 'Our data could be new revenue. We don't know what to build.'Building a B2C product from AI/ML capability — turning the technology (computer vision, real-time data pipeline, neural-network training) into a coherent commercial offering with an omni-channel approach to user adoption. The engagement was an Implementation — direct delivery of the product, the data pipeline, and the commercial development that brought it to market.