Organisations have built up real data, AI, or operational capability — often as a by-product of years of running the core business — and they sense it could become a sellable product or service. Customers ask if they can buy it. Adjacent markets look reachable. But the path from internal capability to commercial offering isn't obvious. The technology is real; the product, the buyer, the pricing, the distribution, and the support model still need to be designed.
We've got something interesting in our data, and customers keep asking if we'd sell it.
Our AI works internally. Could it be a product?
We could build something here, but I don't know what we'd be selling, or to whom.
Everyone's telling us to productise our data. What does that even mean?
Productised data science into a B2B SaaS for daily fantasy sports; outperformed FanDuel and DraftKings on the analytical core.
Productised computer vision and real-time machine learning into a commercial offering for autonomous wind turbine maintenance.
Productised AI/ML into a B2C esports product with computer vision for in-game motion detection.
This is the constructive inverse of structural archaeology — going to the problem the offering would solve before committing engineering. Engagements start with a Depth Check to pressure-test the commercial case, often move into an Analysis that defines the customer persona, the pricing model, and the route-to-market, and then into Implementationthat builds the product alongside the commercial structure that will sell it.
Start here
“If customers keep asking, that's a signal — not yet a product. The question is whether the signal is strong enough to act on, and what to build first.”
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