We build AI features into real products — from proof of concept to deployment — starting with whether AI is genuinely the right tool for your problem.
- The prototype impressed everyone and then nobody could make it reliable enough to ship.
- You're being sold AI for a problem that a database query would solve.
- Nobody defined what "working" means, so the project can't be finished.
01.
Whether AI solves this, and what it would cost. Sometimes the answer is that it doesn’t.
02.
A working demonstration on your actual data.
03.
How the AI feature behaves, including when it’s wrong.
04.
Built into your product, monitored, with fallbacks.
05.
Getting your data where the model needs it.
06.
Measuring accuracy over time.
Process
Four stages, ending in a document that says yes or no — with the reasoning and the data behind it. Most research projects run [3–5 weeks], and you'll know by the end whether this product has a market worth building for.
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01
Assess
Define the task, the success criteria and the failure cost.
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02
Prototype
Proof of concept on your data, fast and cheap.
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03
Design
The UX around the AI — how users correct it, trust it, and work with it.
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04
Deploy
Into production, with evaluation and monitoring in place.
Smart Delivery, Smart Delivery PLUS, White Label Delivery App, Smart HR, Wine List App, Smart Catalog, Smart Restaurant — our own platforms, in production, maintained by the same team that would build yours.
Companies with a repetitive, data-heavy process that’s eating staff hours.
Products that need an AI feature to stay competitive.
Teams with an AI prototype stuck short of production.
