Applicable machine learning, for any manufacturing plant.
Serious industrial AI already exists. It has always been heavy artillery — multi-year roadmaps, large budgets, deep integrations. The majority of plants are locked out below that cost floor, even though they produce the majority of the revenue. Jemba exists to close that gap.
The bottom 99% of plants produce 60% of the revenue.
And they cannot justify a six-figure deployment with an analytics team attached. This is not an SMB problem: big-name manufacturers run a galaxy of smaller plants, each too small to fund that alone, and very significant in aggregate.
Vertical specialists build first-principles models and digital twins for specific large processes. Excellent where they fit — and the approach does not amortise across plants.
Mega platforms sell unified suites and custom builds to any enterprise willing to fund them. They win where the budget exists, which means the top of the market.
Both chase the same 1% of plants. Industrial AI custom-built for one process or one enterprise cannot reach down. That structural fact is the premise of everything we build.
Incubated inside an industrial company, not a lab.
The technology was developed on proprietary manufacturing data and real production use cases, alongside ten years of domain expertise in the plants themselves. That is why it assumes a rough export rather than a clean warehouse.
A standalone company
Jemba owns its technology, its IP and its clients outright. Nothing is licensed back from anyone, nothing is shared, and the team is dedicated and full time.
Why it had to be standalone
An established monitoring business and an R&D-heavy venture have different risk profiles, different capital needs and different horizons. Funding both from one budget shortchanges both.
The data side is ready. That was not true ten years ago.
The instrumenting is done
Factories have spent more than a decade installing historians, PLCs, sensors and quality systems. The data exists and dies in storage. There is nothing left to deploy in the plant before value can be extracted.
The technology matured
The mathematics is not new. What is new is being able to productise the full data-science workflow so a plant manager operates it without hiring anyone. Five years ago that was a consulting engagement.
The attention is elsewhere
AI capital is overwhelmingly aimed at office productivity. Manufacturing efficiency is a signal problem, and it is not being built at the same pace.
Name one line and one target variable.
Maxime Bunel · contact@jemba.ai
Paris, France
