We believe that experimentation will enable AI to make profitable decisions in complex and partially known environments.
In other words: if you want AI to yield profit, let AI discover how to make profit!
We are actively scraping the whole web for Reinforcement Learning (RL) simulation environments across a huge variety of application domains.
Our goal? A universal RL algorithm able to adapt to any decision process: the Large Decision Model (LDM).
We have estimated that 31% of business processes are quantitative and can be formulated under the RL framework.
That implies a market size of at least 3 trillions $, comparable with the estimated LLM market.
We build the LDM to disrupt this market
Name your KPI, our model then takes the decisions that maximize it.
It is not learning from internet text, it is learning from interactions with thousands of RL libraries.
Embrace the Large Decision Model: less talk, more profit.
Close the gap between your data and your decisions: put an end to hundreds of charts and dashboards that need to be interpreted.
RL-based actionable insights that directly shows the long-term impact of decisions on your KPI:
• you visualize the best decision in every situation.
• you audit past decisions at a glance.
Bioreactor optimization:
Optimize bioreactor control (temperature, pH, oxygen, agitation, feeding) to maximize protein yield
Product ordering:
Optimize daily supermarket orders to improve sales and reduce waste
Research
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