the First to push for experimenting Agents
Since 2022, Neoinstinct develops framework sto deploy agent in live business processes.
We believe that experimentation is the key to enabling artificial intelligence to make profitable decisions in complex and partially known environments.
In other words: if you want AI to yields profit, let AI discover how to make profit !
the First to scrap the entire web for thousands of RL libraries
We are actively scraping the whole web for 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).
the First to autonomize 31% of quantitative business tasks and to have LLM's market share without LLM (3 trillions $)
We have estimated that 31% of human tasks are quantitative decision tasks that might be formulated under the RL framework.
That means a market size of 3 trillions $, and not even counting already automated tasks we will be able to improve.
We build the LDM for this enormous market
the First to have operational meta RL models in business process
Meta-RL is not a promise, it is the production heartbeat of our pipelines.
The LDM rewrites its own learning rules as soon as conditions flip, so strategies evolve at market speed, not human-retrain cadence.
In short: autonomous Business decisions with built-in evolution
the First to build general-purpose Agents to optimize business
Our foundation model is a universal quantitative decision making problem solver, and it’s optimized for this.
It’s not learning from text from the Internet, it’s learning by interacting with thousands of quantitative processes in parallel, with the goal of optimizing each process’ KPI.
Embrace the LDM revolution: less talk, more profit.