Adaptive ML
French AI firm
From Wikipedia, the free encyclopedia
Adaptive ML (often styled as Adaptive) is a private software company based in New York, United States and Paris, France.[2] The company focuses on reinforcement learning (“RLOps”),[2] providing tools that allow organizations to customize and operate open-source large language models for specific applications.[3]
| Company type | Private company |
|---|---|
| Industry | Artificial intelligence, enterprise software |
| Founded | 2023 |
| Founders | Julien Launay, Baptiste Pannier, Daniel Hesslow |
| Headquarters | New York, United States; Paris, France |
Key people | Julien Launay (CEO), Baptiste Pannier (CTO) |
| Products | Adaptive Engine |
| Services | Tools for deploying open-source LLMs |
Number of employees | 32 (2025)[1] |
| Website | www |
History
Adaptive ML was founded in 2023 by Julien Launay, Baptiste Pannier, and Daniel Hesslow, who had previously contributed to open-source language model projects such as Falcon and BLOOM, and held research and engineering roles at Hugging Face.[2][4][5]
In March 2024, Adaptive ML raised a seed round of US $20 million led by Index Ventures,[6] with participation from ICONIQ Capital, Motier Ventures, Databricks Ventures, and other investors. Sifted reported an implied valuation of $100m in this round.[4][7]
Adaptive’s product team is based in Paris, France, while its corporate, financial, and sales and marketing functions are in New York.[8]
Technology and products
Adaptive ML develops a software platform called Adaptive Engine, used to fine-tune and operate open-source large language models.[9]
The platform enables reinforcement-learning–based post-training and model-evaluation processes intended for data science teams. Reported areas of application include database query generation, automated customer-service workflows, and systems for retrieving internal information.[10]
Business model and vision
Adaptive ML operates on a B2B model: the platform is targeted at enterprises that wish to deploy large language models with a high degree of customization, privacy, and control, rather than using third-party public LLM APIs.[11][12]
The company’s mission emphasizes long-term adaptability and continuous learning: as users interact with a model, their behavior and feedback become data for ongoing model tuning and improvemen, allowing organizations to tailor AI systems to their specific domain, data, and user needs.[11]