Alessandro Cappelli

Co-founder and Chief Customer Officer at Adaptive ML, an AI startup building reinforcement learning operations infrastructure for enterprise large language model deployment. He previously worked in the Hugging Face/open-source AI ecosystem and is associated with work on large-scale language model datasets such as RefinedWeb/Falcon.

Enterprise GenAI Pilots Fail When Feedback Cannot Reach the Model

Alessandro Cappelli, co-founder and chief customer officer of Adaptive ML, argues that enterprise generative AI pilots fail to reach production because companies lack a systematic way to turn defects, user feedback, business metrics and production signals into model improvement. In a talk on Fortune 500 deployments, he says prompting and instruction fine-tuning can produce credible demos, but reinforcement learning is the mechanism needed to train models and agents against enterprise-specific environments, rewards and KPIs. His case is that agents make this feedback loop more urgent, because they consume more tokens, touch live systems and leave less room for error.

AI EngineerMay 12, 202612 min read