Lessons from Trillion Token Deployments at Fortune 500s — Alessandro Cappelli, Adaptive ML

2026-05-12 · Source: AI Engineer · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Robotics & Autonomous Systems · Depth: Intermediate, long

Summary

Adaptive ML, co-founded by Alessandro Cappelli, offers an RL Ops platform designed to help large enterprises like AT&T and Manulife build, evaluate, and serve specialized large language models in production. The platform addresses the "myth of the last mile," where 95% of GenAI pilots fail to reach production because current methods like proprietary models or instruction fine-tuning lack systematic improvement mechanisms. Reinforcement Learning (RL) is presented as the solution, enabling continuous retraining and refinement by integrating feedback from client interactions, business metrics, and environmental rewards. RL is significantly more effective than instruction fine-tuning or prompting, allowing for smaller, faster, and cheaper-to-serve models, which is crucial for enterprise-scale adoption and managing tokenomics. The Adaptive Engine industrializes RL, providing pre-built recipes and managing the complexity of orchestrating multiple LLMs, making it accessible for businesses to deploy and own their AI solutions.

Key takeaway

For CTOs and AI Architects aiming to move GenAI pilots from MVP to production, adopting a Reinforcement Learning (RL) strategy is critical. RL systematically integrates feedback, enabling continuous model improvement and unlocking the use of smaller, faster, and more cost-effective models. This approach ensures long-term scalability and ownership, mitigating the risks associated with proprietary models or iterative instruction fine-tuning. Evaluate RL platforms like Adaptive Engine to streamline complex RL implementations and accelerate your model lifecycle.

Key insights

Reinforcement Learning systematically integrates feedback to accelerate LLM lifecycle, enabling production-ready, cost-effective, and performant models.

Principles

• RL is disproportionately more effective than instruction fine-tuning.
• Smaller models are cheaper and faster to serve at scale.
• Ownership of data and solution is critical for long-term stability.

Method

RL allows for systematic integration of feedback from diverse sources (client, business metrics, environment) to continuously retrain and refine models, creating synthetic datasets and using LLMs as judges for reward signals.

In practice

• Use RL to train smaller, faster models for cost-sensitive use cases.
• Define rubrics and scenarios for LLM judges to provide reward signals.
• Leverage existing data to train mock users for agent environments.

Topics

• Reinforcement Learning
• GenAI Production
• LLM Agents
• RL Ops Platform
• Model Lifecycle Acceleration

Best for: CTO, VP of Engineering/Data, AI Architect, MLOps Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Engineer.