monte

Our Mission

Building effective AI is hard. Foundation models are powerful, but they do not become effective systems on their own. The real work lies in closing the gap between raw intelligence and the specific knowledge needed to do useful work.

To close that gap, AI cannot be static. It should get more specific over time. The best systems will learn as they work, respond to feedback, and grow into the role they are meant to serve. Monte exists to build those systems.

What We Do

Monte builds systems for continuous agent improvement.

We capture data from your workflows, build evaluation frameworks, and use our training infrastructure to power agents with custom models that run your company's workflows to your standards.

Our continuous learning loop helps agents improve through real-world execution and feedback from your team. Over time, they close the gap between general models and your company's domain expertise.

Who We Are

We are three recent Harvard graduates in Computer Science, Statistics, and Physics with backgrounds in AI research at Harvard, MIT Lincoln Lab, and NASA JPL. We have spent years working in AI/ML and building agents ourselves.