Monte

Monte builds specialized AI agents trained on a company's own work. It describes itself as an applied research lab and as the post training and continual learning layer for proprietary intelligence, and it works with enterprise teams rather than selling a self serve tool. Its process has four steps. Capture extracts training signal from real work, such as traces, outcomes, policies and expert judgment. Measure builds evaluations around the workflows, constraints and edge cases an agent must meet in production. Train specializes a model using supervised fine tuning, reinforcement learning or distillation. Compound builds memory from production traffic and routes outcomes back into training so the agent keeps improving. The Monte platform shows training runs with live metrics and checkpoints, evaluations, artifacts such as recipes, environments, datasets and benchmarks, serving of checkpoints and inference, and team projects and credentials. The vendor says it supports any open weight model and any training method, and that the resulting model is proprietary to the customer. Monte's researchers embed with the customer's team to build the evaluation, memory and post training systems around real workflows. The vendor does not publish pricing, and visitors are directed to book a demo.