HABIT

Founded by Harish Palani & Michael Trehan in 2025

Deploy autonomous research agents to accelerate development and inference of frontier world models

Pricing model

Demo

Rating

★ Mixed Reviews

Time to value

Long Setup (> 1 day)

You can use HABIT to accelerate the development and execution of world models through autonomous research agents. The system provides an inference environment designed specifically for complex simulation, spatial reasoning, and predictive modeling tasks. By deploying specialized research agents, technical teams can automate hypothesis testing, run continuous model evaluations, and inspect robotic or physical world representations at scale. This allows machine learning teams and researchers to conduct deep experiments faster without manually orchestrating every evaluation pipeline, model inference step, or spatial agent benchmark across their robotics infrastructure.

What HABIT does

Deploy autonomous agents to test model behaviorExecute inference queries against world modelsEvaluate model predictions against physical simulationsTrack simulation metrics across research runsRun automated hypothesis tests on training dataGenerate simulated sensor feeds for testingAutonomous research agents for hypothesis explorationSpecialized inference engine for frontier world modelsAutomated spatial reasoning and simulation benchmarksContinuous evaluation pipelines for physical AI systemsSynthetic environment generation for model trainingHigh-throughput model inference infrastructure