micro1
micro1 is a data research platform that supplies expert human data, realistic RL environments, and contextual evaluations to train frontier models and assess AI agents. Its Realm, Cortex, and Robotics lines help labs improve reasoning, safety, and real-world performance.
Overview
Engage micro1 to scope goals, target domains, and success criteria. Collect expert demonstrations in Realm scenarios that reflect operational constraints. Run agents through Cortex evaluations with contextual scoring and diagnostics. Iterate on datasets, policies, and prompts, then re-test to quantify gains before deployment.
Capabilities
Ideal users include AI labs building frontier models, agent teams validating tool-use and planning, applied research groups in law, healthcare, and finance, robotics teams training embodied systems, and production leaders responsible for reliability. Safety, evaluation, and MLOps practitioners benefit from transparent scoring, reproducible tasks, and clear regression detection. Enterprise R&D groups use micro1 to de-risk deployments and document evidence for stakeholders and regulators.
- Generate expert-demonstrated trajectories in realistic RL environments reflecting real-world constraints.
- Run contextual, production-grade evaluations that capture reasoning steps and outcomes.
- Benchmark agents on law, healthcare, and finance with transparent, reproducible tasks.
- Curate high-fidelity robotics datasets for training embodied systems and controllers.
- Close the loop with targeted re-tests that quantify improvements and regressions.
Why It Matters
Who It's For
Getting started typically begins with the “Get in touch” flow or a data partnership. Teams define objectives, domains, and evaluation rubrics, then select or design Realm scenarios with expert demonstrators. Cortex evaluations are configured to reflect production contexts, capturing intermediate reasoning and outputs. micro1 manages secure data handling and provides dashboards and reports for iteration. Experts can join via the opportunities program to contribute demonstrations or reviews on defined tasks. This managed engagement model aligns incentives around quality, accountability, and measurable improvements over successive releases.
Expert human data plus realistic contexts is the shortest path to reliable agents.
Getting Started
In a crowded landscape of demos, micro1 focuses on auditable progress: expert data, realistic tasks, and context-aware scoring. The combination of Realm, Cortex, Robotics, and public benchmarks gives teams comparable baselines and actionable diagnostics. When reliability matters—regulated domains, embodied systems, or agent workflows—micro1 provides the infrastructure to train, evaluate, and iterate with confidence.
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micro1
micro1 Inc. is a data research company focused on advancing machine intelligence with expert human data, realistic training environments, and contextual evaluations. The team partners with AI labs and enterprises to improve reasoning, safety, and performance in production systems across sensitive, real-world domains.
micro1 is a data research company building infrastructure for advancing machine intelligence. The platform combines expert-demonstrated datasets, RL environments that mirror real-world scenarios (Realm), contextual agent evaluations for production feedback loops (Cortex), and high-fidelity robotics data. Teams use micro1 to generate grounded trajectories, measure reasoning quality, and reduce hallucinations across sensitive domains including law, healthcare, and finance. Public benchmarks—covering legal reasoning, pathology-report extraction, and spreadsheet-grounded financial analysis—provide transparent baselines for comparing models and agents. By centering expert humans, realistic tasks, and context-aware scoring, micro1 helps labs stress-test capabilities, align behaviors, and ship systems that perform under real constraints. Organizations can engage via data partnerships or tap into expert opportunities, depending on whether they need curated data, evaluations, or both. The result is a pragmatic path for training and validating frontier models and agentic workflows with evidence, not anecdotes.
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