Realm training environments
Builds RL environments that mirror real-world scenarios to generate human data for agentic actions and improve model reasoning.
AI data platform for frontier model training and agent evaluation
micro1 is a data lab focused on training frontier models and evaluating agents. The company says it builds infrastructure around expert human data, real-world training environments, and contextual evaluations.
Its site presents three product areas: Realm for RL environments and expert data generation, Cortex for contextual evaluation and monitoring of agentic AI, and Robotics for high-fidelity robotics data. The public pages also surface research and benchmarks that focus on domain-specific reasoning and model behavior.
Builds RL environments that mirror real-world scenarios to generate human data for agentic actions and improve model reasoning.
Provides a contextual evaluation layer for agentic AI, with evaluations designed around real workflows and success criteria.
Supports human-in-the-loop data production with performance tracking for velocity, error rates, cost per task, and quality.
Uses expert domain specialists across more than 100 fields to create, review, and deliver complex datasets.
Positions robotics as a source of high-fidelity real-world data for embodied systems.
Surfaces research and benchmarks across reasoning, redlining, and domain-specific evaluation areas.
Use Realm when you need RL environments that resemble real-world scenarios and produce expert data for agentic actions.
Use Cortex when you want to evaluate agent outputs against real workflows, identify failure modes, and feed results into improvement loops.
Use the platform when your team needs domain experts to create, review, and deliver complex datasets in areas such as healthcare, legal, finance, coding, STEM, VLM, or audio.
Use the research and benchmark work as a reference point when comparing performance on pathology-report reasoning, contract redlining, tax reasoning, legal reasoning, or finance reasoning.
micro1 is presented as a data lab for training frontier models and evaluating agents with expert human data, real-world training environments, and contextual evaluations.
The site describes Realm as RL environments that mirror real-world scenarios, Cortex as a contextual evaluation platform for agent performance in production, and Robotics as a source of high-fidelity real-world robotics data.
The pricing page shown in the collected evidence repeats the homepage messaging, so the available evidence does not expose public plan names, prices, or limits.
The visible forms and page copy point to a company/demo flow and a separate application flow for experts looking for roles, but the source does not show deeper onboarding or setup details.
Traffic data is for reference only.
tropir.com
Aviro builds training environments for long-horizon tool use, focused on simulated enterprise workflows and multi-step grounded reasoning. Its public site features benchmarks, writeups, and evaluations for frontier models, not a general-purpose consumer app.
blopai.com
Blop writes browser tests as code, runs them in CI, clusters repeated failures, and opens pull requests to fix broken tests.
termo.ai
Termo is an AI agent platform with isolated VMs for research, automation, and build tasks.
mrge.io
AI code review for pull requests and codebases
vocera.ai
Voice testing and observability for conversational AI teams
modelscan.io
ModelScan compares model specs, pricing, context, modalities, and capabilities before integration.