microagi

Summary

About

About microagi

Atlas captures manual factory work through dedicated sensing hardware and converts it into task demonstrations for robot training. microagi combines those demonstrations with simulation and model adaptation, fine-tuning control policies around the customer’s actual objects, workstations and operating sequence.

shift expands the data layer through first-person recordings of physical work collected across multiple countries. Together the systems connect broad human-action data with site-specific industrial demonstrations, giving robotics developers a route from general physical understanding to a deployed policy for a particular task.

PRICE
Range 1Y
Return
High
Low
Updated

Articles

Funding

Investors

  • Hummingbird - Lead - Unknown - 1 funding event - first seen 2026-07-16
  • LocalGlobe - Participant - Corporate / CVC - 1 funding event - strategic/corporate - first seen 2026-07-16
  • Northzone - Participant - Unknown - 1 funding event - first seen 2026-07-16
  • redalpine - Participant - Unknown - 1 funding event - first seen 2026-07-16
  • Village Global - Participant - VC - 1 funding event - first seen 2026-07-16

Team

  • Zeno Hamers - Managing Director, MicroAGI CH - Leads the Zürich research headquarters and Swiss operating entity.
  • Bercan Kilic - Co-founder and CEO - Leads microagi's industrial robot deployment and embodied-AI strategy.
  • Yoan Iliev - Co-founder - Co-founded microagi and contributes high-performance engineering experience.
  • Anton Poletaev - Co-founder - Co-founded microagi and contributes AI research experience.
  • Nico Nussbaum - Co-founder - Co-founded microagi and contributes engineering experience.
  • Artjem Weissbeck - Co-founder - Co-founded microagi and brings prior company-building experience as a serial entrepreneur.

Research

  • microagi releases MicroPlanBench next-action benchmark

    2026-10-14 - Research Publication - Each model receives eight seconds of 720p video at ten frames per second, with five attempts per clip. GPT-6 Astra reached a 65.6% mean pass rate under the company’s LLM-judged protocol; the judge checks actions, objects, destinations and hand assignment. The evaluation set totals just under 27 minutes; human reviewers selected cases with one reasonable immediate next action.

    Source

Timeline

Events

Notes

Profile notes

  • microagi uses human service work to build physical-AI training data

    reviewed 2026-07-18 - microagi's shift service uses first-person capture equipment to collect household-task demonstrations through human workers. Its Atlas platform applies the same data-and-deployment model in industrial settings, where operational task data is used to prepare and refine robotic systems.

    Source

Relations

Relationships

  • European Student Robotics Association

    Student Robotics Partner - microagi supports ESRA as a financial and hands-on partner building a pan-European student robotics network.

Context

Comparable profiles

  • micro1

    stack adjacency - Physical AI Data Infrastructure

  • Xspark AI

    stack adjacency - Physical AI Data Infrastructure

  • Vision Lab

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • Maniformer

    stack adjacency - Embodied Data Capture

  • Jiiov / 极豪科技

    stack adjacency - Embodied Data Capture

  • 6thSense

    stack adjacency - Physical AI Data Infrastructure

  • Sureform

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • Physen AI

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • Mecka AI

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • MANUS

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • XDOF

    stack adjacency - Embodied Data Capture · Physical AI Data Infrastructure

  • Lightwheel

    stack adjacency - Physical AI Data Infrastructure

Shared workflows

Context tags

  • Embodied AI - Capability - microagi develops data and deployment infrastructure for embodied intelligence.
  • Physical AI data infrastructure - Stack Layer - shift collects and prepares real-world human-task data for robotics training.
  • Embodied data capture - Stack Layer - Atlas and shift capture physical work in factories and everyday environments.
  • Factory robot deployment - Workflow - Atlas supports task-specific model training and factory robot deployment.