Encord

Summary

About

About Encord

Encord provides the data pipeline between raw robot logs and trainable perception or control models. It ingests time-aligned camera, depth, LiDAR, radar, IMU, force-torque and joint-state streams, preserving temporal context so teams can label action phases, contact events, trajectories and 3D objects against a shared ontology.

Annotation tools cover images, native video, point clouds, audio and text, with model-assisted segmentation, object tracking, workflow routing and human review. Active learning surfaces unusual or low-confidence samples for curation, and deployment failures can be routed back into new training sets. The platform supports cloud-resident data through APIs and handles MCAP and ROSBAG formats for robotics, autonomous vehicles, drones and vision-language-action development.

PRICE
Range 1Y
Return
High
Low
Updated

Articles

Funding

Investors

  • Bright Pixel Capital - Participant - VC - 1 funding event - first seen 2026-02-26
  • Crane Venture Partners - Participant - VC - 1 funding event - first seen 2026-02-26
  • CRV - Participant - Unknown - 1 funding event - first seen 2026-02-26
  • Harpoon Ventures - Participant - VC - 1 funding event - first seen 2026-02-26
  • Isomer Capital - Participant - VC - 1 funding event - first seen 2026-02-26
  • N47 - Participant - Unknown - 1 funding event - first seen 2026-02-26
  • Wellington Management - Lead - Angel / Individual - 1 funding event - first seen 2026-02-26
  • Y Combinator - Participant - Unknown - 1 funding event - first seen 2026-02-26

Team

  • Ulrik Stig Hansen - Co-Founder and Co-CEO
  • Eric Landau - Co-Founder and CEO - Leads Encord's multimodal AI data platform, including data curation and annotation workflows for robotics and physical AI.

Research

  • Encord launches Data Labs with EMG-1 and Encord-REACH

    2026-10-20 - Research Publication - EMG-1 contains 307 hours of field clip-time with wrist EMG and synchronized egocentric video, plus a separate 10.4-hour laboratory collection with optical motion capture. Wearable Devices supplied Mudra Pro technology for field capture and Mudra Ultimate for the laboratory collection. Encord reports about 25% lower fingertip tracking error from EMG-video fusion in its tests; dataset access is advertised through registration.

    Source

Timeline

Events

Relations

Relationships

  • Wearable Devices

    Technology Partner - Encord and Wearable Devices collaborated on EMG-1, pairing wrist muscle signals with egocentric video for robotics data research.

  • CoreWeave

    Partner - CoreWeave and Encord combine cloud compute with multimodal data-platform workflows for Physical AI development.

Context

Comparable profiles

  • Mecka AI

    stack adjacency - Physical AI Data Infrastructure · Robotics Data Infrastructure Layer

  • LabyrinthAI

    stack adjacency - Physical AI Data Infrastructure

  • shotwell.ai

    stack adjacency - Robotics Data Infrastructure Layer

  • CrowdWorks

    stack adjacency - Physical AI Data Infrastructure

  • micro1

    stack adjacency - Physical AI Data Infrastructure

  • Yitai Embodied

    stack adjacency - Physical AI Data Infrastructure

  • AstrAlive

    stack adjacency - Robotics Data Infrastructure Layer

  • Lightwheel

    stack adjacency - Physical AI Data Infrastructure · Robotics Data Infrastructure Layer

  • 6thSense

    stack adjacency - Physical AI Data Infrastructure · Robotics Data Infrastructure Layer

  • Cortex AI

    stack adjacency - Physical AI Data Infrastructure · Robotics Data Infrastructure Layer

  • Sureform

    stack adjacency - Physical AI Data Infrastructure · Robotics Data Infrastructure Layer

  • Xspark AI

    stack adjacency - Physical AI Data Infrastructure · Robotics Data Infrastructure Layer

Shared workflows

  • TwelveLabs

    workflow adjacency - Multimodal Robotics Data Labeling

Context tags

  • Robot model supply chain - Customer Environment
  • Robotics data infrastructure layer - Stack Layer
  • Physical AI data infrastructure - Stack Layer
  • Multimodal robotics data labeling - Workflow