Encord
"Train and Run AI on the Right Data"
London-based AI data platform for physical AI teams — annotate, curate, and evaluate multimodal datasets for robots and autonomous systems.
Encord is a London-headquartered AI data infrastructure company founded in 2021 by Ulrik Stig Hansen and Eric Landau (Y Combinator W21), providing the end-to-end data layer for physical AI development. The platform enables computer vision and robotics teams to annotate, manage, version, and evaluate large-scale multimodal datasets — spanning images, video, DICOM, LiDAR, and 3D point clouds — within a single collaborative environment. Encord's toolset includes AI-assisted labeling with temporal interpolation and object tracking for video, Encord Index for petabyte-scale dataset search and curation, and model evaluation pipelines.
The company serves 300+ teams including Toyota, Skydio, and Maxar, supporting use cases from humanoid robot training to autonomous vehicles and medical imaging. In February 2026, Encord raised a $60 million Series C led by Wellington Management, bringing total funding to $110 million. Data volume on the platform grew from 1 petabyte to 5+ petabytes within 18 months.
Encord is SOC 2 Type II certified and HIPAA/GDPR compliant.
Primary type & automation activities this supplier delivers:
Encord Annotate
- Model: Encord Annotate
- Launch Year: 2021
AI-assisted video and image annotation platform with temporal interpolation and object tracking for physical AI and computer vision teams.
Contact Encord
WEBSITE
https://encord.comHEADQUARTERS
United Kingdom
Company Facts
Founded
2021
Primary Role
Software/Algorithm
Company Size
-
Primary Region
Europe
Annual Sales
-
Funding Stage
-
Funding Total
-
Certifications & Memberships
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Handbook of Marine Craft Hydrodynamics and Motion Control Thor I. Fossen — RSF Specialist Shelf · Book S1 of 6
Added mass, wave loading, DVL navigation: the marine GNC reference for USV, ROV and AUV service engineers. The hardest book on the RSF list, 5/5.
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Introduction to Robotics J. J. Craig — RSF Top 10 Robot Education Textbooks · No. 6
Six of ten benchmark universities teach kinematics from Craig. Frames, transforms and D-H parameters: the language vendor manuals already speak.
Modern Robotics K. M. Lynch & F. C. Park — RSF Top 10 Robot Education Textbooks · No. 8
The book the field agreed to make free: full preprint PDF, video lectures and open code from the authors. Screw theory and kinematics, taught rigorously.
Autonomous Mobile Robots Siegwart, Nourbakhsh & Scaramuzza — RSF Top 10 Robot Education Textbooks · No. 3
AMR and AGV fleets are the biggest robot service market. Siegwart decodes the vendor manual: odometry, localization, cost maps.
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Encord and Zander Labs Use EEG Data to Advance Physical AI Training Techniques
In a warehouse in San Leandro, California, a worker is participating in an experiment that combines a data collection helmet with EEG sensors to train robots. This collaboration between Encord and Zander Labs aims to address the scarcity of real-world training data for physical AI, which is a significant challenge in the field. The importance of this experiment lies in its potential to generate valuable training data by capturing the neural activity of operators during tasks. This data can inform robot models about operator states, such as confusion or focus, enabling more efficient training and resource allocation. Encord is also collecting remote control data and first-person videos to create a comprehensive data production system. Looking ahead, the integration of EEG helmets, muscle sensors, and detailed annotations could revolutionize how robots are trained, providing the necessary real-world data that is currently lacking. No further timeline was disclosed at the time of publication.
Physical AI Robot Training Data Collection EEG Technology
Encord Explores Brain Wave Data to Enhance Physical AI Training in California
Encord, a data tooling company, is pioneering the use of brain wave measurement to enhance physical AI training. Located in San Leandro, California, the company is conducting trials with a brain wave headset developed by Zander Labs, aiming to create a unique data set that captures mental states during robotic training tasks. This initiative is significant as it addresses the critical shortage of real-world training data for humanoid and warehouse robotics. Encord's approach could potentially revolutionize how robotics companies generate and utilize training data, moving beyond traditional methods that often fall short in fidelity and scale. Looking ahead, Encord plans to evaluate the effectiveness of the brain wave-tagged data set in improving robotic performance. The outcome of this trial could determine whether the company will expand this innovative data generation method, which is seen as essential for overcoming the current data bottleneck in robotics.
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