Encord

What Encord does

Encord provides the data infrastructure layer for Physical AI and Enterprise teams, enabling them to manage, curate, and annotate AI data. The platform supports multimodal workflows and native agent integrations for various data types including video, image, audio, LiDAR, text, document, geospatial, and HTML. Encord offers tools for native video annotation, LiDAR, audio, text, and sensor fusion, with built-in label lineage and quality controls. It also facilitates data curation and collection through embedding-based search and model-in-the-loop curation to identify edge cases and close distribution gaps. For models in production, Encord orchestrates RLHF, rubric-based evaluation, and pairwise comparison to identify and address model failures. Additionally, Encord provides Data-as-a-Service, including annotation services with vetted domain experts and collection services for training-ready data, particularly for physical AI. The platform is designed to scale from prototype to production for various use cases, offering different pricing tiers (Starter, Team, Enterprise) with features like AI-assisted labeling, quality control, data curation, active learning pipelines, and comprehensive support.

Key capabilities

• Customizable multimodal workflows
• Native agent integrations
• Native video annotation
• LiDAR annotation
• Audio annotation
• Text annotation
• Sensor fusion in workflows
• Label lineage
• Quality controls for production scale
• Embedding-based search for data curation
• Model-in-the-loop curation
• RLHF orchestration
• Rubric-based evaluation for models
• Pairwise comparison for models
• API/SDK-first design
• Zero data migration (data stays in user's cloud)
• Image & video annotation toolkit
• Complex & dynamic ontologies
• Customizable workflows
• Data agents
• Performance analytics
• Model evaluation
• Multiple workspaces
• Single sign-on (SSO)
• Enterprise SLA & support
• VPC & on-prem deployments
• Multi-factor authentication
• AI-assisted labeling
• Model prediction import
• Segment Anything Model 2 integration
• Object tracking & interpolation
• Advanced object tracking
• Consensus workflows
• Annotator training modules
• Role-based access controls
• Annotator performance dashboard
• Advanced and custom analytics
• Data visualization
• Dataset curation, querying & filtering
• Multi-modal search
• Outlier detection
• Image duplication detection
• Dashboards & analytics
• Pre-computed embeddings
• Custom metadata fields & schema
• Custom embeddings
• Custom quality metrics
• Label & model analytics
• Label validation
• Label exploration
• Label error detection
• Data and label tagging
• Model analytics & plots
• Model comparison
• Custom model metrics
• Active learning pipelines
• Dataset balancing
• Acquisition functions
• Custom acquisition functions
• Seamless data integration with existing cloud infrastructure
• Support for large datasets (images, videos, audio, PDFs, DICOM files)
• Granular metadata and attribute filtering
• Embeddings-based and natural language search
• Data encryption (AES-256 at rest, TLS 1.2/1.3 in transit)
• Flexible deployment (US and EU, VPC, on-premises)
• Continuous security monitoring
• Multi-layered access controls (role-based system)
• Full audit logs and annotation traceability
• SOC 2, GDPR, and HIPAA compliant infrastructure

Use cases

• Training robotic perception, manipulation, and embodied AI
• Building perception systems for autonomous vehicles and ADAS
• Managing multi-sensor datasets for autonomous navigation, inspection, and monitoring in drones and aerial autonomy
• Training AI for monitoring and responding to complex physical environments in industrial and manufacturing settings
• Quality annotation, evaluation, and RLHF for enterprise AI
• Developing healthcare and surgical AI
• Frontier and generative AI development
• Video intelligence applications
• Defense applications
• Sports AI development
• Voice AI development
• Annotating high-volume video for smart spaces
• Curating operational edge cases for smart space models
• Aligning model behavior to specific deployment environments for smart spaces
• Table extraction model improvement
• Scaling models across image, video, text, and voice modalities
• Collecting embodied, egocentric, and sensor data for robotics and physical AI models
• Training VLA models on structured observation-action data
• Action captioning at scale

AI approach

Encord provides a data infrastructure layer for Physical AI and Enterprise teams, focusing on managing, curating, and annotating multimodal AI data. They offer customizable workflows for various data types including video, image, audio, LiDAR, text, document, and geospatial data. Their platform supports data collection, curation using embedding-based search and model-in-the-loop, and model alignment through RLHF, rubric-based evaluation, and pairwise comparison. They also offer annotation and collection services with vetted domain experts and in-field operators. Encord integrates with state-of-the-art model integrations like SAM 3 and GPT-4o for AI-assisted labeling.

Tech named: RLHF, SAM 3, GPT-4o

Industries served

• Robotics
• Autonomous Vehicles
• ADAS
• Drones & Aerial Autonomy
• Industrial & Manufacturing
• Retail
• Construction
• Warehouses
• Healthcare & Surgical AI
• Frontier & Generative AI
• Video Intelligence
• Defense
• Sports AI
• Voice AI
• Logistics
• Public Safety

What it says sets it apart

• Data infrastructure layer for Physical AI
• Multimodal by design, from pre and post-training to deployment
• End-to-end data infrastructure partner for physical AI, from collection to deployment feedback
• One platform for the full data pipeline
• Data-as-a-Service for production-grade AI
• Expert annotation services with vetted domain experts
• Training-ready data collection services for physical AI
• Designed for reliable AI at scale
• Flexibility and infrastructure for rapid iteration
• Native support for multiple simultaneous annotators with fast sync
• Pipeline visibility for real-time bottleneck identification
• Structured internal review processes for complex annotation tasks
• Proactive identification and addressing of annotation inefficiencies
• Collection protocols built backwards from the training pipeline
• Support for embodiment-specific, teleoperation, egocentric, and UMI data collection
• Zero ingestion overhead for collected data
• Ability to capture failure modes through remote teleoperation and feed back into data pipeline
• Tools built specifically for VLMs and VLAs, supporting long video timelines and complex schemas
• Comprehensive security, governance, and compliance (SOC 2, HIPAA, GDPR)
• Customer data stays in their own cloud

Funding rounds we track

From the AI funding tracker — rounds as reported by the linked publications.