Data collection, annotation and evaluation services
Encord combines expert annotation services, AI-assisted tooling, and real-world data collection infrastructure so your team can build models that work in deployment.
“A big reason why we decided to partner with Encord was because of the quality bar we saw in doing POCs with a ~dozen companies.”
Devi Parikh
Co-Founder & Co-CEO of Yutori
One partner across the full data lifecycle
Encord covers every stage of the data development process for teams building physical and multimodal AI systems. One unified service that feeds insights straight back into your collection plan, so data bottlenecks never show up in your model's performance.
Video
Image
Audio
LiDAR
Text
Document
Geospatial
HTML
The data flywheel
Turn production deployment into a training signal. Route low-confidence predictions back into annotation queues, track for failure modes that are underrepresented, and continuously tighten your training distribution.
The data flywheel
Turn production deployment into a training signal. Route low-confidence predictions back into annotation queues, track for failure modes that are underrepresented, and continuously tighten your training distribution.
Data collection
Encord collects data at the source using in-field operators, teleoperation facilities, and configurable lab environments, designing the collection protocol with your team before any of it scales.
Curation and visualization
We help you find the data that actually improves your model, through embedding-based search, similarity queries, and plain-language filtering.
Annotation and evaluation
AI-assisted annotation with SOTA model integrations, domain expert matching, and structured QA workflows. We work across video, LiDAR, DICOM, audio, text, and code.
Deployment feedback
Human-in-the-loop supervision at deployment through remote teleoperation. Using data from real world model failures, we help you continuously iterate on your data collection and annotation policies.
We’re a design partner, not a fulfillment vendor.
Before anything scales, we work with your team to define what good looks like for your specific task – maintaining full traceability across every decision so you can diagnose quality drift if it happens later.
We’re a design partner, not a fulfillment vendor.
Before anything scales, we work with your team to define what good looks like for your specific task – maintaining full traceability across every decision so you can diagnose quality drift if it happens later.
You're in good company
Encord is used by 300+ frontier AI teams to deploy production-ready AI models.
Enterprise-grade.
Built for scale.
Designed for reliable AI.
Built for scale.
Designed for reliable AI.
API/SDK-first. Zero data migration. Your data stays in your cloud.
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Frequently asked questions
Both. Encord provides trained annotators across video, image, audio, text, LiDAR, DICOM, and multimodal data, and your in-house team can work on the same platform. Hybrid setups also support teams transitioning from an existing BPO without losing annotator continuity.
Multi-stage review with configurable consensus, role-based reviewers, line-item feedback, and full audit trails on every label. Quality benchmarks are agreed upfront and annotation standards are tested with your domain experts before scaling.
Yes! pre-labels from your own models import via SDK or agent workflows, with predictions routed to human review. For teams without a pre-labeling model, Encord's built-in foundation models (SAM, DINOv2, custom agents) handle the automation.
Encord supports annotation for images, videos, audio, text, DICOM (medical imaging), HTML documents, and more.
Annotators specialise across medical imaging, robotics, VLA captioning, ADAS, financial services, multilingual audio, and behavioural video. For highly specialised work, annotation guidelines are built alongside your subject matter experts before scaling, so domain knowledge is built into the workflow.
Three pillars: data collection (in-field, in-lab, teleoperation, and synthetic capture), annotation with built-in QA, and evaluation services covering model output review, red-teaming, preference ranking, and benchmarking. Teams can start at any stage and expand as their models mature.
In-house works for stable, narrow tasks but pulls ML teams away from model development to manage hiring, training, and QA. Traditional BPOs scale through headcount and apply the same process to every project, with limited visibility into who's labelling and how quality is checked. Encord combines a managed team with the platform underneath, giving you domain-matched annotators and traceable QA without running the operation yourself.
Get the data right
300+ of the best AI teams in the world use Encord. Join them.