The complete data pipeline for AI teams

From raw sensor feeds to production-ready training data. Encord combines expert annotation services, AI-assisted tooling, and real-world data collection infrastructure so your team can build models that work.

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Most annotation vendors
were built for a different era

Your models are multimodal. Your data is complex. But most annotation vendors were designed for image classification workflows from 2018. They treat every project the same, scale through volume, and hand back labeled data with no visibility into how quality was assessed. The result is annotation bottlenecks, inconsistent quality, and training pipelines that stall waiting on data you can't trust.

One partner across the full data lifecycle

Encord covers every stage of the data development process for teams building physical and multimodal AI systems.

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

Data collection

In-field operators, teleoperation facilities, and configurable lab environments to generate training data at the source. We design the collection protocol with your team before scaling.

Curation and visualization

Embedding-based filtering, similarity search, and natural language queries to find the data that actually improves your model. Stop training on redundant episodes or uncaught edge cases.

Annotation and evaluation

AI-assisted annotation with SOTA model integrations, domain expert matching, and structured QA workflows. Covers video, LiDAR, DICOM, audio, text, and code.

Deployment feedback

Human-in-the-loop supervision at deployment through remote teleoperation. Continuous iteration on your data collection and annotation policies based on real-world model failures.

Why Encord?
Design partner, not fulfillment vendor.

We do not apply generic rubrics or fill headcount. We work with your team to define what good data looks like for your specific task, iterate on annotation standards before scaling, and maintain traceability across every decision. When quality breaks, we diagnose the root cause.