Encord

Founded by Eric Landau

Annotate, curate, and evaluate multimodal training data for machine learning models

Pricing model

Demo

Rating

★ People love it

Time to value

Moderate Setup (1-3 hours)

You can use Encord to manage, curate, annotate, and evaluate large multimodal datasets for training artificial intelligence models. It supports video, text, audio, and sensor streams such as LiDAR and radar. You can run automated curation pipelines, find edge cases using embedding-based search, and coordinate human-in-the-loop annotations. The system also supports model evaluation, rubric-based checks, and reinforcement learning from human feedback. Teams building autonomous vehicles, robotics, healthcare devices, and computer vision applications can maintain full data lineage without migrating data out of their private cloud storage.

What Encord does

Annotate complex multi-frame video and point cloud sequencesFind visual outliers using embedding similarity searchesConfigure custom labeling rubrics and review workflowsRun automated pre-labeling across raw sensor datasetsEvaluate model predictions against human ground truthExport structured labels and metadata via API or SDKNative video, audio, text, and LiDAR multi-sensor annotation workflowsEmbedding-based search to identify rare edge cases and distribution gapsModel evaluation orchestration using RLHF and rubric-based comparisonsSensor fusion support for synchronized LiDAR, radar, and camera feedsZero data migration architecture keeping datasets inside private cloud storageModel-in-the-loop workflows to accelerate data labeling and curation