The AI data platform CV teams use to ship better models, faster.
Curate the frames that matter. Label with full traceability. Close the loop between data and model performance. Trusted by 350+ computer vision teams.
"The simplest way to deploy & scale."
Why leading CV teams are switching to Encord.
Stop labeling noise. Start labeling what moves the model.
Without curation, you're paying labelers to process empty frames and duplicates your model won't learn from. Encord lets you search by natural language, filter by metadata, and find visually similar frames using embeddings before annotation begins.
Stop labeling noise. Start labeling what moves the model.
Without curation, you're paying labelers to process empty frames and duplicates your model won't learn from. Encord lets you search by natural language, filter by metadata, and find visually similar frames using embeddings before annotation begins.
The annotation platform built for video
Most tools break video into frames, losing temporal context and introducing frame-matching errors. Encord renders video natively. Annotators scrub, play, and label continuously, with object tracking and interpolation built in.
Your models already handle detection. Use them to label.
Encord's agentic framework lets you bring your own models, route high-confidence predictions through automatically, and send edge cases to human review, no custom orchestration required.
Close the loop between labels and model performance.
Most platforms stop at export. Encord lets you compare model versions, slice performance by class or metadata, identify failure modes, and surface the data most likely to fix them all in one platform.
Close the loop between labels and model performance.
Most platforms stop at export. Encord lets you compare model versions, slice performance by class or metadata, identify failure modes, and surface the data most likely to fix them all in one platform.
Automotus runs AI-powered parking and curbside management across hundreds of cameras in cities and airports. As their network grew, footage was coming in faster than they could label. Using Encord, Automotus was able to curate their data, isolating edge cases, filtering out data that wouldn't improve model performance, and running AI-assisted review on what remained.
Read the full case studyimprovement in model performance (mAP)
reduction in dataset size for annotation
reduction in labeling costs
Additional resources for CV teams
Frequently asked questions
Encord's video native interface plays and renders footage continuously without pre-processing or frame extraction. Annotators scrub and label directly on the video which preserves temporal context, eliminates downsampling artefacts, and uses significantly less storage than frame-based workflows.
Yes. Encord's agentic framework lets you integrate custom detectors, domain-specific embeddings, or foundation models directly into your labeling workflow. High-confidence predictions are routed through automatically; low-confidence cases go to human review. No custom orchestration logic required.
That's a common starting point for CV teams. The question isn't whether you can build it, it's whether it's the best use of your engineering time. Most teams find that maintaining internal tooling grows faster than expected as use cases expand. Encord's build-vs-buy calculator lets you run the numbers on your specific setup.
Encord has a structured migration process and dedicated post-sales engineering support. Most teams run a parallel pilot on a subset of their data before full transition. If you're on a platform with a contract running out, that's typically the right moment to start, we can work backwards from your renewal date.
Yes on both. Encord is SOC 2 Type II certified. VPC deployment is available for teams with strict data residency or security requirements, including support for SSO, audit logs, and access controls. US-based annotators are also available on request for teams with data handling restrictions.
Both. Encord has an in-house annotation team available for teams that need labeling capacity alongside the platform. This can be used for ongoing volume, burst capacity, or specialist tasks, independently of the platform subscription.