The data platform behind the world's leading video intellience teams
Use Encord to find the right footage in 24/7 streams, label it with full traceability, and increase model performance by 20%. That's why the leading video intelligence teams are supported by Encord.
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
Why the top video intelligence teams are switching to Encord.
Only label footage that will improve your model
Most surveillance teams annotate too much. The signal is buried in hours of empty corridors, duplicate frames, and low-quality captures. Encord lets you find it before a single labeler touches the data, using Natural Language Search and Metadata Filters for example.
Only label footage that will improve your model
Most surveillance teams annotate too much. The signal is buried in hours of empty corridors, duplicate frames, and low-quality captures. Encord lets you find it before a single labeler touches the data, using Natural Language Search and Metadata Filters for example.
Label video continuously
Image annotation tools break video into frames. Surveillance footage doesn't work that way, and neither should your labeling pipeline with temporal labels and interpolation which propagates labels across frames.
Your models already handle detection. Use them to label faster.
Encord’s custom agentic workflows route high-confidence labels through automatically, and low-confidence ones to human review without manual oversight. Faster and more flexbile than building it yourself or off-the-shelf models.
Traceable, auditable, and exportable labels
Surveillance data carries legal and operational weight. Encord gives you full lineage on every label: who created it, who reviewed it, when it changed, and what model or human touched it. Every annotation decision is traceable, auditable, and exportable.
Traceable, auditable, and exportable labels
Surveillance data carries legal and operational weight. Encord gives you full lineage on every label: who created it, who reviewed it, when it changed, and what model or human touched it. Every annotation decision is traceable, auditable, and exportable.
Millions of frames. How Vialytics Built Their Smart Cities CV Model.
Building Video Intelligence or Smart Cities? Check out our additional resources.
Frequently asked questions
Yes. Encord is API and SDK-first, built to plug into existing pipelines rather than replace them. It integrates natively with cloud storage (S3, GCS, Azure), supports SSO providers including Okta, and connects with tools like Weights & Biases and Sagemaker. You can trigger labeling jobs programmatically, version datasets, and export in formats including YOLO, COCO, and custom schemas, without changing how your downstream pipeline consumes data.
Encord is SOC 2 Type II certified, GDPR compliant, and HIPAA compliant. Your data stays in your cloud. Encord connects to your existing storage via cloud integrations and never requires you to migrate footage to Encord-owned infrastructure. You get granular role-based access control, audit logs on all data access, and configurable data handling policies. For teams with EU data residency requirements, EU-based storage is supported.
Every annotation decision in Encord carries full lineage; who created it, who reviewed it, what model pre-labeled it, when it changed, and what stage of the workflow it passed through. This applies to both human and model-generated labels. Audit logs are exportable, making them usable for internal compliance reviews, client reporting, or legal defensibility. For teams replacing ad-hoc labeling processes with a traceable operation, this replaces what would otherwise require custom engineering to build.
Most teams that have built internal tools find they've solved the annotation UI problem but still lack the data operations layer. Encord's build vs. buy calculator lets you run the numbers on what maintaining that infrastructure costs against a platform contract; the gap is usually significant once engineering time is factored in.
Encord is built for teams operating at the scale surveillance generates billions of images, continuous video streams across hundreds or thousands of cameras. Data ingestion is fast, metadata filters and natural language search operate at frame level across large datasets, and annotation workflows support large distributed teams with role-based task routing.
Encord has a structured migration path from the most common platforms. Existing ontologies, datasets, and label formats can be imported directly via SDK. Most teams run a parallel pilot on a subset of their pipeline before full cutover, which limits engineering risk.