Multimodal annotation
Annotate images, videos, audio, text, DICOM, HTML, geospatial data, LiDAR, ECG, and other multimodal inputs in one platform.
Multimodal data platform for managing and evaluating AI training data
Encord is a multimodal data layer for AI teams that need to manage, curate, annotate, align, and evaluate large datasets across the model lifecycle. The product is positioned for physical AI and enterprise use cases, with support for data from sensors, video, text, and related modalities.
The platform combines annotation, curation, model alignment, and evaluation in one system. Source pages describe native multimodal workflows, human-in-the-loop review, and API/SDK-first deployment, with options that include cloud, VPC, and on-prem setups.
Annotate images, videos, audio, text, DICOM, HTML, geospatial data, LiDAR, ECG, and other multimodal inputs in one platform.
Use customizable workflows, role-based access controls, task assignment, and multi-stage reviews to manage labeling and review at scale.
Apply AI-assisted labeling tools such as model prediction import, SAM 2 support, object tracking, interpolation, and advanced object tracking.
Search, curate, and manage datasets with multimodal search, outlier detection, image duplication detection, embeddings, and dashboards.
Run label validation, label exploration, error detection, model evaluation, model comparison, and active learning pipelines.
Use cloud, VPC, or on-prem deployments, plus API/SDK-first access and zero data migration, to fit existing infrastructure.
Build and label training datasets for robotics, autonomous vehicles, drones, and other physical AI systems that rely on synchronized sensor streams and 3D data.
Set up human-in-the-loop workflows for annotating and reviewing images, videos, audio, text, and medical data with quality controls and performance tracking.
Find rare edge cases, close distribution gaps, and prepare production data using embedding-based search, model-in-the-loop curation, and dataset management tools.
Evaluate production models with rubric-based review, pairwise comparison, RLHF, and label/model analytics to identify failure modes and feed them back into training.
Organize annotation and review across distributed teams with multiple workspaces, SSO, role-based access, and enterprise support.
Encord is designed for multimodal data labeling across images, videos, audio, text, DICOM, HTML, geospatial data, and related sensor inputs. The platform centers on customizable workflows, AI-assisted labeling, and human-in-the-loop review.
The source describes Encord as API/SDK-first and says there is zero data migration, with data remaining in your cloud. It also presents cloud, VPC, and on-prem deployment options on the pricing page.
Pricing is organized into Starter, Team, and Enterprise plans. Starter is for individuals and small teams, Team adds data agents and analytics, and Enterprise adds multiple workspaces, SSO, enterprise SLA and support, and VPC/on-prem deployments.
Yes. The source says Encord supports customizable workflows with role-based access, task assignments, review stages, and workflow automation.
The source supports AI-assisted workflows and integration with models such as GPT-4o, LLaMa 3.2, and Gemini 1.5 Flash on the annotation page, but it does not provide a full public integration list.
Traffic data is for reference only.
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