{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"encord","slug":"encord","name":"Encord","type":"dataset","url":"https://encord.com","page_url":"https://unfragile.ai/encord","categories":["model-training","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"encord__cap_0","uri":"capability://data.processing.analysis.automated.multimodal.annotation.with.model.assistance","name":"automated-multimodal-annotation-with-model-assistance","description":"Reduces manual annotation effort by leveraging pre-trained vision models (Segment Anything Model 2, custom embeddings) to generate initial predictions that annotators refine rather than label from scratch. Integrates model predictions via API import and supports consensus workflows across multiple annotators to validate AI-assisted suggestions, with per-tier constraints on active learning data volumes (50k for Starter, 1m for Team, 10m for Enterprise).","intents":["I want to reduce annotation costs by 40-60% using AI to pre-label images and videos before human review","I need to import model predictions from my training pipeline and have annotators correct them efficiently","I want to run consensus labeling workflows where multiple annotators validate AI suggestions to ensure quality"],"best_for":["computer vision teams with large unlabeled datasets seeking cost reduction","organizations building iterative ML pipelines where model predictions feed back into annotation","enterprises requiring high-confidence labels through multi-annotator consensus on AI suggestions"],"limitations":["Model-assisted labeling available only in Team tier and above; Starter tier cannot import model predictions","Active learning data volume capped at tier limits (50k Starter, 1m Team, 10m Enterprise) — exceeding requires upgrade","SAM2 integration is pre-built; custom model integration requires API-based prediction import with no native framework abstraction","Consensus workflows add annotation latency; no SLA specified for turnaround time with multi-annotator validation"],"requires":["Team tier or above subscription","Pre-trained model predictions in supported format (COCO JSON, YOLO, or custom via API)","Minimum 2 annotators for consensus workflows (optional but recommended for quality)"],"input_types":["images (JPEG, PNG, WebP)","video frames (MP4, MOV, AVI)","model predictions (COCO JSON, YOLO format, or custom API payload)","3D point clouds (LiDAR, RGB-D sensor fusion)"],"output_types":["annotated bounding boxes, polygons, polylines, keypoints","segmentation masks (instance and semantic)","classification labels with confidence scores","structured annotation metadata with lineage tracking"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_1","uri":"capability://data.processing.analysis.video.native.temporal.annotation.with.tracking","name":"video-native-temporal-annotation-with-tracking","description":"Provides frame-by-frame and temporal annotation workflows optimized for video data, with advanced object tracking that propagates labels across frames to reduce per-frame labeling effort. Supports multi-modal sensor fusion (RGB-D, LiDAR + video) for autonomous driving and robotics use cases, with frame interpolation and keyframe-based workflows to minimize manual frame annotation.","intents":["I need to label objects across hundreds of video frames without manually annotating each frame","I want to fuse RGB video with LiDAR or depth sensor data for 3D object detection training","I need to track object identity across video sequences for autonomous vehicle or robotics datasets"],"best_for":["autonomous driving teams building perception datasets with multi-sensor fusion","robotics companies requiring temporal consistency in object tracking across video sequences","video surveillance and action recognition projects needing frame-level and temporal annotations"],"limitations":["Advanced object tracking (frame propagation, keyframe interpolation) available only in Team tier and above; Starter tier limited to per-frame annotation","Temporal consistency validation not automated — relies on annotator review; no built-in temporal coherence scoring","Multi-sensor fusion (LiDAR + RGB-D + video) supported but alignment/calibration must be pre-processed; Encord does not handle sensor synchronization","Video frame rate and resolution constraints not specified; large 4K/60fps videos may incur higher processing costs (pricing model unknown)"],"requires":["Team tier or above for advanced tracking features","Video files in supported formats (MP4, MOV, AVI with H.264/H.265 codec)","Pre-calibrated multi-sensor data if using sensor fusion (LiDAR-to-RGB transformation matrices required)","Keyframe specification or automatic keyframe detection enabled"],"input_types":["video files (MP4, MOV, AVI)","frame sequences (JPEG, PNG)","LiDAR point clouds (PCD, LAS format)","RGB-D depth maps (PNG 16-bit, OpenNI format)","synchronized multi-camera feeds"],"output_types":["per-frame bounding boxes with track IDs","temporal object trajectories with interpolated frames","3D bounding boxes (for LiDAR fusion)","keyframe annotations with propagated labels","video-level metadata (scene type, weather, lighting conditions)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_10","uri":"capability://automation.workflow.dataset.versioning.and.lineage.tracking","name":"dataset-versioning-and-lineage-tracking","description":"Version control system for annotated datasets with full lineage tracking from raw data through annotation to model training. Supports branching and merging of datasets, rollback to previous versions, and audit trails for all changes (annotations, corrections, metadata updates). Integrates with CI/CD pipelines to enable reproducible model training and enables comparison of model performance across dataset versions.","intents":["I want to track how my dataset evolved over time and understand which annotations changed between versions","I need to reproduce a model trained on a specific dataset version for debugging or regulatory compliance","I want to compare model performance across different dataset versions to understand the impact of annotation quality or data composition"],"best_for":["ML teams building production models requiring reproducibility and audit trails","regulated industries (healthcare, finance, autonomous vehicles) needing compliance documentation","research teams conducting ablation studies on dataset composition and annotation quality"],"limitations":["Dataset versioning supported but granularity and branching semantics not specified (unclear if version control is at item-level or job-level)","Lineage tracking mentioned but scope unknown (unclear if it includes data source, preprocessing, or only annotation changes)","Rollback capabilities not specified; unclear if reverting to previous versions is automatic or requires manual intervention","Audit trail completeness unknown; unclear if all metadata changes (annotator, timestamp, corrections) are tracked","Integration with model training pipelines mentioned but no concrete examples or API specifications"],"requires":["API access for programmatic version management (API documentation unknown)","Metadata schema for tracking lineage (format and requirements unknown)"],"input_types":["annotation jobs and results","metadata updates (corrections, re-annotations)","dataset branching/merging operations"],"output_types":["version history with timestamps and change summaries","lineage graph (data source → annotation → model training)","audit trail with annotator IDs and change details","version comparison reports (items added/removed/modified)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_11","uri":"capability://data.processing.analysis.custom.metadata.and.quality.metrics.framework","name":"custom-metadata-and-quality-metrics-framework","description":"Extensible framework for defining custom metadata fields, quality metrics, and evaluation criteria specific to domain or use case. Supports custom metadata at item-level (e.g., image source, collection date, environmental conditions) and annotation-level (e.g., annotator confidence, review status). Enables custom quality metrics beyond standard accuracy/consistency measures, allowing teams to define domain-specific quality thresholds and automated quality gates.","intents":["I want to track custom metadata (e.g., weather conditions, lighting, camera model) alongside annotations for model analysis","I need to define custom quality metrics specific to my domain (e.g., temporal consistency for video, geographic diversity for geospatial data)","I want to set up automated quality gates that flag datasets not meeting custom thresholds before model training"],"best_for":["organizations with domain-specific quality requirements beyond standard accuracy metrics","teams building datasets with rich contextual metadata for model analysis and debugging","enterprises implementing custom quality assurance workflows with automated gates"],"limitations":["Custom metadata and quality metrics mentioned but no details on definition language, schema validation, or API for metric computation","Quality gate automation not specified; unclear if gates are rule-based or ML-based","Custom metric computation may require external services; no mention of built-in metric evaluation engine","Metadata schema evolution and versioning not mentioned; unclear how to handle schema changes across dataset versions"],"requires":["Metadata schema definition (format unknown)","Custom metric definition (language/framework unknown)","Quality threshold specification (format unknown)"],"input_types":["custom metadata fields (key-value pairs, structured data)","metric computation logic (custom code or rule definitions)","quality threshold definitions"],"output_types":["annotated items with custom metadata","custom quality metric scores","quality gate pass/fail decisions","metadata-based filtering and analysis reports"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_12","uri":"capability://planning.reasoning.data.agent.driven.intelligent.curation","name":"data-agent-driven-intelligent-curation","description":"AI-powered data agents that autonomously curate datasets by analyzing data characteristics, identifying gaps, and recommending samples for annotation. Agents use embedding-based similarity, statistical analysis, and custom acquisition functions to prioritize high-value samples and suggest data collection strategies. Supports iterative refinement where agents learn from annotation results to improve future recommendations.","intents":["I want AI to automatically identify which samples in my unlabeled pool are most valuable to annotate next","I need to understand gaps in my dataset (e.g., underrepresented classes, edge cases) and get recommendations for data collection","I want to run iterative curation cycles where the AI learns from each annotation round to improve sample selection"],"best_for":["data-efficient ML teams with large unlabeled pools seeking intelligent sample prioritization","organizations running iterative model development where curation improves with each cycle","enterprises with domain expertise wanting to encode curation logic into autonomous agents"],"limitations":["Data agents available only in Team tier and above; Starter tier cannot use autonomous curation","Agent behavior and decision-making process not specified; unclear if agents use rule-based logic, statistical methods, or ML models","Iterative learning from annotation feedback not detailed; unclear how agents adapt recommendations based on model performance","Custom agent development not mentioned; unclear if teams can define custom agent logic or only use pre-built agents","Agent transparency and explainability not addressed; no mention of audit trails for agent decisions"],"requires":["Team tier or above subscription","Unlabeled data pool with embeddings or feature representations","Initial labeled dataset (100+ samples) for agent training"],"input_types":["unlabeled data samples with embeddings","model predictions and performance metrics","annotation feedback and quality scores","custom curation criteria or constraints"],"output_types":["prioritized sample recommendations with rationale","gap analysis reports (underrepresented classes, edge cases)","data collection strategy suggestions","curation performance metrics (annotation efficiency, model improvement)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_13","uri":"capability://automation.workflow.vpc.and.on.premises.deployment.with.data.isolation","name":"vpc and on-premises deployment with data isolation","description":"Encord offers VPC (Virtual Private Cloud) and on-premises deployment options for teams with strict data governance or compliance requirements. Data remains within the customer's infrastructure, and Encord provides managed services (annotation, quality assurance) with secure data access. This enables teams to use Encord's platform while maintaining control over data location and access.","intents":["I need to keep my medical imaging data on-premises for HIPAA compliance while using Encord's annotation platform","I want to deploy Encord in my VPC to ensure data never leaves my AWS account","I need to maintain data isolation for a multi-tenant SaaS application using Encord for annotation"],"best_for":["regulated industries (healthcare, finance, government) with strict data governance","organizations with data residency requirements (e.g., EU GDPR, China data localization)","enterprises with security policies prohibiting cloud data transfer"],"limitations":["VPC and on-premises deployment are add-ons (not included in base tiers)","Deployment architecture and infrastructure requirements not documented","Managed services availability in on-premises deployments not specified","Support for multi-tenancy and data isolation not documented","Operational overhead for on-premises deployment not specified (e.g., patching, scaling, backups)"],"requires":["Encord Enterprise tier (VPC/on-premises deployment)","AWS account (for VPC) or on-premises infrastructure (servers, storage, networking)","Network connectivity between Encord and customer infrastructure","Compliance expertise to configure data isolation and access controls"],"input_types":["deployment configuration (VPC or on-premises)","data location and access policies","user and role definitions"],"output_types":["isolated Encord instance","audit logs and compliance reports","data access controls"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_14","uri":"capability://data.processing.analysis.llm.evaluation.and.annotation.for.text.and.document.data","name":"llm evaluation and annotation for text and document data","description":"Encord supports annotation of text, documents, and LLM outputs for evaluation and fine-tuning. Teams can annotate text classifications, named entity recognition, question-answering pairs, and LLM response quality. The platform integrates with LLM evaluation frameworks and supports consensus-based validation of LLM outputs. LLM evaluation is available as an add-on feature.","intents":["I need to annotate 10k LLM responses to evaluate quality and identify failure modes","I want to create a dataset for fine-tuning my LLM by annotating text classifications and NER examples","I need to validate LLM outputs with multiple annotators and compute agreement metrics"],"best_for":["NLP teams building and evaluating LLMs","organizations fine-tuning LLMs on domain-specific data","projects requiring human evaluation of LLM outputs"],"limitations":["LLM evaluation is listed as an add-on (not included in base tiers)","Supported annotation types for text/documents not fully specified (NER, classification, QA mentioned but others unclear)","Integration with LLM evaluation frameworks not documented","No documented support for custom evaluation metrics or domain-specific LLM evaluation","Latency for LLM annotation workflows not specified"],"requires":["Encord account with LLM evaluation add-on","Text or document data","Annotation guidelines for LLM evaluation"],"input_types":["text documents","LLM prompts and responses","reference answers (for QA evaluation)"],"output_types":["annotated text with labels (classifications, entities, quality scores)","evaluation metrics (agreement, quality scores)","fine-tuning datasets"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_2","uri":"capability://data.processing.analysis.medical.imaging.annotation.with.dicom.nifti.support","name":"medical-imaging-annotation-with-dicom-nifti-support","description":"Specialized annotation workflows for medical imaging (DICOM, NIfTI formats) with domain-specific tools for 3D volume segmentation, multi-slice review, and radiologist-friendly interfaces. Supports ECG time-series and other medical sensor data, with compliance-ready infrastructure for healthcare deployments (on-premises and VPC options available as add-ons).","intents":["I need to annotate 3D medical imaging volumes (CT, MRI) with slice-by-slice segmentation for tumor detection models","I want radiologists to review and correct AI-assisted predictions on DICOM images with minimal friction","I need to ensure HIPAA/GDPR compliance for medical data annotation with on-premises or VPC deployment options"],"best_for":["healthcare AI teams building diagnostic imaging models (radiology, pathology, cardiology)","medical device companies requiring regulated annotation workflows with audit trails","research institutions annotating multi-center medical imaging studies with privacy constraints"],"limitations":["DICOM/NIfTI support is a paid add-on (pricing not disclosed); not included in base Starter tier","3D volume segmentation tools available but no mention of advanced features like semi-automatic segmentation or watershed algorithms","On-premises and VPC deployment options require Enterprise tier with custom MSA; no standard SLA for medical data residency or compliance certification details","ECG and time-series medical data supported but annotation workflows not specified; unclear if specialized cardiac or EEG tools are included","No mention of DICOM metadata preservation or HL7 integration for EHR systems"],"requires":["Enterprise tier subscription for on-premises/VPC deployment","DICOM/NIfTI add-on license (pricing unknown)","HIPAA Business Associate Agreement (BAA) for healthcare use (availability not confirmed in documentation)","Medical imaging expertise on annotation team or domain specialist support (available as add-on)"],"input_types":["DICOM files (CT, MRI, X-ray, ultrasound, pathology slides)","NIfTI volumes (neuroimaging, research datasets)","ECG time-series data (CSV, HL7 format support unknown)","Multi-slice image sequences (JPEG stacks with metadata)"],"output_types":["3D segmentation masks (voxel-level labels)","per-slice annotations with 3D consistency validation","radiologist reports and structured findings (if integrated with EHR)","DICOM-compliant annotation metadata with audit trails"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_3","uri":"capability://safety.moderation.label.quality.monitoring.with.error.detection","name":"label-quality-monitoring-with-error-detection","description":"Automated quality assurance system that detects label errors, outliers, and inconsistencies across annotation jobs using statistical analysis and model-based anomaly detection. Provides label exploration dashboards for root-cause analysis and supports consensus-based error flagging where multiple annotators identify problematic labels. Integrates with annotation workflows to trigger re-labeling or expert review for flagged items.","intents":["I want to automatically detect mislabeled or inconsistent annotations before they corrupt my training dataset","I need to identify which annotators are producing low-quality labels and retrain or reassign them","I want to understand the distribution of label errors across my dataset to prioritize re-annotation efforts"],"best_for":["ML teams with large annotation workforces needing quality assurance without manual review of every label","organizations building production datasets where label quality directly impacts model performance","data curation teams managing multi-source annotations (crowdsourced, in-house, vendor) with varying quality"],"limitations":["Label error detection methodology not specified (unclear if rule-based, statistical, or ML-based); no published benchmarks on detection accuracy","Outlier detection available but algorithm approach unknown; may miss domain-specific edge cases","Duplication detection limited to images only; no mention of video frame duplication or audio clip similarity detection","Quality metrics are descriptive (error rate, consistency score) but no prescriptive recommendations for remediation","Annotator performance dashboard available only in Team tier and above; Starter tier has no quality monitoring"],"requires":["Team tier or above subscription for annotator performance dashboard","Minimum 2 annotators per item for consensus-based error detection (optional but recommended)","Historical annotation data (at least 100+ labeled items) for statistical baseline establishment"],"input_types":["annotation jobs with multiple annotator submissions","label metadata (annotator ID, timestamp, confidence scores if available)","model predictions for comparison-based error detection"],"output_types":["error flags with severity scores (high/medium/low confidence)","annotator performance metrics (accuracy, consistency, speed)","label quality reports with distribution visualizations","re-annotation task lists prioritized by error severity"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_4","uri":"capability://planning.reasoning.embedding.based.data.curation.with.active.learning","name":"embedding-based-data-curation-with-active-learning","description":"Intelligent sample selection system using embedding-based similarity and custom acquisition functions to identify high-value samples for annotation. Prioritizes uncertain, novel, or outlier samples to maximize model improvement per annotation dollar spent. Supports custom acquisition functions (Enterprise tier) for domain-specific sample selection strategies, with built-in support for uncertainty sampling, diversity sampling, and query-by-committee approaches.","intents":["I want to annotate only the most informative samples from my unlabeled pool to maximize model improvement with limited annotation budget","I need to identify out-of-distribution or novel samples that my model hasn't seen before","I want to implement a custom acquisition function that balances uncertainty, diversity, and business-specific criteria for sample selection"],"best_for":["data-efficient ML teams with large unlabeled pools and constrained annotation budgets","organizations iterating rapidly on models where each annotation round must maximize performance gains","enterprises with domain-specific sample selection criteria (e.g., prioritize rare classes, geographic diversity)"],"limitations":["Custom acquisition functions available only in Enterprise tier; Team tier limited to pre-built strategies (uncertainty, diversity)","Embedding generation requires external model or Encord-provided embeddings; no details on embedding model selection or fine-tuning","Active learning data volume capped at tier limits (50k Starter, 1m Team, 10m Enterprise); exceeding requires tier upgrade","No published benchmarks on annotation reduction vs random sampling; claimed 40-60% cost reduction not independently validated","Acquisition function performance depends on model quality; poor embeddings or weak models reduce effectiveness"],"requires":["Team tier or above for active learning features","Enterprise tier for custom acquisition functions","Pre-computed embeddings for unlabeled samples (can be generated via API or external model)","Initial labeled dataset (100+ samples) to establish baseline for uncertainty/diversity scoring"],"input_types":["unlabeled data samples (images, video frames, text, sensor data)","embedding vectors (768-2048 dimensions typical)","model predictions with confidence scores (for uncertainty sampling)","custom metadata for domain-specific filtering"],"output_types":["ranked sample list prioritized by acquisition score","annotation job with selected samples pre-populated","curation metrics (coverage, diversity, uncertainty distribution)","feedback loop data for iterative acquisition function tuning"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_5","uri":"capability://automation.workflow.programmatic.annotation.pipeline.automation","name":"programmatic-annotation-pipeline-automation","description":"API and SDK-based automation for triggering labeling jobs, importing predictions, versioning datasets, and integrating annotation workflows into CI/CD pipelines. Supports programmatic job creation with custom metadata, conditional job triggering based on data characteristics, and automated result export for downstream model training. Enables end-to-end data pipeline orchestration without manual UI interaction.","intents":["I want to automatically trigger annotation jobs when new data arrives in my data lake without manual intervention","I need to version my annotated datasets and track lineage from raw data through annotation to model training","I want to integrate Encord annotation into my ML pipeline so labeled data automatically flows to my training infrastructure"],"best_for":["ML engineering teams building automated data pipelines with frequent annotation cycles","organizations with continuous data collection (sensors, user-generated content) requiring real-time annotation","enterprises integrating annotation into MLOps/CI-CD workflows for reproducible model training"],"limitations":["API documentation not provided in public materials; endpoint specifications, rate limits, and authentication patterns unknown","SDK language support not specified; unclear if Python, JavaScript, Go, or other languages are supported","CI/CD integration mentioned but no concrete examples (GitHub Actions, GitLab CI, Jenkins) or webhook patterns documented","Version control for datasets supported but granularity and branching semantics not specified","No mention of transaction semantics or rollback capabilities for failed annotation jobs"],"requires":["API key or OAuth credentials for authentication (format unknown)","SDK in supported language (language list unknown)","Programmatic access to data storage (S3, GCS, Azure Blob, or Encord-hosted storage)","Webhook endpoint or polling mechanism for job status updates"],"input_types":["job configuration (data source, annotation template, annotator assignment rules)","data references (S3 URIs, GCS paths, local file paths)","custom metadata (project ID, version tags, priority levels)","model predictions for import (COCO JSON, YOLO, custom formats)"],"output_types":["job ID and status (queued, in-progress, completed, failed)","annotated dataset export (COCO JSON, YOLO, custom formats)","lineage metadata (data source, annotation date, annotator IDs, model version)","quality metrics and error reports"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_6","uri":"capability://data.processing.analysis.model.evaluation.and.comparison.framework","name":"model-evaluation-and-comparison-framework","description":"Structured evaluation system for comparing model outputs using RLHF (Reinforcement Learning from Human Feedback), rubric-based scoring, and pairwise comparison workflows. Supports custom evaluation metrics and integrates with annotation workflows to collect human judgments on model quality. Provides model comparison dashboards to identify performance differences across model versions, datasets, or configurations.","intents":["I want to evaluate my model's predictions using human feedback to identify failure modes and improvement areas","I need to compare two model versions (e.g., v1 vs v2) using consistent rubrics to determine which is better","I want to collect RLHF data from annotators to fine-tune my model's behavior on specific tasks"],"best_for":["ML teams iterating on model quality and needing structured human feedback for evaluation","organizations building RLHF datasets for LLM fine-tuning or reinforcement learning","research teams conducting model comparison studies with rigorous evaluation protocols"],"limitations":["RLHF and rubric-based evaluation mentioned but no technical specification of evaluation frameworks or supported model types","Custom evaluation metrics available but no details on metric definition language or integration with external evaluation tools","Pairwise comparison workflows supported but no mention of ranking aggregation methods (Bradley-Terry, Elo, etc.)","Model comparison dashboards available but statistical significance testing or confidence intervals not mentioned","No integration with popular evaluation frameworks (HELM, LMSys Chatbot Arena, OpenAI evals)"],"requires":["Model predictions in supported format (COCO JSON, YOLO, or custom API payload)","Evaluation rubric definition (format and schema unknown)","Annotators trained on evaluation criteria (quality assurance process not specified)"],"input_types":["model predictions (images, text, structured outputs)","reference labels or ground truth (optional, for comparison-based evaluation)","evaluation rubrics (custom scoring criteria)","annotator feedback (ratings, rankings, free-text comments)"],"output_types":["model evaluation scores (per-sample and aggregate metrics)","model comparison reports (win rates, score distributions, statistical tests)","RLHF training data (preference pairs with human judgments)","failure mode analysis (samples where models disagree or score poorly)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_7","uri":"capability://data.processing.analysis.geospatial.and.satellite.imagery.annotation","name":"geospatial-and-satellite-imagery-annotation","description":"Specialized annotation workflows for geospatial and satellite imagery with support for large-scale orthomosaics, multi-spectral imagery, and geospatial metadata (coordinates, CRS, resolution). Integrates with GIS tools and supports annotation of land use classification, object detection in aerial imagery, and change detection across temporal image sequences. Handles high-resolution imagery (gigapixel-scale) with viewport-based annotation to manage performance.","intents":["I need to annotate satellite or aerial imagery for land use classification, crop monitoring, or urban planning models","I want to detect objects (buildings, vehicles, trees) in high-resolution orthomosaics without manual tiling","I need to track changes in geospatial features across multiple time periods (e.g., deforestation, urban expansion)"],"best_for":["geospatial AI teams building models for agriculture, urban planning, environmental monitoring, or disaster response","remote sensing companies annotating satellite or drone imagery for commercial applications","research institutions conducting large-scale geospatial analysis with multi-temporal imagery"],"limitations":["Geospatial support is a paid add-on (pricing not disclosed); not included in base Starter tier","Viewport-based annotation for gigapixel imagery may introduce edge artifacts at tile boundaries; no mention of overlap handling","Multi-spectral imagery support mentioned but no details on band selection, false-color composition, or spectral analysis tools","Geospatial metadata preservation (CRS, georeferencing) not confirmed; unclear if annotations maintain spatial accuracy","Change detection workflows not specified; unclear if temporal alignment or image registration is automated"],"requires":["Geospatial add-on license (pricing unknown)","Geospatial imagery in supported formats (GeoTIFF, COG, Sentinel-2, Landsat, or custom)","Geospatial metadata (CRS, bounding box, resolution) for proper georeferencing","GIS expertise on annotation team or domain specialist support (available as add-on)"],"input_types":["satellite imagery (Sentinel-2, Landsat, Planet, Maxar, custom)","aerial/drone imagery (orthomosaics, DEM, point clouds)","multi-spectral imagery (4+ bands)","temporal image sequences (for change detection)","geospatial vector data (shapefiles, GeoJSON for reference)"],"output_types":["pixel-level land use/land cover (LULC) classifications","object detections with geospatial coordinates","change detection masks (before/after comparison)","GeoJSON or shapefile exports with spatial accuracy metadata"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_8","uri":"capability://data.processing.analysis.document.and.html.annotation.for.structured.extraction","name":"document-and-html-annotation-for-structured-extraction","description":"Annotation workflows for unstructured documents (PDFs, scanned images, HTML) with support for text extraction, table annotation, and structured field extraction. Integrates OCR for scanned documents and supports hierarchical annotation (document-level, section-level, field-level) for training document understanding and information extraction models. Enables annotation of complex layouts with multi-column text, tables, and embedded images.","intents":["I need to annotate PDFs and scanned documents to train models for invoice, receipt, or contract information extraction","I want to extract structured data from tables and forms in documents with high accuracy","I need to annotate HTML documents for web scraping or content classification tasks"],"best_for":["document processing teams building OCR and information extraction models","financial services companies automating invoice and receipt processing","enterprises digitizing legacy documents and extracting structured data"],"limitations":["Document annotation support mentioned but no details on OCR accuracy, supported languages, or handwriting recognition","Table annotation workflows not specified; unclear if cell-level or row/column-level annotation is supported","HTML annotation support mentioned but no details on DOM structure handling or JavaScript-rendered content","No mention of document layout understanding or reading order detection","Hierarchical annotation (document/section/field) mentioned but no details on nesting depth or relationship modeling"],"requires":["Documents in supported formats (PDF, PNG, JPEG, HTML)","OCR engine (Encord-provided or external) for scanned documents","Document schema or field definitions for structured extraction"],"input_types":["PDF files (native and scanned)","image files (JPEG, PNG of document pages)","HTML documents","document metadata (document type, language, source)"],"output_types":["text annotations with bounding boxes","table annotations (cell-level or row/column-level)","structured field extractions (key-value pairs)","document-level classifications (document type, language, quality)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__cap_9","uri":"capability://automation.workflow.annotator.workforce.management.and.performance.tracking","name":"annotator-workforce-management-and-performance-tracking","description":"Workforce management system for organizing annotation teams, assigning tasks based on skill level and specialization, and tracking individual annotator performance metrics (accuracy, speed, consistency). Supports in-house annotators, crowdsourced workers, and domain specialist vendors with role-based access control and quality-based task routing. Provides performance dashboards with annotator-level metrics and recommendations for training or reassignment.","intents":["I want to assign annotation tasks to the right annotators based on their expertise and past performance","I need to track which annotators are producing high-quality labels and which need retraining","I want to manage a mixed workforce (in-house, crowdsourced, vendors) with different skill levels and specializations"],"best_for":["organizations with large annotation teams (10+ annotators) needing performance management","companies using mixed annotation sources (in-house, crowdsourced, vendors) with varying quality","enterprises requiring skill-based task assignment and quality-based routing"],"limitations":["Annotator performance dashboard available only in Team tier and above; Starter tier has no workforce management","Performance metrics available (accuracy, speed, consistency) but methodology for accuracy calculation not specified (requires ground truth or consensus)","Task routing based on performance not automated; no mention of intelligent assignment algorithms","Crowdsourced worker management not detailed; unclear if Encord provides worker pool or requires external platforms (Mechanical Turk, Upwork)","Vendor management workflows not specified; no details on SLA tracking or quality guarantees"],"requires":["Team tier or above subscription for performance tracking","Minimum 2+ annotators per task for consensus-based accuracy calculation","Historical annotation data (100+ tasks per annotator) for reliable performance metrics"],"input_types":["annotator profiles (name, expertise, language, specialization)","task assignments (data, annotation template, deadline)","annotation submissions (labels, metadata, timestamps)","ground truth or consensus labels for accuracy calculation"],"output_types":["annotator performance metrics (accuracy, speed, consistency, error rate)","task assignment recommendations (skill-based routing)","performance dashboards with trends and anomalies","training recommendations or reassignment suggestions"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"encord__headline","uri":"capability://data.processing.analysis.ai.data.platform.for.automated.annotation.and.quality.management","name":"ai data platform for automated annotation and quality management","description":"Encord is an AI data platform that automates the annotation, quality management, and curation of computer vision training data, making it ideal for teams needing efficient data handling and DICOM support for medical imaging.","intents":["best AI data platform","AI data platform for computer vision","automated annotation tools for AI","quality management solutions for training data","DICOM support for AI training datasets"],"best_for":["AI teams in computer vision","medical imaging applications"],"limitations":["data volume constraints based on pricing tiers"],"requires":["input data quality"],"input_types":["images","videos","DICOM files"],"output_types":["annotated datasets","curated training data"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Team tier or above subscription","Pre-trained model predictions in supported format (COCO JSON, YOLO, or custom via API)","Minimum 2 annotators for consensus workflows (optional but recommended for quality)","Team tier or above for advanced tracking features","Video files in supported formats (MP4, MOV, AVI with H.264/H.265 codec)","Pre-calibrated multi-sensor data if using sensor fusion (LiDAR-to-RGB transformation matrices required)","Keyframe specification or automatic keyframe detection enabled","API access for programmatic version management (API documentation unknown)","Metadata schema for tracking lineage (format and requirements unknown)","Metadata schema definition (format unknown)"],"failure_modes":["Model-assisted labeling available only in Team tier and above; Starter tier cannot import model predictions","Active learning data volume capped at tier limits (50k Starter, 1m Team, 10m Enterprise) — exceeding requires upgrade","SAM2 integration is pre-built; custom model integration requires API-based prediction import with no native framework abstraction","Consensus workflows add annotation latency; no SLA specified for turnaround time with multi-annotator validation","Advanced object tracking (frame propagation, keyframe interpolation) available only in Team tier and above; Starter tier limited to per-frame annotation","Temporal consistency validation not automated — relies on annotator review; no built-in temporal coherence scoring","Multi-sensor fusion (LiDAR + RGB-D + video) supported but alignment/calibration must be pre-processed; Encord does not handle sensor synchronization","Video frame rate and resolution constraints not specified; large 4K/60fps videos may incur higher processing costs (pricing model unknown)","Dataset versioning supported but granularity and branching semantics not specified (unclear if version control is at item-level or job-level)","Lineage tracking mentioned but scope unknown (unclear if it includes data source, preprocessing, or only annotation changes)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:21.548Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=encord","compare_url":"https://unfragile.ai/compare?artifact=encord"}},"signature":"U7QT5ESA2sdKB0aeEm4coDrCkF8xMoiSmu/rQJlhrUo71KAIeeto4/AEse1aAGQzsZiZQOYLKwBVfrX+Fz90Dg==","signedAt":"2026-06-22T06:51:51.098Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/encord","artifact":"https://unfragile.ai/encord","verify":"https://unfragile.ai/api/v1/verify?slug=encord","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}