{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_cognitivemill","slug":"cognitivemill","name":"Cognitivemill","type":"product","url":"https://cognitivemill.com","page_url":"https://unfragile.ai/cognitivemill","categories":["video-generation"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_cognitivemill__cap_0","uri":"capability://image.visual.semantic.video.content.analysis.with.cognitive.computing","name":"semantic video content analysis with cognitive computing","description":"Analyzes video streams using cognitive computing models that extract semantic meaning beyond frame-level object detection, identifying narrative elements, emotional tone, scene composition, and contextual relationships within media content. The platform processes video through a multi-stage pipeline that combines computer vision with natural language understanding to generate rich metadata describing what happens in video, why it matters, and how it relates to media industry taxonomies and workflows.","intents":["I need to automatically understand what's happening in thousands of hours of video content without manual review","I want to extract structured metadata from video that goes beyond 'person detected' to include narrative context and emotional beats","I need to categorize and tag video content according to media industry standards for archival and discovery"],"best_for":["enterprise broadcasters managing large video libraries","media production companies needing automated content understanding","streaming platforms requiring intelligent content classification"],"limitations":["cognitive computing models are computationally expensive, resulting in higher per-video processing costs than simple object detection","accuracy of semantic understanding varies significantly based on video quality, lighting, and production style","no real-time processing capability — designed for batch or near-batch analysis of archived content"],"requires":["video files in standard formats (MP4, MOV, MXF preferred)","cloud connectivity and API credentials for Cognitive Mill platform","enterprise subscription tier for high-volume processing"],"input_types":["video files","video streams","metadata about video source and context"],"output_types":["structured metadata JSON","scene descriptions","entity and relationship graphs","content classification tags"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_1","uri":"capability://image.visual.automated.scene.segmentation.and.shot.detection","name":"automated scene segmentation and shot detection","description":"Automatically identifies scene boundaries, shot transitions, and structural segments within video content by analyzing visual discontinuities, audio cues, and temporal patterns. The system uses frame-by-frame analysis combined with temporal coherence models to detect cuts, dissolves, fades, and other editing patterns, then groups frames into semantically meaningful scenes for downstream processing and metadata generation.","intents":["I need to break down long-form video into logical scenes for easier browsing and indexing","I want to identify all shot transitions and editing patterns in video automatically","I need to create chapter markers or segment boundaries for video content without manual editing"],"best_for":["broadcast and streaming media companies","video archivists managing large content libraries","post-production workflows requiring automated scene detection"],"limitations":["detection accuracy depends on video production quality and editing style — may struggle with artistic transitions or low-contrast scenes","requires minimum video resolution of 720p for reliable detection","does not understand semantic meaning of scenes, only visual/temporal boundaries"],"requires":["video input with clear visual or audio transitions","minimum 720p resolution recommended","API access to Cognitive Mill platform"],"input_types":["video files","video streams"],"output_types":["scene boundary timestamps","shot transition metadata","segmentation timecodes","structured scene hierarchy"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_2","uri":"capability://image.visual.entity.extraction.and.relationship.mapping.from.video","name":"entity extraction and relationship mapping from video","description":"Identifies and extracts named entities (people, locations, organizations, objects) from video content and maps relationships between them across time and scenes. The system uses face recognition, location identification, and object tracking combined with temporal reasoning to build entity graphs showing who appears with whom, where events occur, and how entities relate to narrative elements throughout the video.","intents":["I need to know which people, places, and things appear in my video content for searchability","I want to track character appearances and relationships throughout a film or series","I need to automatically generate cast lists, location credits, and product placement metadata"],"best_for":["film and television production companies","streaming platforms needing rich content metadata","media rights management and licensing organizations"],"limitations":["face recognition accuracy varies with lighting, angles, and occlusion — may require manual correction for edge cases","location identification relies on visual landmarks and may fail in generic or unfamiliar settings","relationship inference is probabilistic and may produce false positives in crowded scenes"],"requires":["video with clear visibility of entities (faces, objects, locations)","optional: reference database of known entities for improved matching","API credentials and enterprise subscription"],"input_types":["video files","optional entity reference databases"],"output_types":["entity lists with timestamps","relationship graphs","entity co-occurrence matrices","structured cast and location metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_3","uri":"capability://data.processing.analysis.content.classification.and.tagging.with.media.industry.taxonomies","name":"content classification and tagging with media industry taxonomies","description":"Automatically classifies video content against media industry-standard taxonomies and ontologies, assigning tags for genre, content type, audience rating, themes, and other metadata relevant to broadcast and streaming workflows. The system uses the extracted semantic understanding and entity data to match content against predefined classification schemes, enabling consistent metadata across large content libraries.","intents":["I need to automatically tag content with genre, rating, and content warnings for compliance and discovery","I want to classify video against our internal taxonomy without manual review","I need to generate metadata that matches broadcast standards and streaming platform requirements"],"best_for":["broadcasters and streaming platforms with large content libraries","media companies needing consistent metadata across multiple systems","organizations managing content for multiple territories with different classification standards"],"limitations":["classification accuracy depends on how well video content aligns with training taxonomy","does not handle ambiguous or borderline content well — may require human review for edge cases","custom taxonomies require retraining or fine-tuning, not available through standard API"],"requires":["video content with clear genre and content characteristics","access to media industry standard taxonomies (EIDR, ISAN, etc.)","enterprise subscription with taxonomy customization support"],"input_types":["video files","extracted metadata from prior analysis stages"],"output_types":["classification tags","confidence scores per classification","structured metadata conforming to broadcast standards","content rating and warning metadata"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_4","uri":"capability://automation.workflow.batch.video.processing.with.cloud.infrastructure","name":"batch video processing with cloud infrastructure","description":"Processes large volumes of video content asynchronously through cloud-based infrastructure, distributing analysis workloads across multiple processing nodes and managing job queuing, progress tracking, and result aggregation. The platform abstracts away infrastructure complexity, automatically scaling compute resources based on queue depth and providing APIs for job submission, status monitoring, and result retrieval.","intents":["I need to process thousands of hours of video without managing my own compute infrastructure","I want to submit a batch of videos and retrieve results when processing completes","I need to monitor processing progress and handle failures gracefully across large jobs"],"best_for":["enterprise media companies with large content libraries","organizations without in-house video processing infrastructure","teams needing to process video at scale without operational overhead"],"limitations":["batch processing introduces latency — typical turnaround is hours to days depending on queue depth and video length","no real-time or streaming processing capability","pricing scales with video volume, making per-video costs significant for large libraries","limited visibility into processing pipeline — no granular control over resource allocation"],"requires":["video files in supported formats (MP4, MOV, MXF)","cloud connectivity and API credentials","enterprise subscription tier","sufficient account credits for processing volume"],"input_types":["video files","batch job specifications","processing parameters and configuration"],"output_types":["job status and progress tracking","processing results and metadata","error logs and failure reports","result download URLs or API access"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_5","uri":"capability://tool.use.integration.rest.api.integration.with.media.workflow.systems","name":"rest api integration with media workflow systems","description":"Exposes video analysis capabilities through REST APIs that integrate with existing media production and asset management systems, enabling programmatic submission of videos, retrieval of results, and incorporation of Cognitive Mill analysis into downstream workflows. The API supports standard HTTP patterns for job submission, polling, and webhook callbacks for asynchronous result notification.","intents":["I need to integrate video analysis into our existing media asset management system","I want to automatically trigger analysis when new content is uploaded to our system","I need to retrieve analysis results and incorporate them into our metadata database"],"best_for":["media companies with existing workflow systems","development teams building custom media applications","organizations needing to integrate Cognitive Mill with third-party tools"],"limitations":["REST API introduces network latency for each request-response cycle","no native SDKs for languages beyond common web frameworks — requires manual HTTP client implementation","rate limiting may apply to API calls, requiring careful job scheduling for high-volume processing","webhook delivery is not guaranteed — requires polling fallback for critical workflows"],"requires":["API key and authentication credentials","HTTP client library in target language","network connectivity to Cognitive Mill cloud platform","understanding of asynchronous job patterns"],"input_types":["video file URLs or uploads","job configuration parameters","webhook callback URLs"],"output_types":["job IDs and status responses","analysis results in JSON format","webhook notifications with result data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_6","uri":"capability://data.processing.analysis.media.specific.metadata.standardization.and.export","name":"media-specific metadata standardization and export","description":"Exports analysis results in media industry-standard metadata formats including EIDR, ISAN, and broadcast metadata standards, ensuring that generated metadata can be directly consumed by downstream systems without custom transformation. The system maps internal analysis results to standard schemas and provides export options for multiple formats and destinations.","intents":["I need to export metadata in formats that my broadcast system and streaming platforms understand","I want to ensure metadata consistency across multiple distribution channels","I need to generate EIDR/ISAN identifiers and metadata for content licensing"],"best_for":["broadcasters and streaming platforms","media companies managing content across multiple distribution channels","organizations needing to comply with industry metadata standards"],"limitations":["export formats are limited to predefined standards — custom metadata schemas require manual mapping","some broadcast standards require human review or validation before use","EIDR/ISAN identifier generation may require external registration and validation"],"requires":["completed video analysis results","target metadata format specification","optional: EIDR/ISAN registration credentials for identifier generation"],"input_types":["analysis results from prior processing stages","export format specifications"],"output_types":["EIDR-formatted metadata","ISAN-formatted metadata","broadcast metadata XML/JSON","streaming platform metadata formats"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_cognitivemill__cap_7","uri":"capability://search.retrieval.content.search.and.discovery.across.video.libraries","name":"content search and discovery across video libraries","description":"Enables semantic search across video libraries using extracted metadata and analysis results, allowing users to find content based on narrative elements, entities, themes, and other semantic properties rather than just filename or manual tags. The search system indexes analysis results and provides full-text and semantic query capabilities against the extracted metadata.","intents":["I need to find all videos featuring a specific actor or location across my library","I want to search for videos by narrative theme or emotional tone","I need to discover content that matches specific metadata criteria without manual browsing"],"best_for":["media companies with large content libraries","streaming platforms needing content discovery features","broadcast organizations managing archives"],"limitations":["search accuracy depends on quality of underlying analysis — errors in entity extraction or classification propagate to search results","semantic search requires sufficient metadata density — sparse or incomplete analysis reduces search effectiveness","search performance may degrade with very large libraries (100k+ videos) without proper indexing"],"requires":["completed analysis of video content","indexed metadata in search system","query interface (API or UI)"],"input_types":["search queries (text-based or structured)","filter criteria and metadata constraints"],"output_types":["ranked search results with relevance scores","metadata snippets and preview information","result counts and faceted filtering options"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["video files in standard formats (MP4, MOV, MXF preferred)","cloud connectivity and API credentials for Cognitive Mill platform","enterprise subscription tier for high-volume processing","video input with clear visual or audio transitions","minimum 720p resolution recommended","API access to Cognitive Mill platform","video with clear visibility of entities (faces, objects, locations)","optional: reference database of known entities for improved matching","API credentials and enterprise subscription","video content with clear genre and content characteristics"],"failure_modes":["cognitive computing models are computationally expensive, resulting in higher per-video processing costs than simple object detection","accuracy of semantic understanding varies significantly based on video quality, lighting, and production style","no real-time processing capability — designed for batch or near-batch analysis of archived content","detection accuracy depends on video production quality and editing style — may struggle with artistic transitions or low-contrast scenes","requires minimum video resolution of 720p for reliable detection","does not understand semantic meaning of scenes, only visual/temporal boundaries","face recognition accuracy varies with lighting, angles, and occlusion — may require manual correction for edge cases","location identification relies on visual landmarks and may fail in generic or unfamiliar settings","relationship inference is probabilistic and may produce false positives in crowded scenes","classification accuracy depends on how well video content aligns with training taxonomy","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:29.717Z","last_scraped_at":"2026-04-05T13:23:42.561Z","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=cognitivemill","compare_url":"https://unfragile.ai/compare?artifact=cognitivemill"}},"signature":"4UI3lLyahi2wsUhDP/QnCwwUtlPyNekxRWlZTkvXy7rYRME81lU4tEvTudyZLY560fltOQwXyM6tZqkWBcPdAQ==","signedAt":"2026-06-23T08:18:27.091Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/cognitivemill","artifact":"https://unfragile.ai/cognitivemill","verify":"https://unfragile.ai/api/v1/verify?slug=cognitivemill","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"}}