Key Capabilities
Deep Video Understanding
Analyze video content at the frame, scene, and narrative level to extract actions, objects, text overlays, and semantic themes automatically
Audio and Speech Intelligence
Transcribe, classify, and search audio content including speech, music, sound effects, and ambient audio with speaker identification
Multimedia Knowledge Graph
Build connected intelligence across video libraries by linking entities, topics, and events across thousands of hours of content
How It Works
Media organizations sit on vast libraries of video, audio, and multimedia content that remain largely untapped. Decades of broadcast footage, production rushes, podcast archives, and multimedia assets contain immense value, but without intelligent indexing, that value is locked behind hours of linear playback and manual tagging. Mixpeek's media intelligence platform transforms raw multimedia into searchable, structured, and connected knowledge. Feature extractors process video at multiple levels of granularity: frame-level object detection identifies every visible element, scene-level analysis captures narrative structure and visual themes, and sequence-level understanding detects events, actions, and dramatic arcs. Audio extractors simultaneously process speech (transcription and speaker identification), music (genre, mood, tempo), and sound effects (environmental audio classification). All extracted intelligence feeds into a unified index organized by collections and namespaces. Collections map to your content taxonomy: by show, season, genre, or production. Namespaces provide data isolation for multi-tenant deployments or regional content separation. Retrievers power the search layer, combining semantic understanding with structured filters so editors, producers, and licensing teams find exactly the content they need. A query like 'aerial shots of European cities at sunset with orchestral background music' returns precise timestamps across your entire library. Beyond search, the multimedia knowledge graph connects entities, topics, and events across your content. The same person appearing in a documentary, a news broadcast, and a podcast interview is linked automatically. Topic threads weave across shows and seasons. Event timelines emerge from distributed references across your archive. This connected intelligence powers content recommendation engines, editorial research tools, and licensing discovery workflows that were previously impossible at scale.
Benefits
Make decades of archived footage searchable in weeks
80% reduction in editorial research and content discovery time
Automated metadata generation replacing manual tagging workflows
Cross-content entity linking reveals hidden connections and story threads
License-ready content discovery for footage sales and syndication
Why Mixpeek
Unified multimodal analysis processes video, audio, and text together rather than in separate pipelines. Mixpeek builds a connected knowledge layer across your entire library, linking entities and topics across content boundaries in a way that siloed tools cannot achieve
Frequently Asked Questions
How long does it take to process and index a large video archive?
Mixpeek processes video at 5-10x real-time speed, meaning a 1-hour video is fully analyzed in 6-12 minutes. For large archive ingestion, processing is parallelized across multiple workers. A 10,000-hour library can be fully indexed in approximately 2-3 weeks with standard infrastructure, or faster with dedicated processing capacity. Incremental ingestion handles new content as it is produced.
What level of granularity does video analysis provide?
Mixpeek analyzes video at three levels: frame-level (individual objects, faces, text overlays, visual elements), scene-level (narrative segments, visual themes, camera angles, transitions), and sequence-level (events, actions, dramatic arcs, topic segments). Each level generates structured metadata with timestamps, enabling search and retrieval at whatever granularity your workflow requires.
How does speaker identification work in audio content?
Mixpeek segments audio by speaker and generates speaker embeddings that can be matched across content. For known speakers, provide labeled reference audio to enable identification by name. For unknown speakers, the system clusters by voice signature and assigns consistent IDs. Speaker segments include timestamps, enabling search for specific speakers across your entire audio and video library.
Can Mixpeek process podcast and audio-only content?
Yes. Audio content is a first-class input alongside video. Mixpeek processes podcasts, radio broadcasts, audiobooks, and music files with speech transcription, speaker diarization, topic segmentation, music classification, and sound effect detection. Audio content is indexed alongside video in the same searchable library, enabling cross-format discovery.
How does the multimedia knowledge graph work?
As Mixpeek processes content, it extracts named entities (people, organizations, locations, events) and links them across your library. When the same person appears in multiple videos, or the same event is referenced in different programs, these connections are captured automatically. The knowledge graph supports queries like 'all content featuring person X' or 'all references to event Y across our archive' without requiring manual tagging.
What video and audio formats are supported?
Video: MP4, MOV, AVI, MKV, WebM, FLV, WMV, and ProRes. Audio: MP3, WAV, AAC, FLAC, OGG, and M4A. Content can be ingested from S3, GCS, Azure Blob Storage, CDN URLs, or via direct API upload. Live stream integration is available via HLS and RTMP for real-time processing use cases.
Can Mixpeek detect and extract on-screen text and graphics?
Yes. OCR extraction captures all on-screen text including titles, lower thirds, chyrons, subtitles, watermarks, and graphics overlays. Extracted text is timestamped and searchable alongside other metadata. This is particularly valuable for news content, sports broadcasts, and any video with informational graphics.
How does semantic video search differ from traditional metadata search?
Traditional search requires content to be manually tagged with keywords and only finds exact matches. Mixpeek semantic search understands meaning, so a query for 'peaceful mountain landscape' finds relevant footage even when no one tagged it with those exact words. The system matches visual content, audio atmosphere, and contextual cues to the search intent, dramatically improving recall for editorial and licensing workflows.
Can we use Mixpeek to power content recommendations?
Yes. The structured metadata, entity relationships, and semantic embeddings generated by Mixpeek feed directly into recommendation engines. You can build content-based recommendations (similar visual style, related topics, same entities), behavioral recommendations (users who watched X also watched Y using Mixpeek signals), or editorial recommendations (programmatic content grouping for themed collections).
What is the pricing model for media intelligence?
Pricing is based on total media hours processed and stored. Standard plans cover typical production workflows of 100-1,000 hours per month. Enterprise plans support broadcasters and streaming platforms processing 10,000+ hours monthly with dedicated infrastructure, custom model training, and premium support. All plans include semantic search, the knowledge graph, and API access. Contact us for volume pricing based on your library size and ingestion rate.