Qualia extracts every dimension of metadata in a single AI call, searches it with built-in explainability, and costs 10–30x less than per-feature alternatives. A two-phase map-reduce engine — built, deployed, and processing real content.
Cloud APIs can tag objects and transcribe speech. But they charge per feature, break video into arbitrary chunks, lose narrative context, and store your metadata in their systems. The core problems remain.
Cloud APIs label individual frames — "person," "building," "car." But mood, tension, narrative arc, and thematic connections require understanding the whole scene in context, not tagging isolated objects.
Video AI vendors charge per feature, per minute. Face detection, OCR, transcription, tagging — each a separate bill. For large libraries, the math breaks.
Content attributes in one system. YouTube retention in another. Facebook engagement in another. No way to ask: "What content attributes drive audience behavior?"
A trending topic has a 48-hour window. If finding the right clip takes a day of manual review, the moment has passed before you find it.
Most platforms store your intelligence in their systems. Switch vendors and start from scratch. Your most valuable extracted data isn't truly yours.
Marketing knows which videos get views. Production knows what's in each episode. Nobody knows which specific content attributes actually drive the numbers.
A two-phase map-reduce architecture. Cheap parallel extraction, then expensive global reasoning.
Videos are probed for metadata, then segmented at natural scene boundaries. Shot detection uses a triple intersection — visual cuts, audio silence, and black frames — to find real transitions. The result: segments that respect narrative flow, giving downstream AI complete context for every scene. Better input quality means better metadata, at no additional cost.
Qualia's architecture is designed to correlate content metadata with audience data across platforms. Each data source you connect unlocks questions no single system can answer.
Different roles ask different questions of the same content. Qualia gives each team the view they need.
Natural language search across your full archive. "Show me scenes where a historian explains the Roman Empire near ancient ruins" returns timestamped results with full context — who's speaking, what's on screen, and exactly why each result matched.
Correlate scene-level attributes (themes, talent, mood, narrative structure) with retention curves, engagement, and revenue. Make programming decisions from evidence, not intuition.
Auto-detected highlights become short-form candidates. Cross-platform analytics show which clip styles perform where. When a topic trends, search your archive and have clip candidates in minutes, not days.
Scene-level metadata means ad breaks align with natural pauses. Content mood and theme data flow into ad decisioning — the right ad after the right scene, reducing viewer drop-off and improving yield.
Automated scanning for content warnings, brand mentions, sensitive material. New compliance rule? Update a prompt — no code changes, no model retraining. Re-scan the back catalog at a fraction of manual review cost.
Cost structure, architecture, and data compounding create a platform that gets more valuable with every video processed.
One multimodal AI call extracts faces, text, transcription, themes, mood, and tension simultaneously. Competitors charge per feature. We extract everything together — 10-30x cheaper per minute.
Phase 1: cheap, parallel extraction on shot-aligned chunks. Phase 2: expensive global reasoning with a 2M-token context window. Extraction scales without cost scaling linearly.
Content metadata alone is a commodity. The architecture correlates it with YouTube retention, Facebook engagement, and ad revenue — each new data source makes existing analysis more valuable. Network effects, not features.
Need commercial detection? Brand mentions? A new compliance field? Change a prompt. No retraining, no code changes, no vendor negotiation. New intelligence deploys in minutes.
Scene boundaries at natural transitions, not arbitrary time cuts. Higher-quality metadata from the same models because each chunk contains complete narrative context.
Runs on your cloud. Metadata in your database. The extraction engine and search pipeline are decoupled — swap the underlying AI model or storage layer without rebuilding. Your intelligence stays yours.
One multimodal call per scene. A map-reduce pipeline that scales horizontally. Search that explains its own results. The architecture is proven — the question is how much of your archive you want to unlock.