Your video already
wrote the title.
Upload a clip. Get ranked titles, descriptions, and hashtags — grounded in what's actually said and shown.
What ClipContext outputs
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Upload a short clip
Drop a 30 second to 2 minute video. ClipContext will validate it, extract speech, sample sparse frames, and prepare grounded publishing candidates.
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Supported formats: MP4, MOV, WEBM
Features
Built around the backend artifacts.
The interface reflects the actual flow: validation, transcription, sparse frame selection, VideoContext construction, and grounded content generation.
Local Speech Context
Extracts audio and transcription before generation so the spoken message anchors the output.
Sparse Visual Evidence
Scans frames locally, selects diverse temporal windows, and avoids sending redundant video context downstream.
Canonical VideoContext
Fuses transcript, visuals, visible text, key moments, uncertainty, style, and audience signals into one schema.
Candidate Sets
Generates 10 titles, 10 descriptions, and 10 hashtag sets from verified context and platform syntax, then ranks and surfaces the top 5 of each.
Pipeline
The pipeline the backend is actually running.
Each stage maps to a concrete backend artifact, from local transcription through sparse visual windows to schema-validated generated content.
Validate
Short video
Accept a creator video, validate the file, and prepare deterministic output folders for cached artifacts.
Transcribe
Audio
Extract audio locally and build the transcript used as the factual speech source for later generation.
Select Windows
Sparse frames
Scan at 1 FPS, score local quality and diversity, then select representative 5-second visual windows.
Build Context
VideoContext
Fuse transcript and visual analysis into topic, core message, visible text, key moments and uncertainty fields.
Generate Sets
Publish assets
Use platform syntax to produce exactly 10 titles, 10 descriptions and 10 hashtag sets.
Technology
Why the output stays grounded.
VideoContext is the factual contract. Trend syntax shapes the writing, but it cannot invent facts that are not in the analyzed video.
Deterministic artifacts
The backend stores transcription, visual timeline, VideoContext, trend syntax, generated content and audit outputs by video ID.
Sparse multimodal budget
The pipeline reduces redundant frame inference by selecting representative temporal windows before paid model calls.
Schema-validated generation
Generated output must pass Pydantic validation: 10 ordered titles, 10 descriptions and 10 hashtag arrays.
Platform syntax
Trend and creator analysis inform structure and vocabulary, while VideoContext remains the factual source of truth.
Grounding guardrails
The content prompt prevents unsupported people, places, claims, brands, objects and events.
Provider flexibility
Visual understanding and text generation can move between providers while preserving the canonical context contract.