Featured Client
Juniu Xiansheng
Rebuilding News Video Editing: Faster Visual Matching and Precise Retrieval
Targeting high-frequency news and financial video production, this workflow builds an AI semantic retrieval and visual planning system for massive video libraries, helping editing teams quickly locate usable shots among tens of thousands of assets.
Precise matching of assets and shots, significantly shortening the delivery time of initial editing drafts.
Hours
Fast turnaround from topic selection to final video for financial news
16%+
Increase in playback data with unified shot rhythm and packaging standards
More Diverse
Dynamically generating storyboards, assets, and packaging without relying on fixed templates
Why is high-efficiency, scaled news video editing so difficult?
Massive Asset Libraries with Weak Shot-Level Retrieval
News and financial videos accumulate large amounts of historical footage, but most are only tagged at the file, column, or date level. Editors spend excessive time manually scrubbing timelines and relying on experience to find usable shots.
Lack of Semantic Connection Between Scripts and Visuals
A segment of voiceover might correspond to various visual expressions involving people, places, industries, events, or data charts. The traditional workflow relies on editors manually interpreting the script and searching the library item by item, making it hard to steadily reuse historical assets.
High Cost of Locating Original Shots
Even when a relevant finished video is found, editors often need to return to the original footage to locate clean shots, text-free scenes, editable clips, or repackageable visuals. This process is highly dependent on manual experience.
High-Frequency Publishing Demands Rapid Response
News and financial content have obvious timing windows. Any delay across topic selection, scripting, visual gathering, or editing affects the final publication time and content value.
From Asset Understanding to Visual Planning: Bridging the Crucial Links of News Editing
The system does not replace the editor's final judgment; instead, it turns the asset library into a comprehensible, searchable, and reusable visual asset pool, generating shot recommendations and visual plans based on script semantics.
Semantic Parsing of Video Assets & Shot Assetization
Segmenting historical video assets into shots, recognizing visuals, extracting tags for people/places/events/industries, and building a searchable shot-level asset index.
Precise Shot Retrieval Based on Script Semantics
The system reads scripts or paragraph voiceovers, identifies the core semantics of each sentence—including people, companies, industries, events, scenes, and emotional tones—and retrieves matching shots from the asset library.
Visual Planning & Editing Assistance Generation
Based on the script's rhythm and content structure, generating visual suggestions, shot sequences, alternative assets, and supplementary visual directions for each voiceover segment, allowing editors to quickly review and adjust.
Boosting Efficiency While Retaining Editing Judgment
Traceable Shot Origins
Every recommended shot must include its source file, timecode, retrieval reasoning, and matching tags, allowing editors to quickly review and confirm.
Explainable Semantic Matching
The system not only provides the asset result but also explains why the shot fits the current script segment (e.g., related to a person, industry, scene, emotion, or event).
Manual Review & Version Correction
Editors can accept, replace, delete, or rearrange recommended results. The system records manual choices to continuously optimize subsequent retrieval and visual planning.
Asset Compliance & Copyright Boundaries
Identifying asset sources, licensing scopes, and usable scenarios to prevent the misuse of unpublishable visuals in news video production.
Ideal for teams with massive video assets and high-frequency content production needs
News Media & Financial Content Teams
Accelerating video response times for trending topics and reducing the manual pressure of finding assets and filling visuals.
Large-Scale Video Asset Management Teams
Transforming historical video assets from mere 'stored files' into 'searchable, reusable, and recommendable' shot assets.
MCNs & Enterprise Content Hubs
Ideal for teams needing to continuously produce short videos, explainer videos, news clips, and feature videos around the same themes.
