Multimodal video perception
Frames, speech, audio cues, subtitles and metadata aligned into a shared stream of observations that downstream models can reason over.
Independent R&D lab for real-time video intelligence and media processing.
Building systems that understand video as it happens.
Veloryq develops technologies for live and long-form media, from multimodal signal fusion and temporal scene understanding to semantic retrieval, event detection, highlight generation and live metadata.
Search the video intelligence stack by concept.
Local concept index. No model call. Matches map to the video intelligence timeline.
Core research stack
Our work focuses on turning continuous video streams into structured, queryable state with low enough latency to support live media workflows.
Temporal scene and event understanding
Tracking scenes, events, entities, relationships and narrative changes across time instead of treating frames or transcript segments in isolation.
Semantic video indexing and retrieval
Time-addressable representations that let systems retrieve the exact moment, event or context a query refers to across live streams and large catalogs.
Live media automation
Incremental outputs for contextual metadata, highlight candidates, clipping, editorial triggers and personalized experiences while a stream is still running.
Systems in development
Focused R&D systems that test how real-time video understanding can become reusable media infrastructure rather than a one-off demo.
Live Semantic Timeline
in development
- live video
- temporal state
- multimodal fusion
- event understanding
Building a continuously updated, time-addressable semantic timeline that represents what is happening in a stream as scenes, events and entities evolve.
Moment Retrieval Engine
in development
- semantic search
- timestamp retrieval
- scene context
- catalog intelligence
Retrieving precise moments from long-form and live content by meaning, event and context rather than transcript wording alone.
Real-Time Highlight and Metadata Pipeline
in development
- event detection
- highlight candidates
- live metadata
- editorial automation
Detecting editorially relevant moments as they emerge and producing structured metadata, highlight candidates and downstream media actions with live-oriented latency.
Video should be queryable while it is still happening.
Video understanding is a temporal systems problem. Frames, words, sounds and metadata only become useful when they are aligned, connected and continuously updated as the stream evolves.
Veloryq develops the intelligence layer that turns continuous media into structured state that can be searched, reasoned over and acted on in real time.
Active R&D
Veloryq is developing the core components of a real-time video intelligence stack. Current work spans multimodal ingestion, temporal state, timestamp-level retrieval and live outputs designed to become reusable media infrastructure.
Build the primitives. Measure the system. Productize what works.
Working on live video, OTT, broadcast or media infrastructure?
Technical discussions, research collaborations and early product partnerships around video intelligence and media processing are welcome.
hello@veloryq.xyz