VideoVector

VideoVector takes large video, audio, and image libraries and turns them into structured, timestamped media intelligence. It’s a fit for teams who need more than keyword search, like archive discovery, evidence-based review, licensing workflows, or grounded “answer with sources” retrieval.

Key capabilities include:

• Structured metadata + evidence. Output includes time-stamped, asset-level fields designed for downstream pipelines.
• Multimodal embeddings. Supports search across visual, audio, and (where provided) text-like signals embedded into retrieval surfaces.
• Schema you define. Teams align extraction fields, taxonomy, and JSON output to their domain and business rules.
• Evidence-grounded search. Results stay traceable to the source segment and extracted fields, not just similarity scores.
• Multimodal RAG-style workflows. Uses VideoRAG, AudioRAG, and ImageRAG approaches for retrieval that includes visual references and extracted context.
• Flexible segmentation. Splits video into timestamped units using narrative-aware instructions, adaptive signal detection, or fixed interval options.
• Cloud exports + webhooks. Connects to cloud storage (GCP, AWS, Azure) and delivers results to downstream systems through webhook notifications and exported JSON.