Enabling existing media content to be searched using natural language
Our Metadata Extraction and Enrichment Platform introduces a game-changing approach to managing video and image data. Instead of storing footage as a mass of unstructured files, we equip these assets with detailed, AI-generated metadata that captures everything from the objects within a scene to subtle behavioral cues. By parsing video streams or archived material, our system pinpoints critical information—such as the presence of vehicles or individuals wearing certain colours—and stores it in a searchable database that is easy to navigate using natural-language queries.
This enriched repository becomes invaluable for organizations like law enforcement agencies, where rapid identification of a suspect’s clothing or vehicle can significantly expedite an investigation. Equally, city planners can leverage the platform to uncover traffic patterns or to analyze pedestrian movement in crowded areas, thus informing decisions on infrastructure improvements. Even retail businesses can gain insights into customer demographics and behaviours, refining marketing strategies based on how shoppers interact with different parts of a store.
Underlying this capability is a powerful combination of computer vision algorithms, deep learning models, and a Retrieval-Augmented Generation (RAG) chatbot that lets users retrieve footage simply by typing questions in everyday language. For instance, a user could ask, “Show me all clips with a red car near Jalan P9 from 3pm to 6pm,” and the system would quickly return relevant video segments, ready for review. This approach transforms traditionally cumbersome video archives into a valuable resource that drives efficient decision-making, stronger public safety measures, and greater urban management capabilities.
Metadata Schema & Architecture Blueprint
This deliverable defines the data structures and relationships used to store, index, and retrieve enriched metadata from video streams or images. It also includes an architectural diagram outlining how AI models, databases, and retrieval-augmented generation (RAG) components interact to ensure fast, accurate content searches.
Automated Tagging & Indexing Module
We configure and deploy advanced computer vision and deep learning models that automatically label each frame or image with detailed contextual information—such as objects, people, gestures, or time-coded events. This module ensures new videos are continuously tagged and indexed without manual intervention.
RAG Chatbot Integration
In this phase, we embed retrieval-augmented generation capabilities into a user-facing chatbot, allowing natural-language queries like “Show me all clips of red cars on Jalan P8.” The chatbot surfaces relevant indexed video segments or images in seconds, reducing reliance on manual keyword searches.
Quality Assurance & Continuous Improvement
As part of the final deliverable, we provide an iterative testing framework that periodically evaluates the accuracy of metadata extraction and the relevance of search results. We then refine models, improve the tagging logic, and update query-handling processes to keep the platform optimised for evolving data sets.
Explore our work