Technology
Multimodal Intelligence
LLM & VLM-driven AI for unified contextual understanding
Multimodal Intelligence leverages Large Language Models (LLMs) and Vision-Language Models (VLMs) to combine vision, language, sensor and contextual data into a single, coherent understanding of "what is happening right now."
Inputs such as robotic vision, CCTV feeds, access logs, textual reports, time and location are unified through LLMs and VLMs, enabling AI to move beyond isolated detection towards context-aware reasoning and situational understanding.
Overview
Centred on LLM and VLM technologies, Multimodal Intelligence integrates:
Vision
Cameras, robotic vision, CCTV
Language
Text, reports and documentation
Sensors
Access logs and event data
Context
Time, location and environment
AI connects all inputs into a single flow and applies LLM-driven reasoning together with VLM-based visual-language understanding to deliver contextual analysis and intelligent decision-making.
Key Capabilities
Unified Data Integration
Combines vision, text, sensors, logs and context within a single LLM- and VLM-driven AI pipeline
Context Awareness
Understands complete situations rather than isolated events
Meaning-based Reasoning
Interprets visual and linguistic signals to derive meaning and enable appropriate action
Platform-wide Scalability
Extends across the Trace ecosystem, including Trace ACE, Trace Watch, robotics and monitoring platforms
Use Cases
Integrated intelligent monitoring and security platforms
Robotics-driven situational awareness and automation
Smart building and city operations
AI-powered contextual analysis and decision support