Company BrochureA multimodal AI agent that reads the invisible field
InnerSense AI
InnerSense AI is
a next-generation field intelligence system where
compound AI integrating sound, vibration, vision camera, and air quality data
communicates with identical compound AI to predict disasters before they happen.
Why We Built This
Conventional field management has its limitations.
Existing systems rely on individual sensor alerts and fail to understand the context of complex risk situations. They detect the 'results' of disasters but cannot predict the 'causes.'
After Response
Reactive post-incident response
- Response only after incidents
- No proactive anomaly detection
- Limited preventive management
Human Judgment
Judgment dependent on operator experience
- Reliance on operator skill level
- Subjective judgment criteria
- Difficulty ensuring consistency
Siloed Data
Fragmented sensor data
- Siloed data per sensor
- Limited integrated analysis
- Difficulty in holistic situational awareness
After Alert
Alerts only after disasters occur
- Notifications only after incidents
- Delayed immediate response
- Limited damage mitigation
Beyond fragmented sensors and experience-dependent judgment, multimodal AI integrates all signals under a single standard.
Introducing Multimodal AI Agent Technology
Introducing the Multimodal AI Agent Architecture.
InnerSense AI operates with compound AI agents that analyze multi-sensor data in a distributed architecture, deriving risk situations through consensus via the agent network.
Unified AI Agents
Powered by Multi AI Agents that unify perception.
InnerSense AI is not a single AI, but a structure where compound AI agents integrating sound, vibration, vision camera, and air quality data communicate and make decisions collaboratively.
Sound Agent
Sound Agent
Analyzes abnormal sounds and pattern changes from equipment and processes, enabling early detection of noise-based anomalies.
Vibration Agent
Vibration Agent
Analyzes subtle vibration changes and frequency patterns to identify structural anomalies and equipment condition changes.
Air Quality Agent
Air Quality Agent
Detects airborne hazards and environmental changes to monitor work environment anomalies and risk factors.
Vision Agent
Vision Agent
Analyzes visual anomalies, abnormal behaviors, and process errors based on video data to assess situations.
Collaborative
Multi AI agents consult each other to determine disaster situations.
Each agent's independent judgment results are shared in real-time through the network, cross-referencing different sensory signals and adjusting reliability scores. Through this process, simple noise and individual errors are filtered out, and the 'reality' of a disaster situation is confirmed.
No Accident Data Required
Why can it operate immediately without large-scale accident training data?
InnerSense AI does not rely on large-scale training data based on accident history. This is thanks to an innovative technical architecture focused on 'rate of change' analysis compared to normal patterns and 'correlation' judgment between agents.
Technical Features
Rate-of-Change Analysis:Detects subtle changes compared to normal baselines rather than absolute thresholds
Correlation-Based Judgment:Minimizes false positives through concurrent occurrence patterns of sound, vibration, and vision
Initial Rule-Base & Progressive Learning:Immediately applicable to new sites, with self-learning enhancement during operation
Key Benefits
Zero-Shot deployment to new sites:Immediate deployment without prior training
Stable minimization of PoC duration and failure probability:Rapid validation and immediate value realization
Maximized operational efficiency and scalability:Immediately applicable to multiple sites, minimizing operational burden
No Accident Data Required
Introducing the 5 core values of implementing InnerSense AI.
Risk Prevention
Preemptive Disaster Prevention
- Detects risk signals before incidents occur, securing the golden time for response.
Cost Reduction
Operational Cost Reduction
- Prevents equipment failures and shutdowns, reducing unnecessary costs and labor losses.
Zero Accidents
Zero Critical Accidents
- Enhances field safety and mitigates risks from the absence of skilled personnel.
24/7 Monitoring
24/7 Automated Surveillance
- Reduces dependence on personnel with continuous automated monitoring, improving operational efficiency.
Data Assetization
Field Data Digitization
- Structures field data to build the foundation for process improvement and optimization.
InnerSense AI goes beyond simple monitoring to accelerate digital transformation of the field and build competitive smart workplaces.
Industry Applications
InnerSense AI is applicable across diverse industries.
InnerSense AI
AI Platform
Manufacturing
& Quality
Manufacturing Process & Quality Control
Early equipment anomaly detection
Predicts equipment failures and defects in advance to minimize production downtime and quality risks.
Logistics
Automation
Logistics Centers & Automated Facilities
Noise/vibration-based efficiency improvement
Analyzes equipment noise and vibration data to enhance operational efficiency and stability.
Construction
& Plant Safety
Construction Sites & Plant Safety
Real-time field risk detection
Detects structural anomalies and worker risk factors early to prevent critical accidents.
Energy
& Environment
Energy, Environment & Public Infrastructure
Environmental change detection
Detects environmental changes and hazardous substances to support safe and stable infrastructure operations.
Smart City
Urban infrastructure monitoring
Monitors citywide infrastructure conditions to achieve safe and efficient urban operations.
Smart Factory
Integrated monitoring & operational optimization
Integrates field data monitoring to simultaneously improve productivity and operational efficiency.
We offer customized solutions optimized for your industry.
Not response 'after' incidents, but judgment 'before' incidents.
InnerSense AI presents a new standard for predicting the future of the field.