Vigil AI real-time emergency response with accelerated computing using NVIDIA NIMTM  for low latency and deployment at scale.

VigilAI is a real-time emergency response system with next-generation smart surveillance. It is developed as a proof of concept for accelerated AI application development using NVIDA NIM, accelerated GPUs and optimised frameworks. The core objective is to reduce emergency response times and ensure faster, more accurate detection of critical accidents, crimes and evolving situations using advanced AI. 

VigilAI integrates multiple domain-specific models into an agentic AI workflow, tied together with robust engineering. The system detects threats, generates on-demand incident reports, responds to queries through a multilingual AI chatbot, and delivers an interactive geo-aware map of alerts and incidents.

VigilAI improves smart cities, crowded events, industrial plants, shopping malls and traffic systems. Its predictive power and coverage saves lives, reduces property damage and fosters safer communities worldwide.

  • Challenges
  • Solutions
  • Outcomes

Challenges

67% of female victims of violence never report incidents, while 50% of ambulance workers have watched patients dying due to delays. CCTV reduces crime by 25% but surveillance is intermittent and subject to human error. When emergencies are reported, distressed witnesses struggle to express themselves clearly, report inaccurate information and often do not know where they are located. 

These challenges lead to lost time, false alarms, unnecessary deaths and loss of public trust in authorities. 

In the US, the average security guard is paid two-thirds of the median salary and there is a shortage of personnel due to the physical danger, unsocial hours and lack of training and equipment. In the UK, three quarters of a million health emergency callouts are not attended by a fully qualified paramedic. In England and Wales, warehouse fires alone result in £190 million of losses a year. Emergency response systems are creaking and there is a lack of funding to close the gap and meet the expectations of electorates.

Automating surveillance faces a number of significant challenges. Multiple video feeds must be streamed simultaneously and analysed for a wide range of potential threats with minimal false positives. The AI models to handle these workloads must operate with minimal latency to save time, money and lives. Human operatives must be able to identify the highest priority risks and locate and respond to incidents rapidly.

Solutions

VigilAI automates the emergency response pipeline from detection to alerting, with the option to automate despatch. The prototype identifies smoke, fire, car accidents, assaults and violent actions. The single screen application updates in real-time, flagging priority situations and can generate summary incident reports and briefs on emerging situations in multiple languages.

NVIDIA accelerated computing underpins VigilAI’s ability to handle heavy workloads without lags. GPU-accelerated inference enables quick text generation and embeddings. NVIDIA’s hardware and ecosystem supports scaling from small edge devices to large-scale data centre deployment. This will allow VigilAI to scale with users’ requirements and operate without interruption in remote areas without constant internet connectivity.

NVIDIA NIM is a containerised inference microservice including industry-standard APIs, domain-specific code, optimised inference engines and enterprise runtime as part of NVIDIA AI Enterprise. Microservices may be deployed anywhere, including local workstations, cloud and on-premise data centres.

NVIDIA Accelerated Computing

Microservices, Models and Ecosystems Purpose Result
NVIDIA NIMTM Microservices for accelerating generative AI model deployment, anywhere Secure, reliable deployment of high performance AI models
NVIDIA® TensorRT™ An ecosystem of APIs, toolkits, and compilers, for low latency and increased throughput Improved LLM inference, quantisation, sparsity and distillation to compress models for compilation
CUDA-X™ Microservices, libraries, tools, and technologies built on CUDA® for building applications Higher performance data processing, AI and high performance computing (HPC)
Cosmos Nemotron 34B A leading vision language model (VLM) deployable anywhere Query and summarise real-time images and video
Technologies Purpose Result
Roboflow YOLO Object identification and classification in a single pass Detect fire, smoke and car accidents in real-time
Video Masked Autoencoder (VMAE) Masked reconstruction Identifies assault and violent activities
DeepSeek R1 LLM inference for near real-time text processing On demand, structured reports
Meta LLaMA Embeddings, re-ranking of textual queries and chatbot functionality Representation of high dimensional data, enhanced relevance and user interaction
React + Tailwind Interactive user interfaces and customisable UI components Display of alerts, incident logs and maps
Leaflet + OpenStreetMap Open-source JavaScript library for a free, open map database Mobile friendly interactive maps
FastAPI Data routing from multiple AI endpoints Handling queries, selecting detection models and storing logs
SQLite Readily upgradable database for storing logs and incidents Retrieval and scalable analysis of data

This results in

  1. Real-Time Processing of Visual Data
    Streaming and analysing multiple video feeds simultaneously using CPU/GPU intensive models.
  2. Detecting Multiple Types of Incident
    The prototype detects fires, smoke, car accidents, assaults and violent behaviour. It can be expanded to suspicious packages, medical emergencies and kidnapping.
  3. Multilingual Output
    Graphs, statistics and summarised logs for rapid interpretation of the security status. The prototype delivers structured incident reports with real-time data in English and Arabic and may be extended to almost any language.
  4. Geo-Spatial Visualisation
    A live map with pins to incident locations to improve efficiency of despatch.

Outcomes

  1. Dramatically Reduced Response Times
    • VigilAI automates the entire pipeline from detection to alerting.
    • Emergency services can be despatched within seconds of an incident being flagged.
  2. Improved Accuracy Through Specialised Models
    • YOLO offers robust performance for smoke/fire/car accident detection.
    • VMAE focuses on subtle cues of assault or violent actions—situations that other detectors might overlook.
  3. Bilingual Chatbot for Wider Reach
    • Rapid generation of Arabic and English textual outputs.
    • Operators can ask for daily incident summaries or quick briefs on emergent situations and get responses in multiple languages.
  4. Holistic Dashboard
    • A single-pane-of-glass UI built with React and Tailwind, where all alerts, statistics and chat interactions are centrally managed.
    • Live data is fed to the dashboard, reducing manual refresh or data-polling overhead.
  5. Proactive Reporting and Analysis
    • DeepSeek R1 harnesses large language model capabilities to generate comprehensive reports with graph visualisations, making post-incident investigation faster and more insightful.

If you interested in seeing VigilAI in action and developing a production version of its emergency response system for your business or government department, please contact Priyank Bhavsar at info@msbcgroup.com or https://www.linkedin.com/in/priyankb/

Potential Use Cases and Applications

  1. Smart Cities and Public Surveillance
    • Airports, train stations and large public squares—anywhere with high foot traffic that requires constant monitoring for accidents or suspicious behaviour.
  2. Industrial and Factory Settings
    • Rapid detection of smoke, fire, or hazardous incidents in real-time can prevent massive operational and financial losses.
  3. Commercial Complexes and Retail
    • Malls and shopping centres can immediately respond to theft, violence, or fires before they escalate.
  4. Emergency Despatch Services
    • Ambulance and fire departments can integrate VigilAI for faster alerts, cutting out any middle layers of manual reporting.
  5. Mass Event Crowds
    • Concerts, festivals and sporting venues can use VigilAI to detect fights, track potential hazards, and coordinate emergency staff more effectively.

Benefits to Stakeholders

  1. Law Enforcement and Public Agencies
    • Enhanced safety, better resource allocation and increased public trust.
  2. Private Security Firms
    • Automated monitoring, fewer false alarms and more cost-effective solutions.
  3. City and Event Planners
    • Lower risk profile for big gatherings and improved crowd management to minimise reputational risk.
  4. Industrial Operators
    • Prevention of costly accidents and compliance with safety regulations.

Expansion Opportunities

  1. Expanding Detection Classes
    • Beyond fire and assault, VigilAI can be trained to detect kidnapping, suspicious packages, or medical emergencies (e.g., detecting someone who collapses in real-time).
  2. Predictive Insights
    • With enough historical data, the system can use predictive AI to anticipate high-risk periods or locations, re-routing resources preemptively.
  3. Edge Deployment
    • For remote or bandwidth-limited locations, models could run on NVIDIA Jetson devices, ensuring minimal latency even without constant internet connectivity.
  4. Public Portal
    • A moderated, privacy-aware view that allows the public to see certain non-sensitive alerts, encouraging community reporting and faster incident resolution.
  5. Integration with Drone/Robotics
    • Autonomous robots or drones could pair with VigilAI’s detection engine, extending coverage into large or hard-to-reach areas like wide factory floors, agricultural sites, or massive outdoor events.