--- title: Real-Time Surveillance with Multi-Modal AI Agents on Databricks description: Discover how surveillance with Multi-Modal AI Agents enables real-time monitoring, AI-driven insights, and threat detection on Databricks. image: https://www.xenonstack.com/hubfs/surveillance-multi-modal-databricks.png --- - [![xenonstack-logo](https://www.xenonstack.com/hubfs/xenonstack-logo-new-relase.svg)](https://www.xenonstack.com/) - - Foundry Foundry Unified reasoning foundation enabling seamless orchestration, analytics, infrastructure, and trust across intelligent ecosystems [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/our-purpose-line-icon.svg) Akira AI - Reasoning and Agent Orchestration Turn models into collaborative, policy-governed agents that learn and act together](https://www.xenonstack.com/agentic-platforms/akira-ai/) [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/autonomous-operations-icon.svg) ElixirData - Agentic Analytics Intelligence Explainable, decision-centric analytics for measurable business outcomes](https://www.xenonstack.com/agentic-platforms/elixirdata/) [![pointers-icon](https://www.xenonstack.com/hubfs/xs-revamp-header-dropdown/digital-immune-system-icon.svg) NexaStack - 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Get Started ![Close](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Interested in Solving your Challenges with XenonStack Team ## Get Started Get Started with your requirements and primary focus, that will help us to make your solution First Name \* Please enter a valid First Name Last Name \* Please enter a valid Last Name Business Email ID \* Please enter a valid business email address Contact Number \* Please enter a valid Contact Number Company Website \* Please enter a valid Company Website URL LinkedIn URL \* Please enter a valid LinkedIn URL or N/A Leave this field empty Industry Belongs To \* Please Select your Industry Banking Fintech Payment Providers Wealth Management Discrete Manufacturing Semiconductor Machinery Manufacturing / Automation Appliances / Electrical / Electronics Elevator Manufacturing Defense & Space Manufacturing Computers & Electronics / Industrial Machinery Motor Vehicle Manufacturing Food and Beverages Distillery & Wines Beverages Shipping Logistics Mobility (EV / Public Transport) Energy & Utilities Hospitality Digital Gaming Platforms SportsTech with AI Public Safety - 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Proceed Next ## Interested in Solving your Challenges with XenonStack ## Personalization Get Started with your requirements and primary focus, that will help us to make your solution ### What is your Key focus areas? \* AI Workflow and Operations Data Management and Operations AI Governance Analytics and Insights Observability Security Operations Risk and Compliance Procurement and Supply Chain Private Cloud AI Vision AI ### In Which Agentic Platform and Accelerator you are Interested? \* Akira AI - Agentic AI Platform Multi Agent System Metasecure - Autonomous SOC Nexastack – Build and Managed Compound AI Stack Data Foundry XAI – Vision and AI Platform – Visual AI Agents Strategy Consulting AI Managed Services Others (Please Specify) ### Which segment does your company belong to? \* Startup Scale Startup SME Mid Enterprises Large Enterprises Federal Government Non Profits Others (Please Specify) ### At what stage is your AI use case currently in? \* Conceptualized: Use case defined, PoC pending POC Completed In Production with challenges Not yet defined Others (Please Specify) ### What are the primary challenges in adopting AI? \* Data Quality Issues Data Privacy and Compliance Aligning AI with business goals Unclear ROI from POCs Integration with existing ERP systems Scalability Challenges Moving POCs in Production Infrastructure Limitation High Implementation costs Others (Please Specify) ### What kind of infrastructure does your organization currently using? \* AWS Microsoft Azure GCP IBM Cloud Oracle Cloud On Premises Others (Please Specify) ### Are you using any Data platform? \* Databricks SnowFlake Amazon Redshift Azure Synapse Analytics Microsoft Fabric Teradata Oracle Database SAP Hana Informatica Google Cloud BigQuery Others (Please Specify) ### Preferred Approach for AI Transformation \* Assisted Intelligence Agents as Co-Pilot Collaborative Intelligence Agents as AI Teammates Autonomous Intelligence Agents – AI Agents Agentic Actions Agentic Process Automation ### In Which Domain your Solution/Organization belongs to in-terms of Data Privacy, Trustworthy AI \* Internal Organization Highly Regulated Industry (Healthcare, Financials etc) Medium Regulated Non Regulated ### Captcha Verification \* captcha text ![Refresh Icon](https://www.xenonstack.com/hs-fs/hubfs/refresh.png?width=20&height=20&name=refresh.png) Please select all the required fields Review Previous Submit ![green-checkmark](https://www.xenonstack.com/hubfs/green-checkmark.svg) ## your request has been submitted successfully ! Our XenonStack Team will shortly reach out to you. We are looking forward to showcase how XenonStack can transform your business. ![usecase-banner (1)](https://www.xenonstack.com/hs-fs/hubfs/usecase-banner%20(1).webp?width=1921&height=622&name=usecase-banner%20(1).webp) [XAI](https://www.xenonstack.com/blog/tag/xai) # Real-Time Surveillance with Multi-Modal AI Agents on Databricks [Navdeep Singh Gill](https://www.xenonstack.com/blog/author/navdeep-singh-gill) | 28 November 2025 Real-Time Surveillance with Multi-Modal AI Agents on Databricks 15:00 As sensor data multiplies, security faces new challenges that demand innovative solutions. Old surveillance systems often fall short when dealing with so much information. [Real-Time Video Streaming Applications](https://www.xenonstack.com/blog/real-time-video-streaming-applications/) and and Vision AI surveillance systems are now crucial for handling vast amounts of live data efficiently. Modern multimodal surveillance AI platforms merge video, audio, and sensor streams to deliver [real-time video analytics](https://www.xenonstack.com/blog/video-analytics-agentic-ai), faster alerts, and more proactive decision-making—especially in high-risk environments and AI-driven public safety operations.” ## Understanding Multimodal AI Surveillance and Vision AI Security Surveillance today isn’t just about recording video—it’s about enabling [Vision AI system](https://www.nexastack.ai/solutions/vision-ai/) capable of analyzing images, sounds, text logs, and sensor readings in real time. This is the foundation of city-level video surveillance analytics, where multimodal agents provide a complete picture of public environments for safety, compliance, and operational insight. Thanks to smart cameras and on-site sensors, old security methods are being replaced by complex, high-tech monitoring networks that power [Real-Time Store Monitoring](https://www.xenonstack.com/blog/real-time-store-monitoring) and Real-Time [Retail Analytics Dashboards](https://www.xenonstack.com/use-cases/retail-analytics-platform) for security and operational efficiency. ![multi-modal-surveillance-approach](https://www.xenonstack.com/hs-fs/hubfs/multi-modal-surveillance-approach.png?width=1920&height=1080&name=multi-modal-surveillance-approach.png)**Fig 1: Multi-modal Surveillance Approach**   With better machine learning and the ability to mix different types of data, these systems quickly notice anything unusual—whether it's strange movements in a crowd or quiet sounds that might signal trouble. Advanced StreamSets pipelines help process and analyze real-time surveillance data efficiently, ensuring seamless integration of multiple data sources. ### **Challenges in Traditional Surveillance Systems** Conventional surveillance systems typically rely on single-modality inputs—often only video—and are plagued by issues like limited contextual awareness, false alarms, and high latency. Traditional systems cannot easily integrate multiple data types (e.g., audio, text, and thermal imaging), which restricts their ability to provide a comprehensive security overview. Traditional surveillance lacks the multimodal fusion capabilities required for modern environments. Without real-time video analytics, these systems produce false alerts and struggle in low-light or high-noise scenarios—problems that Databricks Vision AI pipelines are designed to overcome. Furthermore, these systems struggle with scalability, especially when tasked with processing vast amounts of heterogeneous data in real time. This is particularly challenging in [Real-Time Drought Prediction Monitoring Systems](https://www.xenonstack.com/use-cases/real-time-drought-prediction), where multiple sensors need to be analyzed simultaneously to detect critical environmental changes. ### **The Multi‑Modal AI Revolution** Multimodal AI surveillance represents a paradigm shift—moving from basic motion detection to intelligent Vision AI surveillance capable of real-time anomaly detection, video summarization AI, and predictive threat scoring. For instance, a single surveillance agent may analyze video, audio, and even sensor metadata to detect an emerging threat more accurately than any unimodal system could. The adoption of Real-Time Analytics Architecture in platforms like Databricks ensures that vast amounts of security data are processed efficiently, allowing for proactive and intelligent surveillance solutions. ![real-time-surveillance-multi-modal](https://www.xenonstack.com/hs-fs/hubfs/real-time-surveillance-multi-modal.png?width=1920&height=1080&name=real-time-surveillance-multi-modal.png)**Fig 2: Real–time Surveillance With Multi-modal**   This “fusion” of data helps overcome environmental challenges such as low‑light conditions or noisy audio, thereby increasing the system’s robustness and accuracy.  ## Core Concepts of Multimodal Surveillance AI Explained Multi-modal AI is built to handle different types of data at the same time. Instead of using one network for one kind of input, it uses several networks, each focused on a specific type. Their outputs are merged to form a complete view of the scene, which improves accuracy and makes the system flexible for various surveillance needs. Real-time data integration plays a crucial role in ensuring these [AI agents](https://www.xenonstack.com/blog/ai-agents) seamlessly process diverse data sources, enabling efficient and intelligent surveillance solutions. ### **Key Components of AI Surveillance Agents** Multi‑modal surveillance agents consist of several key components:  1. **Input Modules:** Dedicated pipelines for ingesting video, audio, text logs, sensor readings (e.g., thermal, LIDAR), and more. Real-time data products help structure and manage these diverse data streams efficiently. 2. **Feature Extraction Units:** Pre‑processing steps where each modality’s raw data is converted into structured features. For example, [convolutional neural networks](https://www.xenonstack.com/blog/convolutional-neural-networks) (CNNs) extract visual features from images, while recurrent neural networks (RNNs) or transformers capture contextual information from audio or textual inputs. [Time-series databases in real-time analytics](https://www.xenonstack.com/insights/time-series-db-real-time-analytics) enable efficient storage and retrieval of this continuous, high-velocity data. 3. **Fusion Module:** The heart of the multi‑modal AI architecture, where data from disparate sources is combined using techniques like early fusion (merging raw features) or late fusion (integrating outputs after independent analysis). Real-time analytics tools ensure that these fusion processes happen with minimal latency for actionable insights. 4. **Decision‑Making Module:** A classifier or regression model that leverages the fused data to detect anomalies, trigger alerts, or even predict potential threats. Real-time analytics architecture enables these models to function at scale, providing security teams with timely and precise alerts. These capabilities enable advanced use cases like **crowd monitoring**, **traffic analytics AI**, perimeter security, and **AI-driven public safety** workflows. ## Why Databricks Is the Ideal Platform for Vision AI Surveillance? ### **Unified Analytics Infrastructure** Databricks delivers a unified architecture ideal for **Databricks Vision AI** workloads, providing scalable compute for high-throughput video pipelines used in **real-time video analytics** and smart city surveillance applications. Its [Lakehouse](https://www.xenonstack.com/blog/personalized-ai-agents-databricks-lakehouse) setup blends the strengths of traditional data warehouses with those of data lakes, so organizations can manage both organized and raw data in one system. This integration is especially important for multi‑modal surveillance, where information from cameras, microphones, and IoT devices must be processed at the same time.  ### **Scalable Machine Learning Environments** One of Databricks’ key strengths is its ability to scale machine learning workloads effortlessly. Whether you’re training computer vision models on terabytes of video data or fine‑tuning natural language models for audio transcription, Databricks provides a scalable, cloud‑based environment. Its managed Apache Spark clusters, combined with MLflow for model tracking and deployment, allow data scientists to experiment rapidly and deploy models that can handle real‑time surveillance needs.  ### **Real‑Time Data Processing Capabilities** Real‑time detection is paramount in surveillance. Databricks’ Structured Streaming and Delta Lake technologies ensure that data ingestion and processing happen with low latency. For instance, when new video frames or sensor readings arrive, they are automatically ingested, processed, and integrated with historical data. This enables near‑instantaneous anomaly detection and alerting, which is essential in security applications where seconds can make the difference between prevention and loss. > Surveillance is evolving beyond just video—AI now understands text, audio, and more in real time! Discover how Multi-Modal AI Agents on Databricks are revolutionizing security with smarter, faster threat detection in [this blog](https://www.xenonstack.com/blog/video-text-databricks-multi-modal-agents). ## Technical Architecture of Real-Time Multimodal Surveillance Systems ### **Sensor Fusion Techniques** Sensor fusion is the process of combining data from multiple sensors to produce a comprehensive picture. Techniques range from early fusion—where raw data or early‑extracted features are merged—to late fusion, which combines decisions from multiple unimodal models. For instance, an early fusion approach might concatenate features from a CNN (processing images) and an [RNN](https://www.ibm.com/think/topics/recurrent-neural-networks) (processing audio) into a joint vector space, while a late fusion approach might weight and combine the confidence scores from separate anomaly detectors. These fusion models are the backbone of intelligent video analytics in airports, transportation hubs, and city-level surveillance systems. ### **Data Ingestion and Preprocessing** Efficient [data ingestion](https://www.xenonstack.com/blog/big-data-ingestion) is critical when dealing with real‑time streams. [Databricks’ Auto Loader and Delta Live Tables](https://community.databricks.com/t5/data-engineering/databricks-auto-loader-vs-delta-live-tables/td-p/24570) streamline this process by automatically detecting new data files, ingesting them into a unified data lake, and ensuring data quality through schema enforcement and automated error handling. Preprocessing steps may include noise reduction, normalization of sensor data, and synchronization of timestamps across modalities to ensure that all inputs are aligned correctly.  ### **AI Model Integration Strategies** Integrating multiple AI models requires careful orchestration. In a multi‑modal surveillance system, separate models for computer vision, natural language, and audio analysis are integrated through a fusion layer. Strategies include:  1. **Ensemble Methods:** Combining outputs of multiple models to make a final decision. 2. **Hierarchical Models:** Using one model to filter or pre‑classify data before a more detailed analysis is performed by a specialized model. 3. **Attention Mechanisms:** Leveraging transformer‑based architectures to weigh the importance of different modalities dynamically. ## Machine Learning Models Enhancing Surveillance Capabilities ### **Computer Vision Algorithms** [Computer vision](https://www.xenonstack.com/artificial-intelligence/computer-vision/) is a key part of modern security systems. Deep learning models are used to identify objects, recognize faces, and track motion. They can learn to pick up on small details even in busy or poorly lit environments. In addition, methods like object tracking and image segmentation help these systems maintain focus on moving targets from one frame to the next.  ### **Natural Language Processing in Surveillance** While video and audio often dominate surveillance discussions, text and metadata are also crucial. NLP models can process logs, social media feeds, or transcribed audio to detect potential threats or suspicious activities. Transformer‑based models (like BERT or GPT‑4) can contextualize text data and uncover patterns that might be missed by simpler rule‑based approaches.  ### **Audio and Acoustic Analysis Techniques** Audio signals provide an additional layer of context. Techniques like spectral analysis and deep learning models designed for audio classification (e.g., WaveNet or CNN‑based architectures) can detect abnormal sounds—such as gunshots, breaking glass, or distress calls. By combining these signals with visual data, surveillance systems can significantly reduce false positives and improve detection accuracy. > Modern surveillance isn’t just about watching—it’s about understanding. By integrating AI, IoT, and computer vision, security systems can detect threats in real-time, ensuring faster, smarter responses. Explore how [Azure Computer Vision is changing surveillance](https://www.xenonstack.com/blog/smart-surveillance-systems-azure-computer-vision) to learn more. ## Real-Time Detection Techniques in Multi-Modal Surveillance 1. **Low‑Latency Processing Frameworks:** For real‑time monitoring, systems must act almost immediately. Databricks’ Structured Streaming runs on many machines at once, allowing it to quickly process incoming data so that unusual events are spotted within a few seconds. 2. **Anomaly Detection Algorithms:**  These methods learn what the usual patterns look like by examining different types of data. By using techniques such as autoencoders and clustering, they can notice when something deviates from the norm—helping identify potential security issues. 3. **Intelligent Alert Mechanisms:** When something unexpected occurs, the system sends out alerts in a smart way. It ranks alerts based on urgency, cuts out false alarms, and directs the message to the appropriate team member, ensuring a timely and useful response. > ![introduction-icon](https://www.xenonstack.com/hubfs/Imported%20sitepage%20images/introduction-icon.svg)**Navigating Data Privacy and Ethics in AI Surveillance** > > 1. **Compliance Frameworks:** When setting up real‑time AI surveillance, companies must follow data protection rules like [GDPR](https://www.xenonstack.com/insights/gdpr) and HIPAA. Databricks helps by providing tools like Unity Catalog, which gives a clear, unified view of who accesses the data and how it's used, making it easier to stick to the rules and check data usage. > 2. **Anonymization Techniques:** To protect privacy, these systems change the raw data so that personal details aren’t visible. For example, they might blur faces in videos or remove names and other personal information from audio records. This allows organizations to use AI while keeping individual identities safe. > 3. **Ethical AI Deployment Strategies:** It’s important to be open and responsible about how surveillance data is collected and used. This means sharing information with the public, engaging with stakeholders, and having independent reviews. Clear ethical guidelines help ensure that the technology isn’t misused or pushed toward more invasive methods like unchecked facial recognition. ## Strategies for Performance Optimization and Scaling Systems ### **Distributed Computing Approaches** Multi‑modal surveillance systems must handle data from many sensors at once. Using tools like [Apache Spark on Databricks](https://docs.databricks.com/gcp/en/spark/) lets you process large amounts of data in parallel.  ### **Resource Management on Databricks** Databricks automatically adjusts computing power with autoscaling and dynamic allocation. This ensures workloads are handled efficiently and cost‑effectively during busy times.  ### **Cost‑Effective Scaling Strategies** Keeping costs low is key, so Databricks offers serverless and spot instance options. Efficient storage and caching with Delta Lake further cut down on expenses while keeping data ready for real‑time use.  ## Future Trends in Multi-Modal AI Surveillance Technologies 1. **Emerging Technologies:** Future surveillance will benefit from faster networks (like 5G), local processing power, and improved sensors that capture high‑quality data instantly. New AI methods will also help merge various data types for a more complete view. 2. **Predictive Intelligence:** Soon, security systems will not only respond to events but also forecast potential threats by studying past and current patterns. This proactive strategy gives teams an early heads‑up to act before problems develop. 3. **Potential Research Directions:** Researchers may work on making AI decisions more understandable, cutting down biases, and better integrating data from multiple sensors. Cooperation between universities, industry, and government will be key to developing smarter, fairer surveillance tools. ## Multimodal AI Surveillance: Transforming Security with Unified Insights Multi‑modal AI surveillance is changing security by combining video, audio, and sensor data into one live system. This unified approach fixes many issues of older systems and gives a clearer, faster picture of what's happening. Databricks is well‑suited for this task since it brings all types of data together, scales machine learning easily, and processes information very quickly.   As AI becomes a bigger part of security, it’s important to use it responsibly. New systems offer real‑time insights and can even predict issues, but they must be managed with strict data rules and ethical checks to protect privacy and rights. When used wisely, these technologies can help create safer, more efficient communities. ## Next Steps for Implementing Multi-Modal AI Solutions Connect with our experts to implement advanced AI surveillance systems. Learn how industries and departments leverage multi-modal workflows and predictive intelligence to enhance decision-making processes. Harness AI-driven solutions to automate and optimize IT operations, improving efficiency and real-time responsiveness. [Contact Us](https://www.xenonstack.com/contact-us/) [Talk To Specialist](https://www.xenonstack.com/talk-to-specialist/) ### More Ways to Explore Us ### [Video and Text Analysis for Media with Databricks Multi-Modal Agents](https://www.xenonstack.com/blog/video-text-databricks-multi-modal-agents) ### [How to Manage Multi-Modal Datasets for Computer Vision on Databricks](https://www.xenonstack.com/blog/multi-modal-datasets-computer-vision-databricks) ### [Databricks-Powered Solutions for Smart City Computer Vision Projects](https://www.xenonstack.com/blog/databricks-solutions-smart-city-computer-vision) ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks%20&body=Check%20out%20https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) ## Table of Contents ## Share Article - [![XenonStack Facebook](https://www.xenonstack.com/hubfs/xenonstack-facebook-service.svg)](http://www.facebook.com/share.php?u=https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) - [![XenonStack Twitter](https://www.xenonstack.com/hubfs/xs-twitter-white-updated-icon.svg)](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) - [![XenonStack Linked In](https://www.xenonstack.com/hubfs/xenonstack-linkedin-service.svg)](http://www.linkedin.com/shareArticle?mini=true&url=https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) - [![XenonStack Email Icon](https://www.xenonstack.com/hubfs/xenonstack-email-service.svg)](mailto:?subject=Check%20out%20https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks%20&body=Check%20out%20https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks) ## Explore Related Topics [Decision Intelligence](https://www.xenonstack.com/blog/tag/decision-intelligence) [Cloud Native Applications](https://www.xenonstack.com/blog/tag/cloud-native-applications) [Generative AI](https://www.xenonstack.com/blog/tag/generative-ai) [Big Data Engineering](https://www.xenonstack.com/blog/tag/big-data-engineering) [FinOps](https://www.xenonstack.com/blog/tag/finops) [Data Foundry](https://www.xenonstack.com/blog/tag/data-foundry) [XAI](https://www.xenonstack.com/blog/tag/xai) [Autonomous Agents](https://www.xenonstack.com/blog/tag/autonomous-agents) [MetaSecure AI](https://www.xenonstack.com/blog/tag/metasecure-ai) ![navdeep-singh-gill](https://www.xenonstack.com/hubfs/navdeep-gill-ceo-xenonstack.svg) ## Navdeep Singh Gill Global CEO and Founder of XenonStack Navdeep Singh Gill is serving as Chief Executive Officer and Product Architect at XenonStack. He holds expertise in building SaaS Platform for Decentralised Big Data management and Governance, AI Marketplace for Operationalising and Scaling. His incredible experience in AI Technologies and Big Data Engineering thrills him to write about different use cases and its approach to solutions. [Explore More by Navdeep Singh Gill ![cta-blue-arrow](https://www.xenonstack.com/hubfs/cta-arrow-blue.svg)](https://www.xenonstack.com/blog/author/navdeep-singh-gill) ![Subscribe background](https://www.xenonstack.com/hubfs/blog-post-subscribe.svg) ## Subscribe to our Latest Technology Insights and Resources Subscribe Now ![slider-cross-icon](https://www.xenonstack.com/hubfs/slider-cross-icon.svg) ## Get the latest articles in your inbox Business Email ID \* Please enter a valid Business Email ID Company Name \* Please enter a valid Company Name Yes, I would like to receive the XenonStack newsletter as well as marketing emails regarding XenonStack products, services, and events. 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Resources [Blog](https://www.xenonstack.com/blog) [Insights](https://www.xenonstack.com/insights/) [Use Cases](https://www.xenonstack.com/use-cases) [Case Study](https://www.xenonstack.com/case-studies) [Videos](https://www.xenonstack.com/videos/) [EBooks](https://www.xenonstack.com/e-book) [Presentations](https://www.xenonstack.com/presentations/) @2026 XenonStack - A Stack Innovator! [Privacy Policy](https://www.xenonstack.com/privacy-policy/) [Terms and Conditions](https://www.xenonstack.com/terms-and-conditions/) Global Presence : ![india-flag-icon](https://www.xenonstack.com/hubfs/united-states.svg) USA ![india-flag-icon](https://www.xenonstack.com/hubfs/uae-flag-icon.svg) Dubai ![india-flag-icon](https://www.xenonstack.com/hubfs/india-flag-icon.svg) India ![india-flag-icon](https://www.xenonstack.com/hubfs/united-kingdom.svg) UK ![india-flag-icon](https://www.xenonstack.com/hubfs/australia-flag-icon.svg) Australia ✕ ## Agent SRE for Reliability and Observability Solutions AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability - ![Performance Icon](https://www.xenonstack.com/hubfs/performance.svg)Proactive detection of performance and availability issues - ![Root Cause Icon](https://www.xenonstack.com/hubfs/root-cause.svg)Root-cause analysis across microservices and environments - ![Remediation Icon](https://www.xenonstack.com/hubfs/remediation.svg)Automated remediation playbooks to reduce MTTR [Explore Agent SRE](https://agentsre.ai/) ![akira-ai-banner-illustration](https://www.xenonstack.com/hubfs/akira-ai-banner-illustration.svg) ✕ ## Physical Surveillance with Vision AI Agent Technology AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly - ![Motion Icon](https://www.xenonstack.com/hubfs/motion.svg)Real-time detection of suspicious motion or intrusion - ![Video Search Icon](https://www.xenonstack.com/hubfs/video-search.svg)Natural language video search and instant playback - ![Summary Icon](https://www.xenonstack.com/hubfs/summary.svg)Smart summaries for audits, investigations, and compliance [See Vision AI in Action](https://www.xenonstack.ai/) ![physical-surveillance](https://www.xenonstack.com/hubfs/xai-banner-image.svg) ✕ ## Agentic Data Intelligence Across Your Full Data Stack Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics - ![Connects warehouses](https://www.xenonstack.com/hubfs/data-icon.svg)Connects to warehouses, lakes, and streaming sources - ![Question Answering](https://www.xenonstack.com/hubfs/answers.svg)Question-answering in natural language - ![Continuous monitoring](https://www.xenonstack.com/hubfs/monitoring-2.svg)Continuous monitoring for anomalies and KPI deviations [See in Action](https://elixirdata.co/) ![agentic-data-intelligence](https://www.xenonstack.com/hubfs/empowerment-of-analysts.svg) ✕ ## Intelligent Diagnostic for Self-Healing System Automation Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment - ![Diagnostics Icon](https://www.xenonstack.com/hubfs/diagnostic.svg)Automated diagnostics for recurring errors - ![Playbook Icon](https://www.xenonstack.com/hubfs/playbook.svg)Playbook execution: restart services, scale pods, clear queues - ![Feedback Icon](https://www.xenonstack.com/hubfs/feedback.svg)Feedback loop for improving remediation strategies [See in Action](https://agentanalyst.ai/) ![intelligent-diagnostic](https://www.xenonstack.com/hubfs/dataops-reimagined-banner-image.svg) ✕ ## Agentic GRC - Monitoring Risk and Compliance Controls AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready - ![Controls Icon](https://www.xenonstack.com/hubfs/controls.svg)Continuous control checks across infrastructure and SaaS - ![Audit Icon](https://www.xenonstack.com/hubfs/audit.svg)Automated evidence collection for audits - ![Risk Icon](https://www.xenonstack.com/hubfs/risk.svg)Risk scoring and prioritized remediation recommendations [Explore Agent GRC](https://agentgrc.ai/) ![monitoring-risk-and-compliance](https://www.xenonstack.com/hubfs/enhanced-security-measures.svg) ✕ ## Agentic Finance and Procurement Intelligent Agents Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter - ![Visibility Icon](https://www.xenonstack.com/hubfs/visibility.svg)Real-time visibility into spend and commitments - ![Anomaly Icon](https://www.xenonstack.com/hubfs/anomaly.svg)Anomaly detection on invoices and vendor performance - ![Workflow Icon](https://www.xenonstack.com/hubfs/workflow.svg)Intelligent workflows for approvals and sourcing decisions [Optimize Finance & Procurement](https://www.xenonify.ai/) ![agentic-finance-and-procurement](https://www.xenonstack.com/hubfs/responsible-ai-aviators-banner-illustration.svg) ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Navdeep Singh Gill", "url" : "https://www.xenonstack.com/blog/author/navdeep-singh-gill" }, "dateModified" : "2025-11-28T13:54:14.955Z", "datePublished" : "2025-03-24T05:59:59.000Z", "headline" : "Real-Time Surveillance with Multi-Modal AI Agents on Databricks", "image" : [ "https://www.xenonstack.com/hubfs/surveillance-multi-modal-databricks.png" ], "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://www.xenonstack.com/hubfs/Xenonstack-Serives%20-3-2-2021-10.png" }, "name" : "Xenonstack Inc" } } ``` ```json { "@context" : "http://schema.org", "@type" : "Organization", "address" : { "@type" : "PostalAddress", "addressCountry" : "USA", "addressLocality" : "Plano", "addressRegion" : "Texas", "postalCode" : "75024", "streetAddress" : "7700 Windrose Ave. " }, "description" : "XenonStack is the #1 Data and AI foundry and technology services company to simplify and scale Enterprise and Generative AI Journeys.", "email" : "business@xenonstack.com", "logo" : "https://f.hubspotusercontent30.net/hubfs/8161231/Xenonstack-Serives%20-3-2-2021-10.png", "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks", "@type" : "WebPage", "description" : "Discover how surveillance with Multi-Modal AI Agents enables real-time monitoring, AI-driven insights, and threat detection on Databricks." }, "name" : "XenonStack", "sameAs" : [ "https://www.facebook.com/XenonStack", "https://www.linkedin.com/company/xenonstack/", "https://www.youtube.com/c/XenonStackOfficial", "https://twitter.com/xenonstack" ], "telephone" : "", "url" : "https://www.xenonstack.com/" } ``` ```json { "@context" : "http://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Navdeep Singh Gill" }, "dateModified" : "November 28, 2025, 1:54:14 PM", "datePublished" : "2025-03-24 05:59:59", "description" : "Discover how surveillance with Multi-Modal AI Agents enables real-time monitoring, AI-driven insights, and threat detection on Databricks.", "headline" : "Real-Time Surveillance with Multi-Modal AI Agents on Databricks", "image" : { "@type" : "ImageObject", "url" : "https://www.xenonstack.com/hubfs/surveillance-multi-modal-databricks.png" }, "mainEntityOfPage" : { "@id" : "https://www.xenonstack.com/blog/surveillance-multi-modal-ai-agents-databricks", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://f.hubspotusercontent30.net/hubfs/8161231/Xenonstack-Serives%20-3-2-2021-10.png" }, "name" : "XenonStack" } } ```