We build custom computer vision systems for video surveillance — from YOLOv8/YOLOv9 object detection and DeepSORT multi-object tracking to facial recognition, LPR/ANPR, and behavioral analytics. Our AI-powered solutions deploy on NVIDIA Jetson edge devices, on-prem servers, or cloud infrastructure (AWS/Azure/GCP) with sub-200ms latency. Proven across 600+ projects since 2005 — including V.A.L.T (2,500+ cameras, 770+ police departments, 50K daily users) and MindBox (50+ retail locations with AI analytics).
We design and develop custom computer vision and AI video surveillance platforms that transform raw video streams into structured, actionable intelligence in real time. Our systems combine deep learning, video analytics, and scalable infrastructure to help organizations detect threats early, automate monitoring, and reduce operational risk.
Each solution is tailored to your cameras, deployment model, and business logic.
We deploy YOLOv8/YOLOv9 with DeepSORT to detect and track people, vehicles, and assets across multiple camera streams in real time. We deploy YOLOv8/YOLOv9 with DeepSORT/ByteTrack for multi-object tracking, custom CNN architectures for domain-specific detection, and TensorRT-optimized inference on NVIDIA Jetson edge devices — delivering sub-200ms latency. V.A.L.T processes 2,500+ camera feeds across 770+ police departments; MindBox runs AI analytics at 50+ retail locations.
We handle custom model training, dataset annotation, transfer learning, and domain adaptation — fine-tuning detection models on your specific environment data to achieve 90–98% accuracy with minimized false positives across diverse lighting, weather, and occlusion conditions.up to 70%.
We build high-accuracy facial recognition and LPR/ANPR systems using ArcFace, FaceNet, and custom CNN architectures — with real-time identification against databases of 100K+ entries. Our license plate recognition supports multi-country formats with 95%+ accuracy. V.A.L.T leverages facial recognition and ANPR across 770+ law enforcement departments for suspect identification and vehicle tracking.InsightFace and custom-trained datasets optimized for your operational and regulatory needs.
Edge deployment ensures low latency and biometric data protection.
Our AI video analyticsOur behavioral analytics and anomaly detection systems identify loitering, crowd formation, perimeter breaches, unusual movement patterns, and PPE compliance violations in real time. Using spatiotemporal analysis, pose estimation, and trajectory prediction, we transform passive surveillance into proactive security intelligence — enabling sub-second automated alerts and incident response.
Searchable timelines, AI-generated heatmaps, and indexed events transform video from passive recording into proactive risk prevention. Integrated with Grafana dashboards, SIEM/SOAR platforms, and custom BI tools for centralized security operations management.
Traditional CCTV relies on manual monitoring and reactive review, often generating false alarms from basic motion detection.
Computer vision-powered surveillance analyzes video streams in real time using YOLOv8/YOLOv9 object detection, DeepSORT/ByteTrack multi-object tracking, and behavioral analytics — triggering alerts only when relevant objects, behaviors, or anomalies are detected.
Monitoring
Manual, human operators required 24/7
Automated YOLOv8/DeepSORT AI analysis, sub-200ms detection
Alerts
Frequent false alarms from motion
AI-triggered alerts only on relevant objects, behaviors, or anomalies — 90–98% accuracy
Response
Reactive, slow
Proactive, faster response
Labor & Cost
Labor-intensive, higher operational cost
Lower manpower requirements, cost-efficient
Scalability
Limited, single-site focus, no cross-camera intelligence
Multi-site orchestration, edge+cloud hybrid, centralized AI management
Insight
Raw video review
Structured, context-aware notifications
Accuracy
Prone to missed incidents
90–98% detection accuracy with custom-trained models, fewer false positives
Our AI and computer vision surveillance platforms are built with modern, high-performance technologies to ensure real-time detection, low latency, and scalable multi-site deployment.
Every component is optimized to deliver accurate insights, handle large camera networks, and integrate seamlessly with existing infrastructure.
Our AI-powered computer vision systems powered by YOLOv8/YOLOv9 object detection, DeepSORT/ByteTrack multi-object tracking, and predictive behavioral analytics — turning video feeds into actionable intelligence in real time. From retail security and industrial safety to smart cities, logistics, and healthcare, our AI computer vision solutions reduce risk, improve operational efficiency, and deliver 90–98% detection accuracy across multi-site deployments.
Custom AI Video Surveillance Software Development for every case. Secure, scalable, and packed with smart features.
![[background image] image of logistics control room (for a trucking company)](https://cdn.prod.website-files.com/64e8910adc5a63966a68acc1/68e7dfd17638aaf511162f7a_f841ed23dc31eb8a94e23195c64f4acb_develop.webp)
We design, develop, and deploy custom computer vision surveillance systems from scratch — including object detection models (YOLOv8/YOLOv9), tracking pipelines (DeepSORT/ByteTrack), and analytics dashboards. 600+ projects delivered since 2005.
Upgrade your existing CCTV/VMS infrastructure with AI-powered analytics — add object detection, facial recognition, LPR/ANPR, behavioral analytics, and real-time alerting. We’ve upgraded systems like V.A.L.T (2,500+ cameras) to deliver 10× more actionable insights.
![[digital project] image of a showcased project (for a ai robotics and automation)](https://cdn.prod.website-files.com/64e8910adc5a63966a68acc1/68e7e04abb8f1a3770a8625e_fix.webp)
Inherit and stabilize struggling computer vision projects — model optimization, accuracy improvements (70%→95%+), inference pipeline fixes, and edge deployment optimization. We audit, fix, and scale underperforming AI surveillance systems.
Startup 💡
YOLOv8/EfficientDet pretrained models, single-camera or small-site deployment, basic detection dashboard, RTSP/ONVIF integration. 4–6 week delivery.
from
$20,000
from 1.5 months
Growth 🚀
Custom-trained detection models, DeepSORT multi-object tracking, LPR/ANPR, behavioral analytics, BI dashboards. 10–1,000+ cameras with sub-200ms latency.
from
$40,000
from 3 months
Enterprise 🏢
Edge+cloud hybrid architecture, facial recognition, predictive analytics, multi-site orchestration, HIPAA/SOC II compliance, 99.9% uptime SLA. AWS/Azure/GCP deployment.
from
$60,000
from 5 months
Perfecting complex real-time video software — delivering V.A.L.T, MindBox, and 600+ custom solutions with proven real-world impact.
Senior developers, QA, UI/UX designers, analytics – all in-house. We think like product owners, building end-to-end AI surveillance platforms.. We think like product owners, not just coders.
Over 600+ completed projects including V.A.L.T (2,500+ cameras, 770+ police depts) and MindBox (50+ retail locations). 90–98% detection accuracy, 100% Upwork Success rate, and 400+ honest client reviews. Results you can verify.
Get the scoop on real-time video/audio, latency & scalability – straight talk from the top devs
AI video surveillance software uses computer vision models (YOLOv8, EfficientDet, custom CNNs) and multi-object tracking (DeepSORT, ByteTrack) to detect people, vehicles, faces, license plates, behaviors, and safety risks in live video feeds — with sub-200ms latency on edge devices like NVIDIA Jetson or cloud infrastructure.
Computer vision enables software to automatically interpret video content using deep learning models — detecting and tracking people, objects, faces, vehicles, and behaviors without human monitoring. Combined with TensorRT-optimized inference and edge AI deployment, it delivers real-time automated surveillance at scale.
With environment-specific model training, transfer learning, and domain adaptation, accuracy typically reaches 90–98% depending on task complexity and camera quality. DeepSORT/ByteTrack tracking plus TensorRT optimization further reduces false positives. Our custom training pipeline fine-tunes models on your real-world data for maximum precision.
Yes. Every component is fully customizable: AI detection/tracking models, analytics dashboards, alerting workflows, third-party integrations (Grafana, SIEM/SOAR, ERP/WMS), hardware deployment (NVIDIA Jetson, on-prem, cloud), and UI/UX. V.A.L.T and MindBox are examples of fully custom platforms we’ve built.
Yes. With distributed edge+cloud inference, TensorRT optimization, and cascading architecture, our systems scale to thousands of cameras across multiple sites. V.A.L.T manages 2,500+ cameras across 770+ police departments with 50K daily users and 650+ organizations.
Yes. We support ONVIF, RTSP, IP cameras, DVR/NVR systems, and hybrid environments. Our AI layers integrate with your existing CCTV/VMS infrastructure without replacing hardware — V.A.L.T upgraded 2,500+ existing cameras with AI-powered search and analytics.
Yes. We implement GDPR/HIPAA-compliant privacy controls including on-device edge AI inference (data never leaves premises), encrypted SRTP video streams, role-based access control, audit logging, and configurable data retention policies.
Edge AI (NVIDIA Jetson Nano/Xavier/Orin) reduces latency to sub-50ms and cuts bandwidth costs by 60–80%. Cloud (AWS/Azure/GCP) enables centralized storage, large-scale model retraining, and multi-site management. Most enterprise deployments use hybrid edge+cloud architecture for optimal performance and cost.
Startup MVPs: 4–6 weeks. Growth systems (custom models, multi-camera, analytics): 2–4 months. Enterprise deployments (multi-site, compliance, hybrid architecture): 4–6+ months. Agentic Engineering accelerates delivery 2–10× with AI-assisted development.
