Computer Vision
We build computer vision systems that run in production — not lab environments. From real-time object detection and multi-camera pipelines to on-premises inference engines that process live video without cloud dependency.
Vision Systems Built for Real-World Conditions
Computer vision that handles occlusion, lighting variation, crowded scenes, and commodity hardware — the conditions that exist in production, not in benchmark datasets.
Object Detection & Tracking
Production-grade object detection with YOLOv8 — person detection, vehicle recognition, custom class training — combined with ByteTrack multi-object tracking for consistent identity across frames.
Face Recognition & Re-ID
InsightFace-based face recognition with cross-camera re-identification — track known individuals across multiple camera zones, match against registered databases, and handle occlusion and angle variation.
Video Analytics Pipelines
Frame acquisition, pre-filtering, multi-model inference, result collation, and event dispatch — end-to-end video AI pipelines engineered for throughput, low latency, and efficient resource utilisation.
On-Premises Edge Inference
Deploy vision AI where data privacy demands it — on-site inference that eliminates cloud video streaming, removes per-frame API costs, and satisfies data-sovereignty requirements for regulated sectors.
Multi-Camera Orchestration
Coordinate AI inference across dozens of cameras simultaneously — per-camera scenario assignment, configurable frame rates, time-window scheduling, and parallel inference core management.
Compute Efficiency Engineering
Pre-filter-first architectures using MOG2 background subtraction and perceptual hashing to eliminate over 90% of redundant frames before any deep learning model is invoked — enabling more AI on less hardware.
96.8% Accuracy. Commodity Hardware.
We've achieved 96.8% AI detection accuracy on commodity on-premises hardware across live deployments — not controlled lab conditions. That gap is all engineering.
Pre-Filter Architecture
We eliminate irrelevant frames before they reach any model — using motion detection, scene-delta analysis, and perceptual hashing to keep inference load focused on frames where something is actually happening.
7 Concurrent AI Capabilities
Our production systems run intrusion detection, vehicle recognition, face recognition, re-identification, litter detection, wrong-parking detection, and scene analysis simultaneously — on a single on-premises node.
Full Stack: Hardware to Cloud
We built the CCTV camera nodes, the on-site inference engine, the cloud orchestration backend, and the mobile apps — giving us unique insight into computer vision system engineering at every layer.
Computer Vision In Production
Compute-Efficient Video AI
A pre-filter-first architecture eliminating over 90% of video frames before any deep learning model is invoked — enabling 7 concurrent AI capabilities on commodity on-premises hardware.
SajagAI Platform
An intelligent video surveillance ecosystem with on-site computer vision — multi-camera AI inference running on a homebuilt embedded Linux platform with no cloud video dependency.
Building a computer vision system?
From camera hardware to cloud integration — we've engineered the full stack and can accelerate your vision AI project.