Discover what ML-powered video analysis can do
Our team knows how to turn everyday video into insight using advanced visual AI. Manage parking, track activity, and boost safety — all in one view.
Bring AI-powered visual intelligence into your organization. We build solutions that help teams stay safer, spot quality issues faster, improve medical imaging, and work with greater accuracy and efficiency.
Our team knows how to turn everyday video into insight using advanced visual AI. Manage parking, track activity, and boost safety — all in one view.
Backed by ongoing research and years of hands-on development, our team brings deep knowledge to some of the most complex areas of visual content analysis and cognitive learning.
We stress-test your assumptions before any model code is written, identifying annotation gaps, evaluating data distribution, and defining success criteria grounded in your deployment environment, not benchmark leaderboards.
End-to-end engineering from raw data to inference-ready artifacts. Architecture choices account for your accuracy targets, hardware constraints, and whether the model runs in the cloud or at the edge.
We handle the production requirements that research code ignores. That means stream reliability, frame-level latency, throughput under load, and clean connectivity to SCADA, ERP, and cloud infrastructure, built to hold up in environments where downtime has a real cost.
Delivered in weeks rather than quarters, with a working inference pipeline, basic review UI, and benchmark report against your actual data. No commitment to a full production build before the approach is proven, and no ambiguity about what the PoC is meant to answer.
We’ll help you find the smartest way to make a real impact on performance, safety, and customer experience. Practical, measurable, and built around your priorities.
Automating video analysis with advanced vision systems is not only faster and far less resource-intensive, it’s also remarkably versatile. These tools can handle everything from moderating live content and flagging policy violations to identifying scenes or actions for cataloging and video annotation.
Other practical applications in video include:
Imagine achieving significantly higher CTRs by showing the right ads to the right users at precisely the right time, just as they’re ready for your recommendations. With emotion and attention analysis, that becomes standard practice. Vision-based analytics also elevate interactions between advertisers and audiences through:
True security becomes possible when visual intelligence is applied across surveillance and monitoring. Modern camera technologies and image analysis algorithms help detect violent or hazardous behavior, prevent retail theft, reduce losses, and manage crowd movement efficiently.
They also support following capabilities:
AI-powered video and IoT analytics give production teams real-time visibility into process deviations, equipment behavior, and factory-floor safety conditions. Automated quality inspection and smart surveillance shift defect detection from post-hoc audits to in-line catches.
Computer vision and AI-assisted video analytics monitor adherence to safety protocols in real time, combining facial recognition, thermal imaging, and object tracking into a unified situational layer.
Modern video infrastructure for body-worn, in-vehicle, and stationary cameras handles secure live streaming, transcoding, and long-term storage with full chain-of-custody compliance. Microservices architecture keeps each component independently scalable and auditable.
Vision AI technologies unlock a wide range of enhancements for eLearning platforms, like optimizing assessments, streamlining administrative tasks, and tracking attendance. One major benefit is spotting frustration or distraction in real time, allowing quick response or future teaching adjustments.
All this can be achieved with:
Vision-driven automation is transforming retail by optimizing processes and elevating customer experience across both online and offline channels. These technologies enhance inventory management, streamline product labeling, and forecast demand peaks with high accuracy.
They can also be used to:
With exceptional accuracy and efficiency, visual AI is reshaping the financial sector by automating repetitive, error-prone processes. Organizations are using it to extract data from documents, assess image-based evidence, and evaluate damages for insurance claims.
Additional use cases include:
We’re always testing new ideas and building in-house prototypes to tackle tough challenges. What we learn along the way helps us deliver smarter solutions faster and more cost-effectively for our clients.
Since 2005, we’ve gained deep experience in live streaming, VoD, and real-time communication. Combined with our visual intelligence know-how, this background enables us to deliver advanced AI-driven solutions.
Security comes first for us. Being ISO 27001 certified means we take every measure to keep your data protected and treat it with the same care and responsibility we bring to every project.
Get in touch to explore how visual intelligence can benefit your organization. We offer personalized computer vision consulting to help you find practical, effective solutions.
Custom computer vision software development converts complex visual data into meaningful insights, forming the backbone of the technologies we use every day.
TensorFlow • PyTorch • ML • mxnet • Caffe2 • Chainer • SonnetTech • Theano • Microsoft Cognitive Toolkit
Kurento • nVidia DeepStream • TensorRT • GStreamer
Google Cloud AI • Amazon Machine Learning • Azure Machine Learning
Server • Desktop • Edge Services • Cloud • Mobile • Tablet
Computer vision, a branch of AI, powers machines to interpret digital images and videos and detect meaningful patterns within them. Using deep learning and neural networks, any modern custom computer vision solution learns and adapts independently, making it exceptionally powerful and versatile.
Modern vision systems employ a range of analytical methods. The most widely used include:
Automated evaluation of components, machinery, and workflow processes allows organizations to identify defects and measure tolerances. This approach maintains compliance with safety and quality standards more quickly and accurately than traditional manual inspections.
The highest-value deployments cluster in industries where visual data is abundant and decisions based on it carry real cost or risk. Manufacturing uses CV for inline quality inspection and defect detection. Healthcare applies it to medical imaging and patient monitoring. Retail covers inventory tracking and checkout automation. Automotive and transportation lean on it for ADAS and fleet safety. Public safety uses it across surveillance and forensic video pipelines.
The real question is whether your use case has sufficient data, clear success criteria, and a production environment where the model’s output connects to an actual decision.
Our core work sits at the intersection of video engineering and computer vision: object detection and tracking, semantic and instance segmentation, face recognition, biometric verification, and anomaly detection for industrial and surveillance contexts. A significant share of our projects involve real-time video, integrating vision models into live camera feeds and IoT networks rather than batch-processing static images.
Yes. Real-time inference introduces constraints that batch processing doesn’t: frame rate requirements, latency budgets, and hardware limits all shape architecture decisions before a model is trained. We work with GStreamer, RTSP, WebRTC, and edge platforms like NVIDIA Jetson, combining model optimization and pipeline design to hit genuine real-time performance.
Two to six weeks for a focused PoC, with data readiness being the biggest variable. If annotated training data exists and covers the target distribution, we move fast. If collection and labeling are in scope, the timeline extends. We scope PoCs to answer one question: does the approach work on your data at the accuracy level your use case requires?
We start with discovery – understanding your data, environment, and constraints before proposing any architecture. From there we move to data preparation, baseline training, iterative refinement against agreed benchmarks, and integration into your stack. Validation runs on data that reflects real deployment conditions, not clean test sets. After launch, we monitor for distribution drift and establish a retraining cadence before handing over ownership.
Oxagile helped a company maintain a safe environment for employees and customers during COVID-19 by developing a system that provided full visibility into on-site processes through:
AI-powered computer vision industrial applications help monitor processes, detect anomalies, and maintain safety and quality standards in real time.
AI-driven video analytics transform surveillance, monitoring, and evidence management. These solutions utilize object and face recognition, automated redaction, and intelligent tracking to strengthen security, accelerate investigations, and boost productivity across public and private sectors.
AI-powered video and IoT analytics provide real-time insights into production processes, detect deviations, and maintain factory-floor safety. Smart surveillance and automated quality inspection help cut errors, improve accuracy, and support data-driven operational decisions.
Computer vision and AI-assisted video analytics monitor compliance with workplace safety protocols and guidelines in real time. Combining facial recognition, thermal imaging, and object tracking, such solutions help organizations maintain secure, efficient, and well-regulated environments.
Modern video systems for body-worn, in-vehicle, and stationary cameras enable secure live streaming, transcoding, and playback with full compliance. Powered by microservices and intelligent analytics, they protect data, streamline video management, and strengthen situational awareness.
