Computer Vision Solutions
AI That Sees and Understands
Deploy intelligent visual systems that detect defects, recognize objects, analyze video, and automate inspection tasks. Our computer vision solutions operate at scale with accuracy that exceeds human performance.
What is Computer Vision?
Teaching machines to see and understand
Computer vision enables machines to interpret and act on visual information from images, videos, and cameras. Our solutions go far beyond simple image recognition-we build systems that detect specific objects, measure dimensions, identify defects, track movement, and extract meaning from visual data.
Applications span every industry: manufacturing quality inspection that catches defects human eyes miss, retail shelf monitoring that ensures product availability, security systems that detect anomalies, medical imaging that aids diagnosis, and agriculture solutions that monitor crop health.
We develop custom models trained on your specific visual data, achieving accuracy levels impossible with generic pre-trained solutions. Whether you need to process thousands of images per second or deploy models on edge devices in remote locations, we architect solutions that perform reliably in production.
Why Choose DevSimplex for Computer Vision?
Production-proven visual AI systems
We have deployed over 100 computer vision models processing millions of images daily. Our systems run in manufacturing plants, retail stores, warehouses, and hospitals-environments where reliability and accuracy are not optional.
Our approach prioritizes production readiness. Many teams build impressive demos that fail in real-world conditions. We engineer for edge cases, lighting variations, camera angles, and the countless variables that cause models to fail. Extensive testing on real-world data ensures our models perform as expected when deployed.
We optimize for your deployment environment. Some applications need cloud-scale processing; others require edge inference on constrained devices. We select and optimize architectures-from efficient MobileNets to powerful Vision Transformers-based on your latency, accuracy, and infrastructure requirements.
Continuous improvement is built-in. We implement feedback loops that capture model errors, monitor accuracy over time, and trigger retraining when performance degrades. Your computer vision system gets smarter with use.
Requirements & Prerequisites
Understand what you need to get started and what we can help with
Required(3)
Training Images
Labeled examples of what the model should detect, classify, or analyze.
Clear Visual Task
Well-defined objective: what should the model detect, measure, or classify?
Camera/Image Specifications
Details about image sources, resolution, lighting conditions.
Recommended(2)
Deployment Environment
Where models will run: cloud, on-premise servers, or edge devices.
Performance Requirements
Latency, throughput, and accuracy thresholds for production.
Common Challenges & Solutions
Understand the obstacles you might face and how we address them
Limited Training Data
Deep learning models require large datasets that may not exist for specialized applications.
Our Solution
Data augmentation, synthetic data generation, and transfer learning from pre-trained models maximize accuracy even with limited samples.
Variable Lighting Conditions
Models trained in controlled conditions fail when lighting changes.
Our Solution
Training on diverse lighting scenarios, image preprocessing, and robust feature engineering ensure consistent performance across conditions.
Real-Time Performance
Production systems often require instant results that naive implementations cannot achieve.
Our Solution
Model optimization, GPU acceleration, batched inference, and architecture selection balance accuracy with speed requirements.
Edge Deployment Constraints
Edge devices have limited compute, memory, and power for complex models.
Our Solution
Model compression, quantization, and efficient architectures deliver high accuracy within edge hardware constraints.
Your Dedicated Team
Meet the experts who will drive your project to success
Computer Vision Engineer
Responsibility
Develops detection and classification models, optimizes for production.
Experience
5+ years in CV/deep learning
ML Infrastructure Engineer
Responsibility
Builds training pipelines, manages GPU infrastructure, deploys models.
Experience
5+ years in ML systems
Data Annotation Lead
Responsibility
Manages labeling workflows, ensures annotation quality.
Experience
3+ years in ML data operations
Solutions Architect
Responsibility
Designs end-to-end system including cameras, networking, and integration.
Experience
8+ years in enterprise systems
Engagement Model
Typical engagements begin with a proof-of-concept (6-10 weeks) validating accuracy on your data, followed by production deployment and optimization.
Success Metrics
Measurable outcomes you can expect from our engagement
Detection Accuracy
99%+ mAP
Mean average precision on test data
Typical Range
Inference Speed
< 50ms
Per image on standard hardware
Typical Range
False Positive Rate
< 0.1%
Critical for production reliability
Typical Range
Throughput
100+ FPS
Images processed per second
Typical Range
Value of Computer Vision
Visual AI delivers operational and quality improvements.
Inspection Speed
100x faster
Within Immediate
Defect Detection
99%+ catch rate
Within Post-deployment
Labor Cost Reduction
60-80%
Within 6 months
Quality Improvement
50% fewer escapes
Within 3 months
“These are typical results based on our engagements. Actual outcomes depend on your specific context, market conditions, and organizational readiness.”
Why Choose Us?
See how our approach compares to traditional alternatives
| Aspect | Our Approach | Traditional Approach |
|---|---|---|
| Model Customization | Trained on your specific visual data 20-40% higher accuracy on your use case | Generic pre-trained models |
| Production Readiness | Optimized for real-world conditions Reliable in variable conditions | Lab-quality demos |
| Edge Deployment | Optimized for constrained devices Works where you need it | Cloud-only or heavy models |
| Ongoing Support | Monitoring and continuous improvement Accuracy maintained over time | One-time model delivery |
Technologies We Use
Modern, battle-tested technologies for reliable and scalable solutions
PyTorch
Deep learning framework
YOLO / Detectron2
Object detection models
OpenCV
Image processing library
TensorRT
GPU inference optimization
NVIDIA Triton
Model serving at scale
Edge Devices
Jetson, Coral, custom hardware
Ready to Get Started?
Let's discuss how we can help you with ai & automation.