🚀 Project Showcase

Get from “we have data” to “we have a reliable AI system in production.”

Hands-on AI consulting for teams shipping computer vision into production — model design, active-learning loops, edge deployment, and the compliance evidence that makes it defensible. Featured case study: CGS hardware inspection (below).

AI monitoring system analyzing network server ports showing occupied and empty connections in data center infrastructure

Problem Context

CGS needed reliable detection and classification across multiple hardware components, including:

Server network interface cards labeled with 2, 4, and 8 ports in data center rack showing port configuration and capacity"

Port availability and segmentation across R2, R4, and R8 cards

Network ports showing empty and occupied states with Ethernet cable connected to server interface in data center"

Identification of empty vs occupied ports

Network interface showing uncapped and capped ports in server hardware with labeled comparison of open and protected connections"

Detection of capped vs uncapped ports

Network connector showing well engaged and improperly engaged cable with visible gap detected in server port connection"

Classification of jack connections (well-engaged vs improperly engaged)

"Server hardware components labeled power supply, fan module, and port modules in data center infrastructure visualization"

Structural understanding via chassis segmentation

Edge computing device performing real-time processing with network connections and low compute infrastructure setup"

the solution operates in an air-based (edge) environment with constrained compute and real-time requirements

Solution Architecture

Designed a multi-model computer vision pipeline integrated for AR overlays. Focused on accuracy, robustness, and deployment readiness.

AR overlay classifying server ports and chassis modules in real time.

Model Development

Custom segmentation models for ports and hardware components.
Transfer learning applied for improved chassis segmentation.

Diagram showing an active learning loop cycling through model inference, annotation, retraining, and error sampling.

Active Learning Loop

Continuous loop: inference → error sampling → annotation → retraining.
Focused on edge cases to improve generalization.

YOLOv11 model optimized and packaged for deployment-ready client integration.

Optimization & Deployment

YOLO-based architectures for high-precision detection.
Hyperparameter tuning, dataset balancing, failure-case debugging.
Delivered models optimized for real-time edge + AR deployment.

Business Impact

~95% accuracy across detection and classification Achieved

Significant reduction in manual inspection effort

Enabled real-time hardware validation

Low-latency, production-ready deployment

What Made This Enterprise-Ready

Delivered a full-stack AI delivery pipeline: complete production system, not just models.

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Production-oriented model design

Illustration of a production-oriented model training and deployment workflow.

Active learning-driven improvement cycle

Circular diagram of an active learning cycle spanning error sampling, model inference, and retraining.

Edge-optimized deployment

Edge optimized model onto server infrastructure in real world environments

Scalable to new hardware variations

Scalable architecture diagram connecting servers to FPGA, CPU, and cloud hardware.

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We help teams move from experimental models to production AI systems.

“We have data” → “We have a reliable AI system in production”

By combining

Deep computer vision expertise

MLOps and deployment pipelines

Iterative, feedback-driven model improvement

Illustration highlighting computer vision expertise, MLOps pipelines, and feedback-driven model deployment capabilities.

Ready to move from prototype to production AI?

Replace manual inspection with scalable, real-time AI systems.