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Development of Modular AI-Powered Image Analysis Platform

neurosys.com

Information technology

Medical

Biotechnology

Challenges in Manual Image Analysis

Manual object detection in images and videos is time-consuming, error-prone, and resource-intensive. Existing solutions lack flexibility to adapt to different use cases and fail to maintain precision under time constraints, particularly in critical domains like microbial identification.

About the Client

Research-driven technology company specializing in AI/ML solutions for scientific and industrial applications

Key Project Goals

Create a modular AI platform for automated object recognition across diverse domains
Develop a scalable solution supporting real-time video analysis and live stream processing
Implement flexible deployment options (NuGet package, REST API, UI interface)
Enable seamless integration with client-specific neural network models

Core System Capabilities

Automated object detection using customizable neural network models
Modular framework supporting interchangeable analysis components
REST API integration for cross-platform compatibility
Video processing with frame-by-frame analysis
Live stream analysis with hardware-accelerated performance
Dedicated module development and testing tools

Technology Stack

C++

LibTorch

OpenCV

.NET

REST API

System Integrations

Client-specific neural network models
On-premise/cloud infrastructure
Existing laboratory information systems
GPU acceleration frameworks

Operational Requirements

Horizontal scalability for concurrent analysis tasks
Real-time processing performance optimization
High-precision data sanitization mechanisms
Secure module update and version management

Business Value

Enables 24/7 automated analysis with consistent precision, reduces manual workload by 70-90%, accelerates time-to-insight in research environments, and provides flexible ROI through modular licensing options.