AI ComputerVision Analytics

The system shall process multiple formats of unstructured textual data to produce a semantic repository, enabling advanced search, similarity analysis, and visual knowledge graph exploration.

Design and implement a computer vision-based system capable of automatically detecting, counting, and estimating biomass of shrimps in images captured from farm environments, with scalability for other aquaculture species.

Design and develop an AI-powered inspection platform capable of analyzing hardware images to identify hardware types, detect defects, and suggest potential root causes, including thermal imaging capabilities for damage assessment.

Design and implement a computer vision system using machine learning algorithms that processes RGB camera recordings and sensor data to accurately estimate contact points at the nanoscale, accounting for optical and dimensional challenges.

Development of a flexible, scalable software platform that facilitates real-time data processing, machine learning integration, and comprehensive analytics dashboard functionalities.

Design and implement a computer vision system that supports image uploads, annotations, model training, prediction, threshold tuning, and deployment, tailored for defect detection in industrial manufacturing.

Design a software system that accepts video inputs, allows for manual annotation of sample frames, and extrapolates annotations to entire datasets, enabling training and deployment of computer vision models for production line monitoring and optimization.

Development of a centralized platform supporting standardized creation, testing, and deployment of AI-based visual data filters, with integrated project management, documentation, and testing modules.

A searchable, AI-powered maintenance assistance platform that supports diagnosis and troubleshooting of machinery failures, leveraging internal and external data sources, and providing user-friendly interaction with contextual recommendations.

Design a computer vision system capable of real-time analysis of wooden panels to detect and classify surface flaws, with alert mechanisms for operators and integration into production workflows.

A web-based platform providing secure, scalable, and intuitive access to multiple AI language models, featuring conversational interfaces, session management, and administrative controls.

Design and develop a digital twin environment to simulate pest activity, environmental conditions, and camera perspectives. Generate synthetic data to train recognition algorithms. Deploy real-time pest detection, tracking, and reporting features within a scalable system.