Images contain critical data, from scanned documents and product photos to satellite and medical imagery. Yet, extracting actionable insights from these sources usually requires significant technical expertise. ADAT’s Computer Vision Engine changes that.

Built with PyTorch and optimized with reinforcement learning, ADAT’s vision engine supports:

• Classification: Assign images to categories (e.g., types of documents or defect status).

• Object Detection: Identify and locate elements such as barcodes, tumors, or license plates.

• Semantic Segmentation: Divide images into regions (e.g., land use types in satellite imagery).

• Anomaly Detection: Spot deviations in quality control workflows.

• OCR Integration: Convert image-based text into structured data using Tesseract.

Whether you're working with real-time surveillance footage or archived image data, ADAT can process both batch and streaming inputs, with GPU acceleration for scale.

Use cases range from healthcare diagnostics and industrial inspection to financial document analysis and environmental monitoring. More importantly, these capabilities are embedded into a no-code platform—making them accessible to a wide range of users.