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Use Cases

Microflowtek demonstrates deployable AI systems for technical workflows. These examples show how data, documents, infrastructure, and expert knowledge can be connected inside controlled operational environments.

Measurement Evaluation Runtime

Turn recurring measurement, inspection, and test workflows into a structured runtime: ingest data from devices and legacy sources, run engineering logic, and output technical decisions or reports.
  • automated data ingestion
  • engineering-specific calculations
  • plausibility checks
  • AI-supported interpretation
  • report generation
  • secure in-environment deployment

Spare Part & Component Identification

Support service and operations teams with AI-assisted identification from photos, labels, documents, and incomplete descriptions, then connect findings to product data and internal workflows.
  • image-based component identification
  • matching against spare-part or product databases
  • document-based context retrieval
  • suggested next actions for service or sales
  • workflow UI for technicians and operations teams
  • optional API integration with internal systems

Edge AI Inspection Runtime with Jetson

Deploy local AI runtime systems on edge hardware such as NVIDIA Jetson Orin Nano for private, low-latency inspection workflows near machines, cameras, and sensors.
  • local AI inference on edge hardware
  • camera and sensor integration
  • real-time inspection support
  • private deployment without cloud dependency
  • local document retrieval
  • hybrid synchronization with cloud or backend systems

Positioning

These use cases are not generic chatbot demos. They represent Microflowtek’s core capability: building deployable AI-supported runtime systems for real engineering and operations workflows.