Streamline manual inspection triage
Prioritize suspicious regions so specialists can focus on samples that need attention.
AQPTECH · PHYSICAL AI
Vision Intelligence for Non-Destructive Testing
Connect industrial data and artificial intelligence to support anomaly detection, quality control and inspection traceability.
Request a demoOverview
Reimagining Industrial Inspection with AI-Led Visual Intelligence
AQPTech Physical AI connects data from the physical world with AI models and industrial inspection workflows. The solution helps engineering teams analyze images, identify signs of anomalies and organize inspection findings in a shared workflow.
Combining computer vision, data analytics and synthetic data, this approach supports dataset preparation, model experimentation and defect detection evaluation under real operating conditions. Each material, asset and data source requires its own validation.
From a single inspection station to multiple production lines, deployment is designed around existing equipment, security requirements and acceptance criteria. AI findings support expert review; quality and safety decisions follow the organization’s inspection procedures.
One Platform. Multiple Modalities. Smarter Inspection Intelligence.
Multi-modal inspection data
Design ingestion for optical images, thermal imagery or digitized NDT data based on confirmed equipment and formats. Each modality has its own normalization and evaluation workflow.
Synthetic data generation
Generate sample variations for lighting, surfaces and rare defects. Synthetic data supplements real observations; effectiveness is assessed on an independent test set.
Visual workflow configuration
Define role-based steps for ingestion, annotation, inference and review. The scope of no-code configuration and custom integration is agreed during the PoC.
Flexible deployment
Choose edge, on-premises or cloud deployment according to latency, bandwidth and data policies. Configuration is validated against representative workloads.
Model-agnostic architecture
Separate data processing from inference models to support experimentation, comparison and version management. Compatibility depends on model formats and runtime resources.
Reporting and benchmarking
Record findings by batch, asset and model version, then compare against expert labels. Agree precision, recall, false alarm and processing time metrics before acceptance.
Key benefits
Accelerate Quality Outcomes with Intelligent Defect Detection at Scale
Apply AI-powered visual intelligence to support inspection, compare findings and expand quality monitoring across industrial environments.
Enrich model development datasets
Combine real and synthetic data to explore a wider range of defect scenarios.
Evaluate defect detection consistently
Compare predictions against inspection criteria and expert reference labels.
Support earlier detection and less rework
Route anomaly signals to the appropriate workflow stage for timely review.
Scale quality monitoring deliberately
Validate across inspection stations, product variants and operating conditions.
Traceable findings and review-ready reports
Link source imagery, reviewed findings and model versions to support internal audits.
Request a Demo
Tell us about your assets, data sources and inspection challenge.
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