meta-assessment

byF(x) Data Labs Pvt Ltd

Use Case: Automated Rental Machine Condition Assessment The customer operates a subscription model where domestic appliances are deployed at customer homes. When machines are returned (cancellation, upgrade, relocation), they need to assess the condition to determine refurbishment eligibility, required repairs, and damage accountability. Current state: Manual assessment by warehouse staff — slow, inconsistent, and doesn't scale. What they need: An AI/CV system where end-users or field staff capture 5-6 photographs of the returned machine, and the system automatically evaluates: External body condition — scratches, dents, cracks, stains, discoloration Accessories — presence/absence and condition of detachable components (taps, brackets, trays) Power cord — fraying, cuts, heat damage, safety assessment Internal components & filters — discoloration, sediment buildup, deformation, leakage marks Sensors & electronics — corrosion, water ingress, loose connections The system should output a composite condition grade per machine with component-level breakdown, annotated images, and a refurbishment recommendation. Scale: 8,000 — 10,000 machines returned per month 5-6 images per machine (~50,000-60,000 images/month) 4 SKU types to assess Real-time or near-real-time processing required Relevant AWS services: Amazon Lookout for Vision, Amazon Rekognition Custom Labels, Amazon SageMaker, Amazon A2I, AWS Lambda, Amazon S3, Amazon QuickSight.