Aidena

— AI STACK RECOMMENDATION

AI Vision QC System for Manufacturing

Real-time defect detection on production lines using computer vision models, edge inference, and monitoring. Scalable from single line to multi-line deployment.

Stays alive for 365 days after the last visit.

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AI Vision QC System for Manufacturing

Real-time defect detection on production lines using computer vision models, edge inference, and monitoring. Scalable from single line to multi-line deployment.

high confidence

Core Stack ℹ︎

AWS Bedrock

Primary

Managed foundation models with vision capabilities for defect classification. Scales automatically without managing infrastructure. Integrates with AWS services for production deployment.

$0.50-$2/1000 images

Baseten

Primary

Deploy custom vision models as production APIs with auto-scaling and GPU support. Low-latency inference critical for real-time line monitoring.

$0.10-$0.50/hour

Complete the Stack ℹ︎

Amazon Titan Image Generator

Alternative

Generate synthetic defect images for training and data augmentation. Helps bootstrap training datasets when real defect samples are limited.

$0.008/request

Datadog LLM Observability

Alternative

Monitor inference latency, accuracy, and costs across production lines. Track defect detection performance and system health in real-time.

$299/month

Airbyte

Alternative

Ingest defect images and metadata from cameras/sensors into data warehouse. Build datasets for model retraining and analytics.

$0/month (self-hosted)

Getting started

  1. 1Set up AWS Bedrock with vision models for defect classification.
  2. 2Deploy custom YOLO or similar detection model on Baseten for real-time inference.
  3. 3Configure camera feeds to send images to Baseten API endpoints.
  4. 4Use Airbyte to pipeline defect data and images to S3/data warehouse.
  5. 5Set up Datadog monitoring for inference latency, accuracy metrics, and alerts.
  6. 6Create dashboard tracking defect rates per line, model performance, and system health.
  7. 7Implement feedback loop: collect false positives/negatives to retrain models monthly.
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