Aidena

— AI STACK RECOMMENDATION

AI Building Inspection Vision Stack

Computer vision pipeline for automated defect detection in building inspections with scalable inference, image processing, and defect classification capabilities.

Stays alive for 365 days after the last visit.

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AI Building Inspection Vision Stack

Computer vision pipeline for automated defect detection in building inspections with scalable inference, image processing, and defect classification capabilities.

high confidence

Core Stack ℹ︎

AWS Bedrock

Primary

Managed multimodal models (Claude, Llama) for vision-based defect analysis without managing inference infrastructure. Pay-per-token pricing scales with inspection volume.

$0.50-$5/month

Amazon Nova Pro

Primary

AWS's multimodal model optimized for document and image understanding. Excellent for analyzing building photos, floor plans, and structural defects at $0.0008/1K tokens.

$1-$10/month

Complete the Stack ℹ︎

Baseten

Alternative

Deploy custom computer vision models (YOLO, Faster R-CNN) as production APIs with auto-scaling and GPU support. Ideal for fine-tuned defect classifiers.

$50-$200/month

Airbyte

Alternative

Ingest inspection images, metadata, and defect reports from field apps, cloud storage, or databases into centralized data warehouse for training and analytics.

$0-$50/month

Arize Phoenix

Alternative

Monitor model performance, detect drift in defect detection accuracy, and debug misclassifications in production inspections.

$0/month

Braintrust

Alternative

Evaluate vision model outputs against ground-truth defect labels. Automated scoring and dataset management for continuous model improvement.

$0-$50/month

Getting started

  1. 1Set up AWS Bedrock with Claude or Nova Pro for multimodal analysis of inspection images.
  2. 2Create Lambda functions to receive inspection photos from field app and call Bedrock vision APIs.
  3. 3Deploy Baseten for custom defect detection model if needed (YOLO fine-tuned on building defects).
  4. 4Use Airbyte to sync inspection results, images, and defect reports to S3/data warehouse.
  5. 5Integrate Arize Phoenix for production monitoring of model accuracy and false positive rates.
  6. 6Use Braintrust to evaluate model performance against labeled inspection datasets and iterate on prompts/models.
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