Associa Streamlines 48 Million Documents with AWS Bedrock
- •Associa achieves 95% classification accuracy across 48 million documents using Amazon Bedrock models.
- •First-page-only processing improves accuracy and reduces operational costs by 50% per document.
- •Evaluation identifies Amazon Nova Pro as the optimal balance between performance and inference expenditure.
Managing nearly 50 million documents across 300 branch offices is a logistical challenge for Associa, North America’s largest community management firm. Previously, employees spent thousands of hours manually sorting through everything from bylaws to insurance certificates. By partnering with the AWS Generative AI Innovation Center, Associa developed a sophisticated classification system that transforms unstructured documents into organized, usable data streams.
The engineering team discovered that feeding the entire document into the AI was actually less effective than analyzing just the first page. This approach reduced "noise"—irrelevant information that confuses the model—while cutting costs by half. They also found that combining visual data with OCR (Optical Character Recognition) significantly improved the handling of "Unknown" documents compared to image-only analysis. This allows the system to read text while also understanding the layout and visual structure of the page.
After benchmarking several options, including Anthropic's Claude models and the Amazon Nova family, Associa selected Amazon Nova Pro. This specific configuration delivers 95% accuracy at a cost of just 0.55 cents per document. This deployment showcases how Intelligent Document Processing (IDP) can eliminate operational bottlenecks, allowing staff to focus on high-value community management instead of administrative filing.
The solution is built using a modular design that integrates seamlessly into existing workflows. By utilizing Amazon Bedrock, Associa can scale their processing power to handle massive volumes of data without maintaining their own complex hardware infrastructure.