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To support its AI-driven vision, ScaleFlow needed a scalable and automated way to ingest, process, and analyze large volumes of customer documents on AWS.
To support its AI-driven vision, ScaleFlow needed a scalable and automated way to ingest, process, and analyze large volumes of customer documents on AWS.
ScaleFlow is building a next-generation platform for business and financial due diligence. By combining automated data ingestion with generative AI, the company helps organizations analyze complex company data faster, more accurately, and at scale.
Industry: Financial & Business Due Diligence
To support its AI-driven vision, ScaleFlow needed a scalable and automated way to ingest, process, and analyze large volumes of customer documents on AWS.
To power its AI-driven platform, ScaleFlow needed a reliable way to ingest and process large volumes of customer documents and feed them into an Amazon Bedrock knowledge base.
However, several document formats were not fully supported by the default ingestion capabilities, which led to ingestion failures and limited visibility into processing status. This created challenges in maintaining a reliable AI knowledge base and in scaling the platform to support additional customers and datasets.
CloudZone designed a scalable AI data ingestion architecture on AWS that automates document processing and prepares customer-uploaded data for generative AI models.
Using Amazon S3, AWS Lambda, and Amazon SQS, the solution automatically analyzes uploaded files and routes them to the appropriate processing pipeline. Documents are then ingested into an Amazon Bedrock knowledge base, with vector search powered by OpenSearch Serverless.
The solution leverages Anthropic’s Claude model to automatically ingest, analyze, and reason over customer-uploaded documents, transforming them into structured, actionable insights.
The architecture also converts unsupported file types into compatible formats and enables a secure multi-tenant AI environment, allowing ScaleFlow to scale its GenAI platform across multiple customers.
On the AI engineering side, ScaleFlow was able to get the most out of the LLMs through hands-on experimentation, testing different models, and progressively determining which one to apply at each stage of the process.
The move from human prompting to RAG-based automation, with Amazon Bedrock at the center, made it possible to automate benchmarking, compare outcomes under the same variables, and make faster, more accurate decisions about the most suitable Claude family and version to use at each step.
Based on the scope and outcomes of the project, we estimate the following impacts:
