RedTeam
RedTeam Harnesses AWS and Generative AI to Achieve 90% Extraction Accuracy and Supercharge Document Processing
Allata partnered with RedTeam to implement an AI-powered, AWS-native solution that reduced document processing from 8+ hours to 15 minutes, achieving 90% accuracy and transforming RedTeam’s platform into a market-leading, efficient, and scalable construction management tool.
Context
RedTeam, a construction management software company, faced a critical bottleneck with manual specification document review consuming up to 8+ hours per document. Our team implemented an AI-driven pipeline using AWS Bedrock and Textract, achieving around 90% accuracy while cutting processing time dramatically. This solution now serves as a key differentiator and potential revenue stream for RedTeam’s platform.
Operational Impact and Transformational Outcomes
The introduction of an AI-powered solution brought dramatic efficiency gains, transforming RedTeam’s document processing capabilities. The solution consistently delivered 89-90% accuracy, efficiently handling complex, multi-thousand-page documents that previously required extensive manual review.
- Competitive Positioning: RedTeam began showcasing this capability to clients as a key differentiator in the construction management software market, meeting a growing industry demand for automation tools.
- Revenue Opportunity: Created potential for a monetizable feature that can be integrated into RedTeam’s platform as a premium service for clients.
- Efficiency Gains: Processing time reduced from 8+ hours to approximately 15 minutes for documents containing 200+ submittal items.
- Unique Complexity: Supported large-scale documents up to 5,000 pages and 1GB in file size. These requirements significantly exceeded the upper bounds of many off-the-shelf solutions.
- Eval-Oriented Fine Tuning: In addition to the core pipeline, our team provided RedTeam with an evaluation and experimentation application as well, so they could continue to measure and refine the solution’s effectiveness over time.
THE CHALLENGE
RedTeam’s reliance on manual processes for extracting specification requirements from large documents posed significant challenges:
- Scalability Concerns: The unsustainable manual approach limited the ability to serve larger clients and higher document volumes effectively.
- Error-Prone Processes: Manual extraction led to inconsistencies and errors, impacting project timelines and client satisfaction.
- Unique Technical Requirements: Needed to support very large documents, far exceeding typical off-the-shelf processing solutions.
OUR SOLUTION
Our team designed a comprehensive AI architecture in AWS, built on a serverless infrastructure-as-code model. This robust extraction pipeline was tailored specifically for RedTeam’s needs.
- Serverless Architecture: Leveraged use case-optimized serverless services within AWS (Lambdas, Step Functions, S3, DynamoDB, Fargate, etc.) for automated scaling, ensuring efficient processing without manual intervention.
- Infrastructure as Code: Deployed using Infrastructure as Code (IaC) methodologies, allowing rapid deployment and updates while maintaining consistent environments.
- Multi-Stage Pipeline: Included document chunking, text extraction, Bedrock AI interpretation, and storage in DynamoDB for seamless platform integration.
- Rigorous Testing: A test harness compared AI-extracted results against manually verified data, ensuring accuracy and facilitating continuous improvement.
- Fully Serverless: The solution was built out almost exclusively with serverless
“This project has not only streamlined our operations but positioned RedTeam as a leader in AI-driven efficiency.”
Patrick Smith
Chief Technology Officer, RedTeam
THE RESULT
RedTeam’s document processing was revolutionized, with complex documents now processed in minutes instead of hours. This achievement not only enhanced operational efficiency but also positioned RedTeam as a market leader.
- Time Savings: Reduced processing from 8+ hours to 15 minutes for complex documents.
- Consistent Accuracy: Maintained 89-90% accuracy across varied document types.
- Market Differentiation: Promoted the AI-powered capability to clients, enhancing the platform’s appeal and creating excitement.
technology
The solution leveraged a comprehensive AWS-native architecture optimized for large-scale document processing and AI-powered extraction.
- Frontend Development: TypeScript, React
- Cloud Infrastructure: AWS Lambda, AWS DynamoDB, AWS S3, Step Functions, SQS, EventBridge
- AI Services: Amazon Bedrock, Textract
- Architecture: Serverless processing pipeline with automated scaling capabilities
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