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Letting Data Speak, AI Act!
Case Study
AI-Powered Construction Bid Analysis & Document Intelligence System
About the Client
A technology-forward construction services company specializing in advanced building materials and systems for commercial, industrial, and institutional projects.
Challenge
Construction subcontractors face a critical bottleneck: analyzing 400+ page specifications, validating bid proposals with dozens of scope items, and ensuring compliance - all within tight deadlines. Traditional manual analysis by experienced estimators takes days, creates risk of oversight, and limits bid capacity. Missing a single requirement can result in losses exceeding hundreds of thousands of dollars. The industry lacks intelligent automation for systematic document analysis and bid validation at scale.
Key Results
Reduced specification analysis from 2-3 days to 30 minutes (95%+ accuracy)
Cut bid review time by 80% through AI-powered parallel processing
Increased bid capacity 3-5x without additional staff
Eliminated 70% of the estimator workload on routine validation tasks
Processed 30-40 item bids in 3-5 minutes with comprehensive citations
Flagged 100% of non-compliant exclusions, preventing contract disputes
Solution
A comprehensive system integrating document intelligence with intelligent bid validation, orchestrated through AWS Bedrock on serverless infrastructure:
Phase 1: Document Intelligence (RAG System)
AWS Bedrock Knowledge Base with hybrid vector + keyword search, Cohere reranking
Query expansion: 1 question → 3 parallel queries → 36 chunks → rerank to 12 → dedup
Natural language Q&A with cited references from specifications
Automatic OCR, chunking, version management via DynamoDB GSI
Phase 2: Bid Validation Engine
Semantic segmentation of qualifications with auto-categorization
Grouped processing: Scopes (2/group), Inclusions/Exclusions (4/group)
Per group: Query generation → 18 chunks → rerank to 8 → LLM tool analysis
Multi-status validation: OK (compliant), WARNING (ambiguous), ISSUE (violates)
Citation enforcement in [0], [1], [2] format with validation warnings
Technical Architecture
Serverless AWS: Lambda (API Gateway) → ECS Fargate (processing) → Bedrock (Claude 3.7/4.0) → DynamoDB (jobs, metadata) → S3 (documents, payloads, results). Intelligent storage routing: payloads ≤300KB in DynamoDB, >300KB in S3. Model fallback resilience with automatic retry.
Technologies Used
AWS Lambda (Python) - Lambda is used to execute the chatbot code in a serverless environment.
ECS Fargate - ECS Fargate provides serverless container orchestration for running containerized applications.
Claude 4.5 - Claude 4.5 is used to access the LLM model for generating intelligent responses.
AWS Bedrock Knowledge Base - Bedrock KB is used for the retrieval and storage of vector embeddings.
Cohere Rerank 3.5 - Cohere Rerank 3.5 is used to improve the relevance of retrieved search results.
DynamoDB - DynamoDB stores chat history in one table and configuration/costing parameters in another.
Amazon S3 - S3 buckets are used for source document storage, temporary files, and logging.
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