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AI-Powered Tender Document Analysis Platform for Construction & Real Estate Sectors

zaven.co

Construction

Real estate

Information technology

Challenges in Tender Document Analysis

Inefficient manual bid evaluation processes, AI-generated output inaccuracies risking analysis reliability, lack of source citation traceability in diverse document formats, high operational costs from LLM usage, and need for intuitive interface to ensure user adoption

About the Client

Norwegian technology company combining construction/real estate expertise with AI to optimize project cost, time, and sustainability

Key Project Goals

Develop robust data model for structured tender data extraction
Implement AI-powered prompting system for bid comparison
Create proof-of-concept within 2 months to validate market potential
Deliver fully functional MVP within 4 months for investor readiness
Ensure scalable architecture for future AI enhancements

Core System Capabilities

LLM-powered data extraction from PDFs and structured documents
Semantic chunking for context-aware analysis
Interactive bid comparison dashboard
Source citation traceability with document highlighting
User-friendly interface with visual hierarchy for complex data

Technology Stack Requirements

Azure AI Search

Azure OpenAI

React frontend

.NET backend

Bootstrap UI framework

System Integration Needs

Microsoft Document Intelligence
Cloud-native CI/CD pipeline
Vector database connectors

Operational Requirements

Cost-optimized LLM token usage
High-accuracy semantic search capabilities
Enterprise-grade data security
Horizontal scalability for document processing
99.9% system uptime SLA

Expected Business Outcomes

200% faster tender analysis workflows, 40% reduction in bid evaluation errors, 60% lower operational costs through optimized LLM usage, and foundation for AI-driven construction project optimization platform