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Construction Tender Analysis and Data Extraction System

We developed a document intelligence platform that automates the complex process of analyzing, structuring, and assessing construction tender documents.
 The system uses AI to extract critical details, evaluate risks, and generate structured insights from multi-format tender files enabling faster, more accurate decision-making in large-scale construction projects.

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Summary :

We have built an AI-based tender analysis system designed specifically for the construction and engineering industry. The platform processes diverse document types (PDFs, Word files, Excel sheets, and technical drawings) to extract scope details, pricing information, supplier data, and compliance risks. By combining text understanding, vision-language models, and graph-based knowledge management, it transforms unstructured tender documents into actionable intelligence for procurement and project teams.

Problem

Problem

Objectives

  • Automate Tender Analysis

    Extract and structure data from multi-format documents.

  • Identify Compliance Risks

    Detect red flags and deviations from tender requirements.

  • Enable Knowledge Reuse

    Store extracted data in a graph database for future reference.

  • Improve Efficiency

    Reduce manual review time while maintaining accuracy.

  • Enhance Decision-Making

    Deliver structured insights for pricing, supplier, and scope analysis.

Challenges

  • Handling inconsistent document formats across PDFs, Word, and Excel files.
  • Extracting relevant data from technical drawings and multi-column layouts.
  • Ensuring data consistency across different tender packages.
  • Integrating AI text, vision, and relational understanding within one workflow.
  • Building an interpretable, structured output usable for procurement decisions.

Solution

  • We built a document intelligence system that combines natural language processing, computer vision, and graph-based reasoning to automate tender document analysis.
  • Using LangChain for orchestration and Ollama models (Qwen3 8B, Qwen2.5VL) for text and visual understanding, the system parses tender files, extracts structured data, and identifies potential risks.
  • It leverages a Neo4j graph database for linking project data, suppliers, and risks , enabling powerful search and cross-referencing capabilities.
  • The intuitive Streamlit web interface and FastAPI backend allow users to upload multiple documents, view extracted insights, and download structured reports instantly.

Technologies

Architecture

Document Ingestion
Users upload PDF, Word, Excel, or image-based drawings.
AI Parsing & Extraction
Language and vision models process files for data, scope, and pricing details.
Risk Assessment
AI identifies compliance or contractual risks.
Knowledge Graph Integration
Extracted entities stored in Neo4j for cross-reference and search.
User Interface
Streamlit dashboard displays summaries, insights, and downloadable reports.

Results & Impact

Automated extraction of technical and financial data from complex tender files

Reduced analysis time for multi-document tenders by 70%

Improved risk visibility through automated compliance and anomaly detection

Enabled structured knowledge management using graph-based linking

Delivered consistent, data-driven insights for procurement and project planning

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