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Construction Tender Intelligence & Automated Proposal System

We developed an advanced Tender Intelligence platform that goes beyond document extraction to deliver deep contextual understanding, relationship mapping, and automated proposal generation for large-scale construction and engineering tenders.

The system analyzes complete tender ecosystems including documents, drawings, spreadsheets, and annexures builds a knowledge graph of interconnected entities, and enables users to query tenders conversationally and generate compliant proposals in structured, ready-to-submit formats.

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Summary :

We built an AI-powered tender intelligence system for the construction and engineering domain that processes complex, multi-format tender documents and transforms them into structured, queryable, and reusable knowledge.

The platform ingests PDFs, Word files, Excel sheets, images, and technical drawings, extracts critical information, maps relationships using a knowledge graph, and allows users to ask natural language questions across the entire tender corpus. In addition, it automates the generation of technical and commercial proposals aligned with tender requirements, reducing bid preparation effort and risk.

Problem

Problem

Objectives

  • Automate End-to-End Tender Understanding

    Analyze and structure information across all tender documents and formats.

  • Create Relationship-Aware Intelligence

    Build a knowledge graph linking scope, pricing, compliance clauses, suppliers, and timelines.

  • Enable Conversational Tender Queries

    Allow users to ask natural language questions grounded in document context.

  • Automate Proposal Generation

    Generate compliant technical and commercial proposals in structured formats.

  • Improve Speed and Accuracy

    Reduce bid analysis and preparation time while minimizing risk and oversight.

Challenges

  • Handling highly inconsistent document structures across PDFs, spreadsheets, images, and drawings.
  • Extracting and contextualizing data from technical drawings and complex table layouts.
  • Maintaining consistency and traceability across multiple tender packages and annexures.
  • Building a unified system that combines language understanding, vision analysis, and relational reasoning.
  • Ensuring generated insights and proposals remain explainable and auditable for decision-makers.

Solution

  • We built a Tender Intelligence and Proposal Automation system that integrates document AI, vision-language models, and graph-based reasoning into a single workflow.

  • The platform parses all tender-related documents, extracts structured entities such as scope items, pricing components, eligibility criteria, timelines, and contractual clauses, and maps them into a knowledge graph to capture relationships and dependencies.

  • Users can interact with the system via a conversational interface to ask questions like eligibility checks, risk identification, clause interpretation, or scope clarification  with responses grounded in the underlying graph structure.

  • The system also generates automated proposals by assembling relevant content directly from analyzed tender data, ensuring alignment with requirements while significantly reducing manual drafting effort.

Architecture

Document Ingestion
Users upload PDFs, Word files, Excel sheets, images, and technical drawings.
AI Parsing & Entity Extraction
Language and vision models extract scope, pricing, compliance clauses, and metadata.
Knowledge Graph Construction
Entities and relationships are stored in Neo4j for contextual linking and reuse.
Conversational Query Layer
Users query the tender using natural language with graph-grounded responses.
Automated Proposal Generation
AI composes structured technical and commercial proposals based on tender insights.
User Interface
A Streamlit-based dashboard enables uploads, insights review, querying, and report downloads.

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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Santiago Correal Mantilla
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Founder, Sapio AI
My experience with this RAG project was great. The team built a system that delivers accurate, context-aware responses. From summarizing documents to answering complex queries and improving knowledge retrieval, their efficient implementation ensured smooth and reliable performance.
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Brainy Neurals' OCR-based system has made medical report processing much easier. It accurately extracts patient data and summarizes key information, saving us a lot of time and effort. The automation works smoothly and has improved efficiency in handling reports
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Founder, Gemperts India Pvt Ltd
My experience with this project showcased the team’s strong expertise in computer vision and depth estimation. They provided a complete solution, including the app and backend, ensuring seamless integration and high performance.
Billy Brush
Billy Brush
Founder, Darkhorse.AI
The team's attention to detail in data annotation was evident throughout the project. They provided accurately labeled datasets with consistency, ensuring smooth AI model training. Their structured approach made the entire process efficient and reliable.
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