Case study

Multi-Agent AI for Tender Optimization

The Problem:

Strategic personnel were overwhelmed by the manual analysis of lengthy tender documents, spending 70% of their time on administrative tasks, which led to frequent human errors, compliance risks, and a low volume of bid submissions.

Our Solution:

To build a collaborative multi-agent AI system that automates web scraping, document parsing, and requirement extraction, allowing the team to focus purely on strategic pricing and competitive positioning instead of manual data entry.

Technology Stack:

FastAPI, BeautifulSoup, Selenium, LangChain, PyMuPDF, LangChain, OpenAI API, PostgreSQL

Key Results & Value:

4x Submission Volume: Preparation time was reduced from days to minutes, enabling the client to respond to 4x as many opportunities.
Improved Win Rates: The AI eliminated human error in compliance checking, ensuring near-100% accuracy and reducing disqualification rates.
Operational Efficiency: Redirected 70% of staff time from administrative tasks to high-value strategic analysis and optimal pricing.
Competitive Advantage: Established a consistent, fast, and highly accurate bidding process in a previously manual-heavy industry.

Project Overview:

This project focused on developing an AI-powered Tender Optimization Assistant. The system’s core lies in its multi-agent architecture, where several independent AI agents collaborate to streamline the complex tender process. These agents automate critical tasks such as in-depth document analysis, efficient web scraping for tender opportunities, and precise information extraction from extensive tender documentation.

FastAPI

BeautifulSoup

Selenium

LangChain

PyMuPDF

OpenAI API

PostgreSQL

Stack:

Web Framework: FastAPI
Multi-Agent System Technologies:
- Web Scraping Agent: BeautifulSoup, Selenium
- Document Parsing and Chunking Agent: LangChain, PyMuPDF
- Information Extraction Agent: LangChain, OpenAI API
- Analysis and Evaluation Agent: LangChain, OpenAI API, PostgreSQL
Data Handling and Storage: PostgreSQL

Solution:

To improve the tender application process, we implemented a multi-agent AI system through the following structured steps:

Requirements Gathering and System Design: We began by collaborating closely with stakeholders to clearly define project objectives, including the types of tenders the system would handle and the desired outputs. The system architecture was designed with a robust multi-agent framework and microservices to ensure modularity and scalability. We identified key input formats such as PDFs and website URLs and specified the expected outputs, including lists of required documents and comprehensive tender summaries. 
Web Scraping Module: We developed intelligent web scraping scripts tailored to common tender portals. These scripts automatically extract essential tender metadata and links to relevant documents. The raw data collected is then stored securely in PostgreSQL for further processing.

Document Parsing and Preprocessing: The system incorporates a module for efficient PDF parsing using PyMuPDF. Once documents are ingested, the text is normalized to remove inconsistencies and noise. To optimize analysis by subsequent agents, documents are intelligently split into semantically meaningful chunks, and embeddings are generated using LangChain.
Multi-Agent System Setup: We defined the specific responsibilities of each AI agent within the system. To facilitate seamless collaboration and data exchange between these agents, we established robust inter-agent communication using FastAPI microservices.
Information Extraction: Leveraging the power of LangChain and the OpenAI API, the Information Extraction Agent is designed to identify critical information within the processed tender documents. This includes accurately detecting lists of required documents, key deadlines, specific conditions, and other vital details necessary for a successful tender application.

Postprocessing and Output Generation: The system aggregates the outputs from the various AI agents into a structured and easily understandable result. This typically includes a clear list of all required documents and a concise, comprehensive summary of the tender.
Evaluation and Fine-tuning: To ensure the accuracy and reliability of the system, we implemented a rigorous evaluation process involving manual validation of the generated outputs. Based on the feedback received, we iteratively refine the prompts used by the AI models and the document chunking strategies to continuously improve performance.
Deployment: The final solution, comprising the interconnected microservices, is deployed using Docker for efficient containerization and management. We also optimized the system for rapid response times and implemented appropriate API rate limits to ensure stable and reliable operation.