NLP based Market Analysis and Matchmaking between Suppliers and Government Demand

NLP based Market Analysis using ML
and Data Analytics

The Challenge

A US-based organization is developing a platform for federal government supply and demand market analysis, aimed at streamlining the procurement recommendation process. The platform is designed to meet the diverse expectations of stakeholders such as government procurers, government primes and venture capitalists (VCs), while addressing the business realities of engaging a rapidly evolving supplier base in the public sector. The goal is to bridge the gap between suppliers and demand by leveraging NLP-driven recommendations to match potential and relevant suppliers to demanders, using machine learning and data analytics through both traditional systems and large language models (LLMs).

Hypothesis

  • Natural Language Understanding technology can build a bridge between supplier and demand vocabulary to build useful match making between government demand and potential suppliers.
  • LLM technology can be used to provide a succinct interface to explore markets in a conversational way, providing insights embedded in large volumes of unstructured documents

Execution

  • The solution necessitated the development of a comprehensive data processing pipeline, along with robust indexing capabilities, to enable intelligent search and recommendation functionalities. This infrastructure supports the platform’s ability to deliver precise, data- driven insights for smart procurement decisions.
  • The solution enables stakeholders to efficiently analyze market trends, even for niche technologies or products, and provide actionable business recommendations based on these insights. This will empower decision-makers to respond quickly and strategically to evolving market dynamics.
  • Matchmaking algorithm using embedding models was developed to calculate similarity between a procurement document (opportunity) and supplier data.
  • Extraction of a Knowledge Graph using advanced NLP models and graph algorithms to match potential suppliers with the demand.
  • LLM based search and recommendation as well as market insight Q & A using GraphRAG and VectorRAG

Outcomes

  • Search engine driven by ML & Deep Learning Algorithms to support semantic searching.
  • Generates personalized and customized data information to suppliers search.
  • Analyses latest trends in a specific market sector based on technology, product or service and recommends suitable business models for the same.
  • Large scale NLP based unstructured document analysis & interference.

Project Highlights

  • Deployed conversational tool drives higher match accuracy and efficient work flow.