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Hybrid RAG System for Competitive Intelligence

Introduction

In a highly competitive industry, making informed strategic decisions requires a deep understanding of competitors’ growth strategies. Our client faced the challenge of analyzing both structured and unstructured data from multiple sources. Golden Eagle IT Technologies partnered with the client to implement a Hybrid Retrieval-Augmented Generation (RAG) system, combining semantic search with structured data retrieval to provide actionable business insights.

Problem Statement

The client’s challenges included:

  • Scattered data sources : Internal strategy documents, public competitor reports, news articles, blogs, and customer reviews.
  • Structured business data : Sales, pricing, and product data stored in databases.
  • Lack of unified analysis : No mechanism existed to analyse unstructured and structured data together for strategic decision-making.

These limitations slowed strategic planning and prevented real-time, data-driven insights.

Solution

Golden Eagle IT Technologies developed a Hybrid RAG system—an AI-powered business advisor that merges semantic search with structured data retrieva

Data Collection:

  • Gathered internal documents (PDFs, strategy decks) and public data (blogs, market reports).
  • Retrieved structured business data (sales, pricing) from MySQL.

Text Processing:

  • Extracted and chunked unstructured data using PyMuPDF with overlapping windows to preserve context.

Embeddings & Semantic Search:

  • Generated embeddings using OpenAI’s text-embedding-ada-002 and MiniLM.
  • Stored embeddings in Milvus for vector similarity search.

Hybrid Query Workflow:

  • User queries are semantically matched via Milvus for text chunks.
  • SQL filters (e.g., region = India) applied to MySQL.
  • Unstructured and structured insights are merged before feeding to GPT.

RAG + GPT Integration::

  • Combined both datasets and fed them into GPT for actionable business recommendations.
  • Example output:Your competitors use freemium AI APIs for developers in India. You currently use fixed pricing. Consider launching a starter tier.

Feature List

01
Unified access to structured and

unstructured competitor data.

02
Semantic search

for precise retrieval of insights. .

03
Automated merging

of database queries with text-based analysis.

04
AI-generated

strategic recommendations.

05
Scalable architecture

supporting multiple query types and data sources.

Tech and Solution Stack

Backend Framework

FastAPI

Vector Database

Milvus for semantic search

Database

MySQL for structured data

LLM Integration

OpenAI GPT for recommendations

Support Libraries

PyMuPDF, LangChain, SQLAlchemy, Hugging Face

Hosting

The system is deployed in a cloud-native environment, ensuring scalable access to internal and external data sources while maintaining performance and security.

Team & Support

  • Team Composition : 2 AI/ML engineers, 1 backend developer, 1 data engineer, 1 project manager.
  • Support : Ongoing system monitoring, regular updates to embeddings and structured data, and retraining pipelines to improve AI recommendations.

Maintenance

  • Continuous updates to internal and public data sources.
  • Monitoring of vector search performance and database queries.
  • Periodic testing and tuning of embeddings and RAG workflows to ensure accurate results.
  • Documentation and training for client teams to use and extend the system.

Conclusion

By implementing the Hybrid RAG system, Golden Eagle IT Technologies empowered the client to make data-driven strategic decisions :

  • Enabled3 strategic initiatives 2 months
  • Achieved a 13% revenue increase in the following quarter.
  • Transformed fragmented data into actionable business insights.
  • Built a scalable, AI-powered system that integrates structured and unstructured data seamlessly.

This project demonstrates how combining LLMs, semantic search, and structured data retrieval can turn raw data into a competitive advantage.