Real-time, context-aware retrieval using LLMs.
Key Challenges
Intelligent search for data-driven decisions
The client required a solution that could:
Improve the accuracy of information retrieval from large internal databases
Enable context-aware and precise search results
Leverage Large Language Models (LLMs) to interpret complex queries
Ensure reliability and scalability through cloud infrastructure (AWS)
Key components delivered
Retrieval Augmented Generation (RAG) with AWS LLMs
Seargin implemented a cutting-edge RAG-based search engine that integrates traditional data retrieval with LLM-generated natural language responses—delivering semantic precision at enterprise scale.
- RAG-based architecture implementation
- • Designed a hybrid system where relevant documents are first retrieved from structured and unstructured sources.
- • Passed retrieved content to the LLM to generate context-rich responses, rather than returning raw documents alone.
- Self-querying mechanism
- • Integrated self-querying logic where the LLM itself formulates precise, syntactically correct database queries.
- • Reduced dependency on manually written SQL or search queries.
- • Enabled non-technical users to access complex information through natural language.
- Utilization of AWS Bedrock + Llama 3
- • Deployed both Llama 3 8B and 70B models using AWS Bedrock.
- • Chose model size dynamically based on query complexity and performance needs.
- • Leveraged AWS infrastructure to ensure elastic scalability, fault tolerance, and low latency.
Business results
Smarter, Faster, More Satisfying Search
Improved query interpretation
The system accurately understands user intent and delivers responses that reflect real-world context, even when queries are ambiguous or unstructured.
Reduced search time
Users now receive answers in seconds, thanks to intelligent document matching and LLM-based summarization—reducing time spent navigating documents manually.
Higher search satisfaction
Contextual awareness dramatically increased the relevance of search results, leading to better user engagement and decision-making confidence.
Scalable Performance via AWS
Cloud-native design ensures the solution can handle high traffic, massive datasets, and multiple model configurations without performance degradation.
