AI-powered knowledge retrieval with RAG and API management
CEI implemented a scalable AI solution to improve knowledge retrieval accuracy, handle complex queries, and support domain-specific user needs across multiple data sources.
Enhanced retrieval accuracy
Improved response quality for complex queries
Scalable API architecture
Managed performance and usage across applications
Reliable AI responses
Consistent results across structured and unstructured data
Company Overview
A global standards organization supports the oil and natural gas industry by providing technical guidance and specifications across a wide network of companies. The organization manages extensive domain-specific content, including technical documents, charts, and complex datasets.
The Challenge
Complex data landscape
Highly technical content including charts, tables, and calculations
Low retrieval accuracy
Difficulty generating precise responses from domain-specific data
Fragmented data sources
Information spread across multiple systems
Scalability limitations
Need to support growing AI usage while managing cost and performance
The Solution
CEI designed and implemented a Retrieval-Augmented Generation (RAG) solution using Azure AI Search, Azure Functions, and API Management to improve knowledge retrieval and response accuracy. The platform integrates multiple data sources, applies advanced retrieval patterns, and delivers context-aware responses to complex, multi-part user queries. A scalable API architecture ensures performance, governance, and cost control across AI applications.
RAG-based knowledge retrieval using Azure AI Search
Domain-specific tuning for improved response accuracy
Integration of structured and unstructured data sources
Scalable architecture for performance and cost management
API Management with GenAI Gateway for controlled access
Results
Enhanced retrieval accuracy
Improved precision in answering complex, domain-specific queries
Improved performance & reliability
Stable and scalable API-driven AI solution
Controlled scalability validation workflows
Managed usage through token limits and API governance
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