CASE STUDY — NGO, ENERGY AND BEYONG
Natural Language to SQL
Make complex database accessible for all
Sector:
NGO, Energy & Beyond
Focus:
Conversational Data Access
BUSINESS CHALLENGE
Complex data fragmented across multiple systems
Vast and complex data was inaccessible due to fragmentation across multiple systems, making it challenging for users to navigate and obtain the insights needed for planning, resource allocation, and responding to new demands.
SOLUTION IMPLEMENTED
A chatbot turning natural language into SQL queries
Implemented a chatbot that transforms natural language queries into SQL commands, enabling users to retrieve data in an accurate and user-friendly way, enhancing data accessibility and supporting informed decisions.
TECHNOLOGY
Built to store, host, and translate queries into SQL at scale
DATA PLATFORM
Azure Stack
Secures storage and management of extensive data sets, integrating multiple data sources to support real-time data processing and analysis.
SERVING
Azure App Service
Hosts the chatbot with scalability, high availability, and easy access for users globally.
AI
AZURE Foundry
Handles natural language processing, transforming user queries into precise SQL commands for accurate data retrieval.
Results & ROI
What changed once the platform went live
DATA ACCESSIBILITY
Increased the usability of data for anyone needing to navigate specific information, driving more informed, instant responses.
EFFICIENCY
Reduced time spent navigating complex data systems, allowing any user to access insights instantly.
COST SAVINGS
Decreased operational costs by automating data retrieval and reducing reliance on manual processes for information access.
