GenAI Search Solution to Retrieve Insights From Large Datasets
A GenAI Search solution that transforms how a global intergovernmental organization interacts with vast, complex datasets, providing instant, precise, and context-aware insights for gender equality and policy decisions.
Client
An international intergovernmental organization operating in over 150 countries, focusing on governance, environment, poverty, gender equality, and women's empowerment.
Problem Statement
The organization’s reliance on conventional keyword-based search consistently failed to retrieve relevant, precise insights from vast, complex, and cross-domain datasets. This inefficiency, coupled with a lack of technical expertise required to execute precise searches, led to slow and unreliable information retrieval, severely hampering real-time decision-making and policy reporting.
Industry
Non-profit
Interngovernmental Organization
Quick Summary
QBurst developed a sophisticated GenAI search module that integrates LLMs, RAG, and dynamic SQL generation to simplify access to complex policy and financial data.
- Intelligent Retrieval: The system automatically routes natural language queries to the most appropriate engine (text or database) for instant, context-aware results.
- Decision Acceleration: Policymakers and analysts can efficiently retrieve insights on funding partners, financial reports, and systemic outcomes, replacing time-consuming manual effort.
- High Precision & Context: Leverages Retrieval-Augmented Generation (RAG) to ensure search results are precise and grounded in the organization’s domain-specific knowledge base.QBurst developed a sophisticated GenAI search module that integrates LLMs, RAG, and dynamic SQL generation to simplify access to complex policy and financial data.
Client
International intergovernmental organization that operates in over 150 countries, working in areas such as poverty, governance, and environment, with a strong focus on gender equality and women's empowerment.
Challenges: Getting the Right Match
- Ineffective Keyword Search: The existing keyword-based search frequently returned incomplete or irrelevant results from a vast database spanning poverty, governance, and gender domains.
- Data Fragmentation and Volume: Managing large-scale databases containing both structured (financial, KPI) and unstructured (reports, policies) data made precision querying difficult, requiring manual effort.
- Technical Barrier to Data: Users lacked the technical expertise needed to execute precise SQL searches, creating bottlenecks and dependency on specialized teams.
- Response Time and Reliability: Inefficiencies in query execution led to high processing times, making information retrieval slow and unreliable, which hampered real-time decision-making.
GenAI-based Search Solution
QBurst developed a multifaceted GenAI search solution for the client’s Transparency Portal, enabling information retrieval through natural language queries without requiring technical knowledge of database structures. The system intelligently determines the most accurate and efficient method to process each request. The core of the solution relies on Azure OpenAI for generative capabilities and Retrieval-Augmented Generation (RAG) for contextual accuracy.
Key solution phases:
- LLM Router, Text & SQL Engine: Integration of a sophisticated GenAI system that routes queries to the appropriate processing engine (vector-based similarity search for text analysis or dynamic SQL generation for structured data).
- SQL Generator: Development of a natural language to SQL query generator that translates user requests into precise database queries.
- Context Management (RAG): Implementation of vector-based similarity search to quickly identify relevant information from the knowledge base, ensuring results are context-aware and grounded in proprietary data.
Technical Highlights
- Intelligent GenAI Query Routing: Automatically determines the most appropriate processing method (Text-to-SQL or RAG/Similarity Search) for each complex query.
- Vector Search Implementation: Utilized vector-based similarity search (pgvector) and tools like LangChain and LlamaIndex to quickly identify relevant information from vast knowledge bases.
- Multilingual Support: The NLP pipeline was designed for seamless handling of conversational queries in multiple languages.
- Enterprise Security: Integrated with Azure API Management and ensured enterprise-grade security for sensitive data throughout the processing pipeline.
Impact: Optimal Search Results with GenAI
The GenAI search solution delivered significant, measurable gains in speed and efficiency for policy analysis:
- Accelerated Information Access: Reduced the time required to retrieve complex, cross-domain information by 60%.
- Multilingual Inclusivity: Significantly improved access for non-English speaking team members, leading to a 45% increase in cross-organizational knowledge sharing.
- Improved Donor Reporting: Ensured accurate and timely updates for stakeholders, reducing manual data validation time for reports by 50%.
- Enhanced Decision Support: Improved data accessibility and streamlined complex queries, enabling evidence-based decision-making.
- Future-Ready Scalability: Established a secure and scalable foundation that complies with data security regulations and is ready for future innovations like predictive insights.
Client
Challenges
QBurst Solution
Technical Highlights
Impact