AI Transformation Use Case: Secure RAG Search for a Global Climate Research Non-Profit
Title: Secure RAG Search for a Global Climate Research Non-Profit
Subtitle: Unlocking 18 years of climate data through secure, AI-powered search
Short Description:
A global climate research non-profit with over 400 scientists and 18 years of proprietary data transformed its knowledge base with a secure Retrieval-Augmented Generation (RAG) search and chatbot interface. The system delivers intuitive, conversational access to climate insights while ensuring strict protection of sensitive research data.
Budget
- Build: $50,000–$55,000 (enterprise-grade implementation and compliance)
- Maintain: $2,500–$5,000/month (hosting, monitoring, updates, and model optimization)
- Token Costs: ~$200–$400 per 10,000 queries (varies by model and usage scale)
Problem
The non-profit’s research platform, serving governments, NGOs, multinationals, and universities, faced three core challenges:
- Data Access & Usability: Thousands of studies and datasets were locked behind complex Boolean searches and document codes, frustrating researchers.
- Global Reach with Specialized Needs: Stakeholders needed semantic, domain-specific search (e.g., “Show me research on rising tidal levels in Oceania”), which legacy tools couldn’t support.
- Data Sensitivity & Privacy: Proprietary research could not be exposed to public AI models or external training data. A secure, compliant solution was critical.
Solution
We built a custom RAG database and chatbot interface, combining advanced AI with enterprise-grade security:
- Secure Data Management: Proprietary research integrated into a private Amazon Aurora PostgreSQL database. AI models deployed via AWS Bedrock to ensure no public data exposure.
- Custom RAG Workflow: LangChain enabled dynamic RAG pipelines, retrieving relevant documents and generating accurate, referenced responses.
- AI Chatbot Interface: Anthropic Claude powered a natural-language chatbot delivering summaries, full-document references, and direct answers.
- Access Control: Gated interface with secure authentication, ensuring only verified researchers and clients could access data.
- Scalability & Adaptability: Built on AWS infrastructure, future-proofed to handle global query volumes and new datasets.
Results
- Enhanced Usability: 85% reduction in research retrieval time; Boolean searches eliminated.
- Global Accessibility: Researchers and clients across 80+ countries accessed real-time insights with ease.
- Data Security: Proprietary research remained fully secure; no data leakage into public AI models.
- Productivity Boost: Scientists and clients spent more time on analysis and action, less on searching.
- Future-Proofing: Architecture scaled seamlessly to accommodate more queries and new datasets.
Technologies and Tools Used
- Database & Infrastructure: Amazon Aurora PostgreSQL, AWS Bedrock, AWS IAM
- AI & RAG Workflow: LangChain for retrieval pipelines, Anthropic Claude for natural language interaction
- Authentication & Access Control: AWS Cognito + custom paywall integration
- Interface: Custom chatbot with secure web UI, built for multilingual use
- Scalability: AWS global infrastructure for high availability and compliance
