Categories
Healthcare
GenAI
NGO / NPO
Tech Stack
Empowering Researchers with an AI-Driven Knowledge Discovery Platform
A leading scientific and professional organization manages an extensive repository of research articles, journals, and publications, making it one of the most comprehensive databases in their space. They sought to leverage GenAI into their digital platforms, enabling more efficient research discovery, automated summarization, and comparative analysis.
Challenge
Their organization required the ability to integrate multiple LLMs (Azure OpenAI, Claude, Llama, Amazon Titan) for performance comparisons while avoiding dependency on a single vendor.
All research documents, embeddings, and metadata had to remain within their AWS environment while ensuring secure cross-cloud communication.
AI-generated responses needed to be factually accurate, properly cited, and free from fabricated information, requiring a robust retrieval mechanism.
Over 200,000+ research articles in XML format required efficient parsing, metadata extraction, and vectorization for effective searchability.
Researchers needed a seamless and accurate search experience, retrieving relevant, structured, and cited information in real time.
Solution
Designed a user-friendly, secure, AI-powered research discovery platform integrated into the organization's digital ecosystem. This platform enables researchers to efficiently search, retrieve, and analyze over 200,000+ research articles while ensuring factual accuracy, source attribution, and security.
Implemented an orchestration layer in AWS Bedrock to manage multiple LLMs, including Azure OpenAI, Claude, Llama, and Amazon Titan.
Secured cross-cloud communication via AWS Direct Connect, Azure Private Link, and TLS 1.3 encryption.
Applied least-privilege IAM policies to prevent unauthorized AI interactions and ensure compliance.
Developed an automated data pipeline using AWS S3 & Lambda for event-driven document ingestion.
Used ECS-based parsers to extract metadata from XML files and structured research data.
Vectorized research articles using Titan Embeddings and stored them in AWS OpenSearch for optimized retrieval.
Integrated retrieval-augmented generation (RAG) with OpenSearch to provide relevant, cited article snippets.
Leveraged LangChain to format responses, ensuring structured output and source attribution.
Implemented an automated flagging system for responses missing citations, enabling admin review.
Outcome
70% reduction in time spent retrieving and analyzing research articles.
90% accuracy in AI-generated responses with proper citation enforcement.
50% improvement in data processing efficiency for 200,000+ research articles.