AI Research Assistant Platform
Context-aware paper analysis and visualization for researchers and medical professionals — extracting, summarizing and visualizing critical insights from clinical and scientific papers.
- Python
- Django
- LangChain
- FAISS
- Neo4j
- React.js
- Industry
- Healthcare & life sciences research
- Service
- AI / LLM development (RAG)
- Platform
- Web (Django + React)
- Cloud
- Azure
Faster literature review with retrieval-augmented AI
We developed an intelligent AI-driven platform to assist researchers and medical professionals by extracting, summarizing, and visualizing critical insights from clinical and scientific papers. This system streamlines literature review by using semantic search, trial phase extraction, and visual entity mapping.
The platform uses LLM-based Retrieval-Augmented Generation (RAG) to process research content and provides summarized introductions, methodology analysis, trial phase classification, and entity extractions with confidence scoring and editable results.
From hours of reading to minutes of insight
The challenge
- Literature review across clinical and scientific papers was slow and manual.
- Trial phases, drugs, biomarkers and endpoints were buried in long documents.
- Relationships between drugs, trials and entities across papers were hard to see.
- Researchers needed to trust — and correct — AI-extracted results.
Our solution
- LLM-based RAG pipeline with LangChain, FAISS vector search and reranking.
- Trial phase detection and entity recognition for drugs, biomarkers and endpoints.
- Neo4j knowledge graph with interactive React visualizations.
- Confidence scores and editable fields on every extraction.
The result
- Streamlined literature review process.
- Quick extraction of key insights and trial phase classification.
- Entity relationship discovery across papers.
- Custom report saving and export.
An AI research assistant built for clinical papers
AI summarization engine
GPT-like models summarise introductions, methodology and key findings.
Trial phase detection
Detects and classifies clinical trial phases.
Semantic search & copy-paste parsing
Search by meaning, or paste raw text for instant parsing.
Vector search & retrieval
FAISS-powered similarity search across research content.
Graph-based visualization
Neo4j knowledge graphs link drugs, trials and entities.
Confidence scores & editable fields
Every extraction carries a confidence score and can be edited.
Search, extraction and visualization in depth
AI summarization
- GPT-like models for content analysis
- Reranking for improved accuracy
- Concise summary extraction
- Methodology analysis
- Key findings identification
Trial phase detection
- Phase 1/2/3 classification
- Clinical trial outcomes
- Study metadata extraction
- Trial phase highlighting
- Outcome analysis
Entity recognition
- Drug name extraction
- Biomarker identification
- Endpoint detection
- Medical terminology
- Entity relationship mapping
Semantic search
- Context-aware parsing
- Raw text processing
- Full paper upload support
- Entity recognition
- Real-time similarity search
Knowledge graph
- Dynamic relationship charts
- Drug-trial-entity linkage
- Interactive visualizations
- Cross-paper connections
- Graph-based insights
Technology stack
Python and LangChain on the backend, FAISS and Neo4j for retrieval and graphs, React for interactive dashboards — hosted on Azure.
Backend & AI
- PythonCore language
- Django RESTAPI framework
- LangChainLLM integration
- Hugging FaceTransformers
- OpenAI APILLM models
- Azure RAGRetrieval-augmented generation
Data & storage
- FAISSVector storage
- Neo4jKnowledge graphs
- Azure BlobDocument storage
- Document IntelligenceStructured parsing
Frontend & cloud
- React.jsInteractive dashboards
- D3.js / Chart.jsVisualizations
- Azure App ServicesHosting
- JWT authResearcher / admin roles
The expertise behind the platform
Advanced AI/ML integration, real-time processing, scalable architecture, confidence scoring and interactive visualizations.
Python & Django
Backend development with RESTful APIs.
LangChain
LLM integration and RAG architecture.
FAISS
Vector database and similarity search.
Neo4j
Knowledge graph and relationship mapping.
React
Interactive frontend with data visualization.
Azure Document Intelligence
AI-powered document processing.
Building an AI assistant for your domain?
Let's discuss how RAG, semantic search and knowledge graphs can turn your documents into answers.