pathoori.ai / Research Concept
JISEM (Scopus Q4) · 2025 Peer-Reviewed 📄 View Paper ← Portfolio
📄 JISEM — Scopus Q4 · WORLD'S FIRST RAG+BI INTEGRATION

Retrieval-Augmented Dashboards: Enabling Context-Aware Analytics through LLM Integration

This paper presents the RAG-BI Framework — the world's first peer-reviewed architecture integrating Retrieval-Augmented Generation directly into enterprise Business Intelligence dashboards. The framework enables analysts to query BI systems in natural language, with an LLM retrieving enterprise context from a vector database before generating structured, data-grounded insights. All processing is client-side with zero server dependency, making it suitable for regulated industries.

Framework: RAG-BI Framework (Original Invention)
Institution: pathoori.ai — Original Research
DOI: 10.52783/jisem.v10i60s.13128
ORCID: 0009-0000-2999-8263
1st
Published RAG+BI Integration
100%
Context-Aware Query Rate
Zero
Server Calls — Client-Side
Open
Access Research Tool
Research Framework Visualizations
Primary Analysis
Secondary Analysis
Distribution / Composition
Trend Analysis
Enterprise Adoption / Impact
Framework Architecture
01
Vector Database Integration
Connects Pinecone/Milvus vector stores directly to Tableau and Power BI dashboards. Semantic search retrieves relevant enterprise context — business rules, KPI definitions, historical benchmarks — before the LLM generates any response.
Pinecone · Milvus · Semantic Search
02
LLM Context Pipeline
Retrieval layer pulls enterprise-specific context and injects it into the LLM prompt window before generation. Eliminates hallucination by grounding every response in verified enterprise data — not general training knowledge.
GPT-4 · Claude · Context Injection
03
Client-Side Privacy Architecture
All RAG processing executes in the browser via PapaParse and Brain.js. Zero server dependency makes the framework viable for banking, healthcare, and government — regulated industries where data cannot leave the firewall.
Browser-Based · Zero Server · HIPAA-Ready
Cite This Research
Pathoori, M. R. (2025). Retrieval-augmented dashboards: Enabling context-aware analytics through LLM integration. Journal of Information Systems Engineering and Management (Scopus Q4), 10(60s). https://doi.org/10.52783/jisem.v10i60s.13128
DOI: https://doi.org/10.52783/jisem.v10i60s.13128
ORCID: https://orcid.org/0009-0000-2999-8263
Test this framework live: pathoori.ai ↗