Case Study
AI Architecture & Systems Case Study
FinSolve Technologies RAG-RBAC Assistant
Enterprise RAG Assistant with Role-Based Access Control
Role & Architecture
Systems Architect
Release Timeline
June 2025
Core Stack
FastAPI • MongoDB Atlas
Verification
Production Tested
1 / 4
01Executive Overview
A Retrieval-Augmented Generation (RAG) assistant system with Role-Based Access Control (RBAC), developed for FinSolve Technologies. This assistant empowers different departments (Finance, Marketing, HR, Engineering, Executives) to access role-specific financial information through a secure, natural language interface.
02The Engineering Challenge
Operational Problem & Constraints
Critical friction and failure modes observed in existing workflows
FinSolve Technologies faced communication delays and data silos between departments, while strict regulatory compliance required that confidential financial, HR, and strategic documents never be exposed across unauthorized role boundaries.
03Architecture & Technical Solution
System Architecture & Algorithmic Design
Engineering methodology, model selection, and pipeline design
The project implements a decoupled RAG chatbot architecture with RBAC where:
- Users authenticate via JWT with verified organizational roles
- Vector search in MongoDB strictly isolates retrieved document chunks by role permissions
- LangChain orchestrates multi-LLM reasoning (Claude, DeepSeek, Llama, Gemini) with exact source citations
- A modern Next.js frontend renders role-tailored dashboards and document management tools
04Quantified Impact & Benchmarks
Empirical Outcomes & Performance Metrics
Validated benchmarks, latency figures, and operational efficiency
Eliminated departmental communication delays with self-serve instant insights, guaranteed 100% data access isolation and compliance, and increased productivity across all business tiers.
05Core Capabilities & System Features
01
Six-tier granular Role-Based Access Control (RBAC) enforced at database level
02
Vector search and hybrid retrieval strictly filtered by user permission claims
03
Multi-model LLM routing (Claude, DeepSeek, Llama 3, Gemini) via LangChain
04
Automatic source document citation and page-level reference linking
05
Admin dashboard for role assignment, document ingestion, and usage audits
06
JWT authentication and secure encrypted API communication
07Technologies & Frameworks
Production libraries, architectural components, and runtimes used in this system:
FastAPI
MongoDB Atlas
LangChain
Google AI API
OpenRouter
Groq
Next.js
React
TypeScript
Tailwind CSS
NextAuth.js
Shadcn UI
08Artifacts & Verifiable Deliverables
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