Empowering Transportation Professionals with Intelligent Document Search

How Cogninest AI Built a Custom RAG System for Smarter Document Search

April 9, 2025· Amol Walunj

blog

Transform Your Vision with Our AI and ML Specialists

Dive into the world of Natural Language Processing! Explore cutting-edge NLP roles that match your skills and passions.

How Cogninest AI Built a Custom RAG System for Smarter Document Search


Client and Challenge

A leading state level transportation agency manages thousands of regulatory documents, policies, and technical guidelines that professionals rely on daily. However, manual searches through large PDFs and inconsistent document indexing often made it difficult to extract timely, relevant insights.The challenge was clear:

  • Manual document search was inefficient and time consuming.
  • Lack of contextual understanding from conventional search tools resulted in inaccurate responses.
  • Security concerns in enterprise environments demanded compliant user authentication and access control.
  • No scalable monetization model for offering advanced features.

Solution

Cogninest AI built chatbot, a domain-specific Retrieval-Augmented Generation (RAG) based platform tailored to the transportation agency.
By integrating a custom trained Large Language Model (LLM) with real time document retrieval, chatbot offers intelligent insights, streamlines access to regulatory knowledge, and monetizes its offerings with Stripe.

This full-stack solution combines:

  • A Django backend for processing, authentication, and data management
  • A React.js frontend for intuitive querying and document navigation
  • Microsoft Login for enterprise grade authentication
  • Stripe Payment Gateway for secure transactions and subscription based features

Key Features

1. RAG Architecture with Custom LLM

  • Combines retrieval from indexed agency documents with generative AI
  • Handles nuanced queries like "What are agency’s guidelines for rural intersection lighting?

2. Domain Specific LLM

  • Trained on agency regulations, transportation policies, and official documentation
  • Ensures accuracy, context awareness, and reduced hallucination

3. Microsoft Login Integration

  • Secure sign in with enterprise level access control
  • Role Based Access Control (RBAC) for administrators, analysts, and general users

4. Stripe Payment Gateway

  • Supports credit card payments and subscriptions
  • PCI-DSS compliant and suitable for B2B transactions

5. Django Backend

  • Manages user sessions, RAG pipeline, and real time processing
  • Uses PostgreSQL for storing indexed documents and user queries

6. React Frontend

  • Responsive UI for query input, result viewing, and payment management
  • Includes dashboards for usage stats and payment tracking

7. Intelligent Document Processing

  • OCR enabled for scanned documents
  • Upload, search, and summarize custom documents

8. Query History & Bookmarks

  • Save frequently used questions
  • View and download past responses

9. Interactive Dashboard

  • Displays system metrics, document insights, and error analysis
  • Real time monitoring of performance and usage

10. Scalable Cloud Deployment

  • Deployable on AWS, Azure, or GCP
  • Optimized for low latency queries and future expansion into other transport domains

Workflow

  1. User logs in via Microsoft OAuth
  2. Inputs a natural language query (e.g., “Show me the FDOT crash barrier design rules”)
  3. Backend retrieves matching documents from the indexed database
  4. LLM generates a context-aware answer using RAG
  5. Stripe payment check validates access to advanced results (if applicable)
  6. User receives answer with references and options to save, download, or bookmark

Results

  • 70% Reduction in document search time
  • Increased accuracy and trust in regulatory interpretations
  • Enterprise grade security with Microsoft OAuth and RBAC
  • Scalable monetization via subscriptions and pay-per-query
  • Real time insights and analytics for business intelligence and system monitoring

Conclusion

Chatbot represents a leap forward in how transportation professionals interact with regulatory data. With its custom trained AI model, intelligent retrieval system, and seamless enterprise integration, it bridges the gap between complex documentation and actionable insights. Built by Cogninest AI, Pine.ai is not just a product it’s a productivity revolution for agency professionals.

Looking to build your own AI powered domain expert? Let Cogninest AI turn your documents into decisions.

At Cogninest AI, we specialize in helping companies build cutting edge AI solutions. To explore how we can assist your business, feel free to reach out to us at team@cogninest.ai