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A Conversational AI Data Analyst powered by a custom RAG pipeline and locally-hosted LLMs. Engineered to stitch fragmented multi-page tabular data from unstructured PDFs into coherent semantic narratives, enabling natural language queries against complex financial and operational datasets without cloud dependency.
A specialized AI conversational expert that lets you query complex, multi-page tabular data in natural language.
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Secure AI-powered platform tailored for privacy-first data interaction.
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Problem Synthesis
Standard AI chatbots and RAG (Retrieval Augmented Generation) systems fail to understand context when tables span across multiple PDF pages, often giving incorrect answers or 'hallucinating.'
Key Challenges
- 01.Chatbots losing context when a table breaks across pages
- 02.Inability to query specific row-level data using natural language
- 03.General-purpose LLMs struggling to interpret raw, disconnected tabular text
Project Objectives
Goal Create a 'Data Analyst' chatbot capable of answering complex questions from messy, multi-page PDF tables
Goal Bridge the gap between unstructured PDF data and conversational AI
Goal Ensure data privacy by processing sensitive queries locally
Specialized 'Table-Stitching' RAG pipeline that feeds coherent table narratives to the chatbot
Natural Language Interface where users can ask questions like 'What was the Q3 revenue for Department X?' and get accurate answers
Local LLM Integration (Llama 3) for privacy-first, offline reasoning
Interactive Chat UI that handles PDF upload, processing, and conversation in real-time
Empowered users to instantly uncover insights from massive financial and operational reports without manual searching
Achieved high accuracy in complex queries by preserving table context before the LLM sees the data
Eliminated the need for manual data entry, allowing direct 'Talk-to-Data' workflows
Technical Deep Dive
Backend / AI
01- Python
- LangChain (Conversational Logic)
- Ollama (Local LLM - Llama 3)
- RAG Architecture
- pdfplumber & PyPDF2
Frontend
02- React (Vite)
- Tailwind CSS
- Framer Motion
Conversational AI
01- Context-Aware Chat
- Complex Query Understanding
- Multi-Turn Conversations
Data Processing
02- Multi-Page Table Stitching
- Intelligent Header Recognition
- Semantic Data Narratives
User Experience
03- Drag-and-Drop PDF Upload
- Real-time Processing Status
- Secure, Private Chat Session
Data Integrity
01- End-to-End Encryption (AES-256)
- Zero-Training Policy (Data not used for learning)
- Ephemeral Processing Sessions
Enterprise Compliance
02- SOC 2 Ready Infrastructure
- Single-Tenant Isolation