Ravi Kumar
Full-Stack · AI · Cloud
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Full-Stack | AI EngineeringRAG & LLM Production Workflow

AI Document Processing Platform

End-to-End Intelligent Document Parsing, RAG & Semantic Workflow Engine

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The Engineering Challenge

Problem Context

Enterprise teams lose countless hours manually reviewing lengthy technical manuals, contracts, and financial reports. Traditional keyword search misses context, while basic LLM prompts suffer from context size limits and hallucinations.

The Architectural Blueprint

Solution Delivered

Architected a production RAG pipeline using FastAPI, LangChain, and vector embeddings paired with a responsive Next.js document workbench that enables real-time citation tracking, structured table extraction, and contextual questioning.

Interactive Architecture & Topology
System Topology & Interaction Flow

AI Document Processing Platform — Production Architecture

Multi-tier Decoupled Flow
Next.js Document Workbench

Interactive Viewer & Prompt UI

FastAPI Backend

Auth, Orchestration & Async Task Dispatch

Document Ingestion Worker

OCR, Layout Analysis & Parsing

Vector Database (Qdrant)

High-dimensional Embeddings & Search

LangChain RAG Engine

Context Assembly & Prompt Synthesis

LLM Provider (Claude/OpenAI)

Generative Reasoning & Citations

Dockerized Deployment

Containerized Cloud / On-Prem Runner

Execution Flow Sequence (6 Steps)
Flow Stage 1

1. User uploads unstructured documents via the Next.js document workbench.

Direct citation attribution allowing users to audit every AI claim against source coordinates
Chunking strategies tailored to preserving table hierarchies and technical terminology
Pluggable LLM and embedding backends supporting on-prem or cloud-hosted models
Leadership & Hands-on Scope

My Engineering Contributions

Engineered the complete application architecture across UI, ingestion pipelines, and AI layers.
Built resilient document parsing microservices handling diverse formats (PDFs, DOCX, scanned reports).
Designed semantic chunking and embedding pipelines connected to high-dimensional vector search.
Implemented Retrieval-Augmented Generation (RAG) with source citation verification to eliminate hallucinations.
Developed interactive Next.js document workbench with inline citation highlighting and PDF viewers.
Engineered secure REST APIs with FastAPI, rate limiting, and background processing workers.
Containerized the system with Docker for scalable on-premises or cloud deployments.
Platform Features
Multi-format document ingestion (PDF, DOCX, scanned images, txt)
Intelligent semantic chunking with metadata preservation
Vector search indexing for sub-second semantic retrieval
Conversational QA with verifiable source citation pointers
Structured table and entity extraction
Interactive document workbench with side-by-side verification
Dockerized container deployment with configurable LLM providers
Complete Tech Stack
PythonFastAPILLMs (Claude / GPT-4)Vector Databases (Qdrant)LangChainReact / Next.jsTypeScriptDockerTailwind CSS

Need a similar production-ready system?

I can help architect, build, and deploy your entire application stack — with high availability, modern security, and automated cloud infrastructure.

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