Building practical AI systems for document intelligence, automation, backend engineering, and secure software.
I am a B.Tech Computer Science student at Brainware University, working across AI engineering, backend development, application security, data analysis, and blockchain.
I believe in learning by doing: building systems, finding security flaws, optimizing backend operations, and turning experimental ideas into reliable, production-ready applications.
I am currently developing a private AI-powered workspace focused on document understanding, information retrieval, research workflows, and intelligent automation.
The project is intentionally kept partially undisclosed while development is in progress.
Key Development Areas:
- 🔍 Source-grounded AI conversations — connecting generated answers with retrieved evidence and source material.
- 📄 Large-document processing — extracting, structuring, and analyzing complex files.
- 🧠 Retrieval-Augmented Generation (RAG) — experimenting with retrieval, ranking, context construction, and grounding.
- 🔒 Data isolation & privacy — maintaining strict boundaries between users, documents, and conversations.
- ⚡ Background-processing pipelines — handling computationally expensive workloads outside request-response paths.
- 💻 AI workspace interfaces — designing clean environments for reading, research, analysis, and interaction.
- 🛡️ Secure multi-user architecture — strengthening authentication, authorization, and access-control boundaries.
- 🌐 Web-assisted information retrieval — combining external information sources with AI reasoning pipelines.
The goal is to explore how retrieval, document intelligence, AI reasoning, and reliable software architecture can work together inside a production-oriented system.
- 01. Improve retrieval and answer grounding
- 02. Process large documents efficiently
- 03. Strengthen document-access controls
- 04. Build a clean and responsive AI workspace
- 05. Reduce unnecessary architectural complexity
- 06. Improve observability, testing, and reliability
- 07. Turn experimental prototypes into production-ready systems
- 🐼 Pandas & Matplotlib
- 🧼 Data cleaning & analysis
- 📄 OCR & document extraction
- 🛠️ Foundry (Forge, Cast, Anvil, Chisel)
- 📈 Chainlink Price Feeds & Chainlink VRF
- 📜 Solidity scripting & contract testing
- 🪟 Windows and WSL
- 🐳 Docker Desktop
- 🐙 Git and GitHub
- 🦙 Ollama (Local LLMs)
- 🔄 n8n automation
- 🌐 Web-search API integrations
- 🔌 REST APIs
- 🐚 PowerShell and Bash scripting
AI · Django · RAG · Document Processing · Information Retrieval · Web Search
An ongoing private project exploring AI-powered document intelligence, retrieval systems, source-grounded generation, automation, and production-oriented backend architecture.
Certain implementation details and product information are intentionally not disclosed publicly.
Solidity · Foundry · Smart Contracts · Testing · Deployment
A collection of Solidity projects developed while learning professional smart-contract workflows with Foundry.
Solidity · Chainlink · Forge Testing · Deployment Scripts
A decentralized funding contract using Chainlink price feeds, automated deployment scripts and unit testing.
Solidity · Chainlink VRF V2.5 · Automation · Foundry
A decentralized raffle system using verifiable randomness, mocks, network configuration and automated testing.
Python · Flask · OpenCV · Tesseract OCR · Pillow
An OCR-based application for extracting digital text from handwritten or scanned images.
Python · Pandas · Matplotlib · SQL
Data-cleaning, visualization and exploratory-analysis projects, including analysis of COVID-19 datasets.
n8n · Gmail · Ollama · Docker · Local AI
Experiments with AI-assisted email categorization and local automation using n8n, Gmail integrations and Ollama models.
| Area | Current Focus |
|---|---|
| AI Engineering | RAG, grounding, retrieval, reranking, and model orchestration |
| Backend Development | Django architecture, APIs, and background jobs |
| Document Intelligence | PDF parsing, OCR, indexing, retrieval, and citations |
| Security | Authentication, authorization, and data isolation |
| Cloud | Deployment, monitoring, and scalable infrastructure |
| Frontend | Responsive AI workspaces and TypeScript |
| Blockchain | Foundry, Solidity, and Chainlink integrations |
| Data | Python, SQL, and data visualization |
Understand the problem
↓
Build the smallest working version
↓
Test it with real inputs
↓
Find architectural weaknesses
↓
Improve security and reliability
↓
Document what matters
↓
Repeat
- Clear architecture over unnecessary abstraction
- Secure defaults over client-controlled trust
- Grounded AI answers over confident hallucinations
- Practical testing over assumptions
- Maintainable code over short-term shortcuts
- Simple interfaces over technical clutter
- Evidence-driven engineering over guesswork
My current objective is to become capable of building complete real-world systems — from interface and backend architecture to AI integration, security, testing, deployment, and infrastructure.
I am particularly interested in projects involving:
AI Systems · Document Intelligence · Information Retrieval · AI Automation · Secure Backends · Developer Tools · Open-Source