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🚀 Automated Lead Management System
I built an automated lead management workflow using n8n that captures incoming leads, stores them in Google Sheets, and instantly notifies the sales team on Telegram.
Capture → Enrich → Score → Save → Notify
Built to reduce manual work, improve response time, and prevent missed leads.
#AI #Automation #n8n #CRM #LeadManagement
Nice work sir,
Sir we can Build this normally with App script. I just Built yesterday.
Where Google sheet will received the leads from any channel + store and distribute as per described assignment rules and notify with lead details to respected person.
"Thanks sir! 🙏 Yes, Apps Script is a solid route for capture → store → notify. I went with n8n + an LLM layer because I wanted AI lead scoring and spam filtering on top, plus retry + error monitoring so no lead silently drops. Different depth, different tools. Happy to compare notes anytime!"
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I built DocuMind — a financial document automation platform for Chartered Accountants
One thing I noticed while working on financial-document workflows was that a lot of a CA's time can go into repetitive document processing rather than actual professional decision-making.
So I started building DocuMind — a tool designed to automate the repetitive parts of working with financial documents.
💡 The idea
A CA uploads a financial document such as a bank statement, and DocuMind handles the mechanical processing:
Document → Extraction → Normalization → Transaction Processing → Rules → Validation → Structured Output → Excel
Instead of manually going through large documents and preparing the same data repeatedly, the system converts the information into structured, usable outputs.
What I built:
- PDF/document extraction
- OCR for scanned documents
- Transaction extraction and normalization
- Python-based rule processing engine
- Data validation and reconciliation
- Automated Excel generation
- Database-backed document management
- Authentication and user-level document access
- RAG/AI assistant architecture for document Q&A
- Local AI support through Ollama for privacy-sensitive workflows
- React frontend + FastAPI backend
Architecture:
React Frontend
↓
FastAPI Backend
↓
Document Processing Orchestrator
↓
Extraction / OCR
↓
Normalization
↓
Transaction Processing
↓
Rule Manager
↓
Validation & Reconciliation
↓
Structured Results
↓
Excel / CA-ready Output
The AI layer is intentionally not responsible for deterministic calculations.
Python handles things that should be predictable and reproducible, while AI/RAG is used where natural-language understanding and document interaction actually add value.
Why I'm building it:
I recently discussed the idea with a CA and got an important piece of feedback:
The goal shouldn't be to replace the CA's professional judgment — it should be to eliminate the repetitive work around it.
That changed the direction of the product.
I'm now refining DocuMind around workflow automation, accuracy, traceability, privacy, and useful outputs rather than trying to make AI “diagnose” financial information.
What's next:
I'm currently working on:
Improving the CA-focused workflow
Better document extraction
More robust validation
Source/page-level traceability
Better Excel outputs
Testing the workflow with real CAs
Expanding into additional financial-document workflows
Eventually building a more complete AI-powered document assistant
This project has been a great learning experience because I'm not just building features — I'm learning how to turn a real-world problem into a software architecture.
Tech Stack:
Frontend: React, Vite, Tailwind CSS
Backend: Python, FastAPI
Database: SQLite, SQLAlchemy
Data Processing: pandas, openpyxl
Documents: PyMuPDF, pypdf, Tesseract OCR, Tabula
AI/RAG: Ollama, ChromaDB, Sentence Transformers
Authentication: JWT
Deployment: Local-first architecture, with cloud deployment planned for later
I'm currently open to freelance and full-time opportunities in:
Full-Stack Development · Backend Development · Python/FastAPI · React · AI Automation · Document Processing · SaaS Development
Building products that solve boring, expensive problems is what I want to do more of. 🚀
#BuildInPublic #FullStackDevelopment #Python #FastAPI #React #AI #Automation #SaaS #Freelance #SoftwareDevelopment #ProductDevelopment
Just wrapped up building flux/, an interactive UI with real-time WebGL rendering, custom GLSL shaders, and scroll-driven animation, built entirely from scratch with vanilla JS, what do you think?
It's been a super fun shift for me, most of my background is Python automation, AI integration, and data pipelines, but diving into frontend/UI work like this has been genuinely rewarding.
Link's in my portfolio if you want to see it in action. Always open to talk automation, AI integration, or interactive web work.
The vanilla JS route is the part I would defend - straight to GLSL instead of inheriting a scene graph you never needed. The one that caught me on scroll-driven work was portrait: visible width is 2*z*tan(fov/2)*aspect, so at 0.75 aspect you see less than half of what you...
Project title: AI Voice & Text Personal Assistant for Slack
Project description:
Built a conversational AI assistant in n8n that lives inside Slack and handles both voice and text messages, giving users a personal assistant they can talk to naturally, without switching apps or typing long commands.
The problem: The client wanted a way for their team to quickly get answers or delegate tasks through Slack, including by voice, without building a separate app or bot from scratch, and needed the assistant to hold context across a conversation rather than treating every message as a one-off.
What I built:
Message filtering: Every incoming Slack message is automatically checked to filter out bot-generated messages, so the assistant only responds to real human input.
Voice and text handling: The system detects whether the incoming message is a voice note or plain text. Voice messages are automatically downloaded and transcribed into text using an audio transcription model, while text messages pass straight through, so both input types end up in the same format for processing.
Conversational AI agent with memory: The transcribed or typed message is sent to a personal AI agent (built on Groq) that generates a response. The agent uses a memory module to retain context across the conversation, so it can handle follow-up questions naturally rather than starting fresh every time.
Instant reply in Slack: Once the agent generates a response, it's posted straight back into the Slack channel, so the interaction feels like a live conversation.
Outcome: The client's team now has a Slack-native assistant that understands both typed and spoken messages, remembers conversation context, and responds instantly, no separate app, no manual transcription, no lost context between messages.
This is good