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Designed and deployed an enterprise-grade LLM copilot that integrates with internal data systems (CRM, analytics, ops) to provide real-time decision support for business teams.
The system goes beyond chat — it executes multi-step reasoning workflows (data retrieval → analysis → recommendation), enabling non-technical users to interact with complex data systems through natural language.
Tech Stack
LLMs: GPT-based APIs, prompt orchestration
Backend: Python, FastAPI, Node.js
Infra: AWS (Lambda, S3, ECS), Docker
Data: PostgreSQL, vector DB (Pinecone / FAISS)
Orchestration: LangChain / custom pipelines
Observability: Prometheus, logging pipelines
The network for creativity
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Built a deployed AI tool that lets users upload research papers and query them using natural language. RAG pipeline with vector search retrieves precise context-aware answers. Features PDF parsing, AI summaries, topic modeling, and citation extraction. Live on Render.
interesting
🚀 SMS Spam Detection Project
I’m excited to share one of my machine learning projects — an SMS Spam Detection System 📩
🔍 The goal of this project is to automatically classify incoming messages as spam or ham (legitimate messages).
💡 Key Features:
Machine Learning-based model
Text preprocessing & feature extraction
Accurate spam classification
Simple and user-friendly interface using Streamlit
🛠 Technologies Used:
Python
Scikit-learn
Pandas & NumPy
Streamlit
🌐 The project is available on GitHub and also deployed using Streamlit.
📎 Project Link:
https://sms-spam-detection-001.streamlit.app/
🙏 Feel free to check it out and share your feedback. Your suggestions will help me improve further!
#MachineLearning #Python #AI #DataScience #Streamlit #GitHub #Learning
Nice work! It seems really interesting. Some time ago i did something similar but for the news online. i sued textblob, nltk and vader for sentiment analysis. Did you use streamlit for the categorization of the words?
Say hello to Pitching Pat, and goodbye to wasting hours each day applying to jobs! 🤩
Every freelancer faces the same thing: when you’re fully booked, you don’t have time to pitch - and when your projects end, you’re left with an empty pipeline and a "cold start" problem.
Pitching is the most critical part of freelancing, yet it’s often the first thing to be neglected 😬
The solution: Pitching Pat - a Notion Custom Agent designed to turn that pitching grind into a quick, seamless process.
How it works:
The freelance brain 🧠: a centralized "source of truth" where the user inputs their services, portfolio, ideal clients, and unique brand voice.
The leads database: you drop in the job descriptions (via the save to Notion extension, or manually).
Pitching Pat: she cross-references the job against its brain to rate the job fit from 1-10, and if it scores 5+, she drafts a pitch that sounds exactly like you ✨
Impact: saves freelancers several hours a week by removing the "blank page" friction of filtering through and applying to jobs.
Reliability: by grounding the Agent in a specific "brain" page, it eliminates AI hallucinations and ensures every pitch is backed by REAL portfolio results ✅
Craft: designed for a "low-friction" setup - no complex third-party automations required - it’s a self-contained Notion ecosystem.
Durability: whether you’re a video editor today or a copywriter tomorrow, you simply update the "brain" page and the system evolves with your business 🙌
PS: the agent hasn't been approved yet, been going back and forth with Notion on some details, and currently waiting their response, but this is the link: https://www.notion.so/agent/331eb5f7-8d81-80ce-8157-009266613a90
Sounds like a must have
Would love to try it out
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