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Built AIRO, a 6-agent ML automation system using LangGraph and Groq LLaMA 3.3. The system automates the entire ML pipeline from data ingestion to final report generation. Ran 18 experiments achieving RandomForest f1_macro of 0.7329. Features parallel training, MLflow tracking, SHAP explainability, and a dark Streamlit UI with live log streaming.
scikit-learnLangChainStreamlitPythonBusiness Workflow AutomationAI Agent EngineerAI Agent Orchestrator
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
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It’s a common misconception that automation is a "set it and forget it" kind of thing. Over the past few days, I’ve been deep in the setup for a large-scale online event where the data flow between Typeform, OneDrive (Excel), and Slack felt more like an obstacle course at first.
What I often see in these projects is a massive manual workload happening behind the scenes: attendee data being exported, analyzed, and shoved into spreadsheets by hand, while the team on Slack doesn't have the info they need when they need it.
This doesn't just eat up time; it drains the mental capacity needed for MMAs (Money Making Activities). You really just want a system that handles the logic while you focus on managing the exceptions.
My solution: The data flows automatically now, but I’ve integrated an AI step that categorizes responses before they even hit the Slack Canvas.
Building this took several hours and a few feedback loops with Grok and ChatGPT, but the result is that manual operations were cut down to just a few minutes, replaced by occasional maintenance.
Of course, it’s not "set and forget." If the form changes or a new event comes up, I need to update the Zaps and maybe tweak the AI’s categorization. But that’s qualified maintenance instead of repetitive copy-pasting.
One side effect that clients value most: This shifts the process from constant human dependency to sporadic system maintenance. The workflow no longer stalls if someone is away—the system just keeps running.
Watching the logic chain verify and file real data correctly makes you realize just how much brain capacity was tied up in the old process.
If you’re building an event setup right now and feel like your tools are clashing instead of flowing: Let me know in the comments what your biggest workflow time-sink is.
This project is a Python-based Ecommerce Sales Analysis tool that works automatically:
📥 INPUT: books_data.csv (contains Product, Category, Price, Quantity, Date)
⚙️ PROCESSING:
Pandas loads and cleans the data
Creates new column 'Total_Sales' = Price × Quantity
Groups data by Category for analysis
Calculates statistics: average price, total revenue, top products
📊 OUTPUT (Auto-generated):
Revenue by category (bar chart)
Order distribution (pie chart)
Price vs quantity (scatter plot)
Daily sales trend (line chart)
CSV file with category analysis
Text report with summary
🎯 RESULT: A complete sales dashboard ready in seconds — no manual calculation needed.
RAG Study Buddy
Built a full-stack, AI-powered study assistant that allows users to upload PDF textbooks and documents, instantly transforming them into interactive learning environments.
The application leverages an advanced Retrieval-Augmented Generation (RAG) pipeline orchestrated with LangChain. Uploaded documents are parsed, chunked, and embedded into a Pinecone vector database. Upon querying, the system retrieves the most semantically relevant sections and passes them to OpenAI's API, ensuring that all answers are strictly grounded in the source text to drastically reduce hallucinations.
Beyond standard Q&A, the system automatically generates study flashcards and maps out document relationships using knowledge graph visualization.
Tech Stack: Python · FastAPI · Next.js · LangChain · Pinecone · OpenAI API
This project demonstrates my ability to build production-ready AI microservices, manage complex backend RAG logic (including semantic chunking and embedding strategies), and integrate them seamlessly with a modern web frontend.
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