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The 2026 small business guide to AI automation
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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I’m currently building a RAG system for an e-commerce company, basically giving an AI assistant access to the company’s own knowledge and product information so it can give useful, context-aware responses instead of just generating random answers.
The interesting part isn’t just connecting an LLM.
It’s making sure the AI can:
→ Find the right information
→ Understand the user’s question
→ Retrieve relevant context
→ Give an accurate response
→ Actually be useful in a real business workflow
Still working on it, but I’m excited about where it’s going.
This is the kind of AI automation I enjoy building, systems that solve actual business problems, not just AI demos.
Building something similar? Let’s talk.
I’ve built something similar for Purchaser, an e-commerce marketplace for automotive and industrial products.
I built a RAG-powered AI agent inside Slack that gives the team access to Purchaser’s product and spare-parts database. Instead of opening Excel and manually searching...
Recently completed a Call Centre 2023 analysis project using Excel, where I explored call trends, customer ratings, representative performance, and city-wise insights.
🔹 Built an Excel Data Model & DAX measures
🔹 Created interactive PivotTables, KPIs & slicers
🔹 Analyzed monthly trends, ratings, caller demographics & representative performance
🔹 Converted raw data into meaningful business insights
🛠️ Tools: Excel | PivotTables | Data Model | DAX | Data Visualization
Another step forward in my journey of becoming a stronger Data Analyst. 🚀
#DataAnalytics #DataAnalyst #Excel #DAX #Dashboard #DataVisualization #PortfolioProject
Pokémon Data Analytics Dashboard
An end-to-end data storytelling project was built, analyzing Pokémon across types through Python-based machine learning and an interactive Power BI dashboard covering type distribution, stat clustering, and battle matchups.
Raw data was cleaned and engineered. K-Means Clustering was used to segment Pokémon by battle stats, revealing distinct performance groups dominated by specific types. Findings were presented across a 4-page interactive dashboard (Type Distribution, Cluster, Collection, Battle) .
Tools: Python (Pandas, NumPy, scikit-learn, Regex), Power BI
