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A no-code AI website builder that has generated 100+ production Next.js sites for non-technical users. It handles theming, Google integrations, SEO metadata, and legal pages (robots.txt, sitemap, privacy policy) end to end, so users go live in minutes without hiring a developer.
Built with: Next.js, AI content generation, SEO
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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Just published: 0-1 design for an Energy Management System (EMS) ⚡
Designed a desktop platform monitoring wind turbines, PV parks, and battery storage across multiple sites. Keeping the interface mostly white, so green and red status indicators do the talking.
Operators managing critical infrastructure need to assess system health instantly. No visual noise, no cognitive overload. Just unmistakable state communication.
Cool work 👌
SupportFlow AI is a customer support management system built with Python, PySide6, FastAPI and PostgreSQL.
It helps teams manage customer requests, assign priorities and responsibilities, track conversations, and generate AI-assisted response suggestions while keeping a human in control of the final reply.
I built the application structure, interface, backend integration, database workflows, API layer and automated testing.
The conversation-revision idea sounds useful. I would also check that revision on the server when accepting the send, so a message arriving between review and click cannot slip through. Mark the draft stale while keeping the human edits visible. Two useful regression cases: a...
One business problem. Three analytical routes. One data-driven story. ☕📊
I turned raw business data into actionable decisions—not just another Power BI dashboard.
For the Onyx Data October 2026 Challenge, I worked on the Sunrise Coffee Chain analytics project, analyzing store performance, profitability, customer loyalty, revenue mix, and trading patterns across 60 UK stores (2023–2024).
🔹 My Analytical Approach
1️⃣ Python — Data Preparation
Cleaned and prepared the source datasets to establish a reliable foundation for analysis.
2️⃣ SQL — Business Analysis
Investigated store-level profitability, cost pressure, customer loyalty, revenue contribution, and trading patterns.
3️⃣ Power BI — Interactive Dashboards
Built a 3-page interactive report featuring advanced DAX measures, KPI cards, YoY comparisons, profitability analysis, and business-focused visualizations.
📊 Key Findings
• £89.7M Revenue | £16.9M Net Profit
The business recorded an aggregate net profit margin of 18.8% across the analysis period.
• Store Location Matters
Transport-Hub stores achieved a 25.1% aggregate net profit margin, compared with 18.6% for High-Street stores.
• An Area Requiring Attention
South London Corner recorded a £50.5K net loss over 24 months.
• Product Revenue Contribution
Espresso contributed 45.7% of daily revenue, while food contributed 26.1%.
• Trading Patterns
Average daily revenue was approximately 24% lower on weekends and 33% lower on UK bank holidays than on regular trading days.
💡 My Biggest Takeaway
Revenue alone does not tell the whole story. Understanding profitability, cost pressure, product contribution, and trading patterns helps businesses identify where to improve performance and make better decisions.
🔗 Explore the Complete Project
GitHub: https://github.com/SajeebTheAnalyst/sunrise-coffee-chain-analytics
If you were advising the management team, which finding would you investigate first—and why?
#DataAnalytics #SQL #PowerBI #Python #DAX #BusinessIntelligence #DataVisualization #OnyxData
I would investigate South London Corner first. Break the 24-month loss into store-month revenue, direct costs and allocated costs, then compare it with similar stores over matched trading periods. Reconcile at store-month grain before joining product or loyalty tables so a...
