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A reel that takes off is good news and a stock problem.
With the ai agent Hop, users point at the pieces and ask if they will last the week. It reads Inventory and Sales once.
4 left. Out by Saturday. Next delivery Wednesday.
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Picture a consultant who takes 8 sales calls a week.
After each one comes the same routine: replay the recording, write notes, update the pipeline sheet, write the follow-up email. Call it 30 minutes a call. That is 4 hours a week, and on busy days the follow-up slips to tomorrow.
I built an n8n workflow that takes that routine away:
→ Fireflies records and transcribes the call
→ As soon as the transcript is ready, AI writes a summary, the open questions, the next step and whether the prospect is ready to go ahead
→ A follow-up email that fits that outcome is saved in Gmail drafts, addressed to the person on the call
→ The call is logged in Google Sheets and the notes land in your inbox
Nothing is sent automatically. You read the draft, change a line if you want, and press send.
For that consultant, 4 hours of admin a week turns into a few minutes of reading, and every prospect hears back the same day.
I tested it on a real recorded client call and have submitted it to the n8n template library as a free template. If you want it set up for your business, send me a message.
Retail Inventory Sales Analysis
Retailers often collect large amounts of sales and inventory data but struggle to turn it into clear decisions around stock, demand, and revenue performance.
I built an end-to-end retail analytics solution using Python, MySQL, and Power BI. With Python and Pandas, I cleaned and prepared the raw dataset, handling missing values based on their business meaning rather than blindly replacing them. The data was then analyzed in MySQL to explore revenue, units sold, inventory, discounts, categories, regions, and seasonal performance.
The final result was an interactive Power BI dashboard designed for quick business monitoring. It includes KPI cards for revenue, total stock, demand fulfillment, total products, and average discount, alongside sales by category, monthly seasonal performance, regional revenue and units sold, inventory by demand forecast, and seasonal distribution.
Key findings included a 95.72% demand fulfillment rate, balanced sales across the five product categories, and a near-even distribution of performance across the four seasons. The dashboard also provides a clear view of regional sales and inventory relationships, helping identify where demand and stock performance may require further attention.
Tools: Python (Pandas) · MySQL · Power BI · DAX
