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JSON to CSV conversion — synthetic portfolio demonstration. Two order objects were exported into three explicitly selected columns with zero missing selected fields. Field order is controlled and the source is preserved. Nested values require agreed serialization rules. Executed locally with Python. AI-assisted preparation; no client project or earnings claimed.
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Excel PivotTable — Sales Summary
Client had raw sales data (14 rows across UK/USA, split by quarter) and needed it turned into something actually usable. I cleaned it up and built a PivotTable that breaks total sales down by rep and country, with grand totals for both.
What I did:
Structured the raw data into a proper table (consistent headers, currency formatting)
Built a PivotTable summarizing Sum of Sales by Last Name and Country
Used SUMIFS so the totals update automatically if the source data changes — nothing hardcoded
Checked every formula for errors before delivery
Delivered as a working .xlsx file, ready to plug into a bigger report or dashboard.
If you've got messy sales/ops data sitting in a spreadsheet and need it turned into something you can actually read at a glance, this is exactly the kind of thing I can help with.
Synthetic JSON to CSV Conversion
AI-created demonstration, not client work. Eight fictional JSON records reconcile to five cleaned CSV records, two exceptions and one removed exact duplicate. Whitespace and case were normalized. SKU strings keep leading zeroes; zero and missing quantities stay distinct. An ambiguous price and invalid quantity remain in exceptions. One formula-like name was escaped and flagged as text.
Exported files were independently parsed and reconciled. Import SKU columns as Text; native spreadsheet import was not tested. This is output-only conversion, not installed software or ongoing automation.
Rules, source and outputs: https://github.com/josephketiv/ai-service-samples/blob/main/data-notes.md
Built an automated weather monitoring system that logs conditions to Google Sheets based on temperature thresholds.
Instead of manually checking weather data and logging it, I built a Make.com scenario that:
Pulls current weather data on a schedule (Weather module)
Routes the data based on temperature conditions
Logs the results into different Google Sheets tabs/rows depending on which condition is met
Result: Weather data is now tracked automatically and organized by condition — no manual checking or data entry required, and the log stays consistently up to date.
