Sample workflow · fictional business
Order-data automation for Kiln & Co. Ceramics
A disposable studio export lands every week with the same dozen faults. This demo runs the exact validate → clean → export pipeline that a scheduled Python job would — in your browser, on synthetic rows. Nothing is uploaded anywhere.
- 1Messy export12 raw rows
- 2Validate & flag12 rule checks
- 3Clean & exportCSV in your hands
Stage 1 — the weekly export as it arrives
Twelve fictional records in a Kiln & Co. weekly orders export. Each planted error below maps to one validation rule.
| # | Order id | Customer | Phone | Item | Qty | Unit price | Date | City | Status | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KC-1082 | Meera Raghavan | meera.r@fernmail.com | +91 98450 11223 | Glaze trial set (5) | 2 | ₹1,250 | 2026-09-02 | Bengaluru | paid |
| 2 | KC-1083 | Devanshi Kulkarni | Devanshi.Kulkarni#gmail.com | +91 98231 44090 | Terracotta planter L | 3 | ₹899/ | 04/09/2026 | Pune | Pending |
| 3 | KC-1084 | Aftab Qureshi | aftab.q@kilnmail.org | +9198674 | Speckled mug pair | 2 units | $19.99 | 5 Sept 2026 | Hyderabad | paid |
| 4 | KC-1085 | Nandita Bose | +91 90512 33440 | Blue vase (medium) | 1 | ₹ | 2026-11-30 | Kolkata | paid | |
| 5 | KC-1086 | Rohit Verma | rohit.verma@outlook.in | +91 97655 21908 | Show plate – indigo | 2 | ₹1,499 | 2026-10-01 | Delhi | PAID |
| 6 | KC-1084 | Aftab Qureshi | aftab.q@kilnmail.org | +919867458202 | Speckled mug pair | 2 | $19.99 | 2026-09-05 | Hyderabad | paid |
| 7 | KC-1087 | Sana Fernandes | sana.f@webpost.io | +91 91140 87002 | Custom name plaques | ten | ₹350 | 07/10/2026 | Goa | on hold |
| 8 | KC-1088 | Kavya Menon | kavya.menon@quickbox.in | +91 98786 66311 | Tea-light set (4) | 4 | ₹625 | 2026-10-08 | Kochi | paid |
| 9 | KC-1089 | Imran Sheikh | imran@sheikh平凡.cn | +91 92055 48190 | Hanging planters (2) | 2 | ₹1,100 | 11/10/2026 | Lucknow | shipped |
| 10 | KC-1090 | Pooja Iyer | pooja.iyer@@gmail.co | +91 98801 77442 | Dinner set – 12 pcs | 1 | ₹6,950 | 2026-10-12 | Chennai | paid |
| 11 | KC-1091 | Tarun Bedi | t.bedi@northwebs.co | +91 95990 22057 | Wheel-thrown planter | 6 | ₹2,400 | 2026-10-14 | Noida | |
| 12 | KC-1092 | Aditi Sharma | aditi.sharma456@m关注度.com | +91 99200 51136 | Garden companion set | 1 | ₹4,250 | 2026-02-29 | Jaipur | paid |
The same pipeline, on your exports
Scheduled, not hand-run
As a Python job (pandas + openpyxl), it can run on a schedule: pick up the export from your inbox or shared drive, validate, clean, and write the CSV — with a run log.
Fail loudly, not silently
When a rule can't be applied safely, the job flags it and notifies you — instead of quietly shipping a broken report that someone only notices at month end.
Delivery the way you read it
Cleaned summary to email, Drive, or straight into a spreadsheet template you already use. You keep the code and the documentation, so it remains yours.
Buried in a weekly export mess?
Describe the current manual steps (or paste a sample export) and I'll scope what automation would actually save — and what's not worth automating.