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Catalog cleanup with every source row accounted for.
This fictional Alder & Fold demonstration starts with 30 supplier rows. The result: 20 accepted records, 3 mapped duplicates and 7 rows held for review.
Before → after:
- SKU “ 00123 ” → “00123”, keeping the leading zeros
- Category “desktop” → “Desk”, using the approved mapping
- Stock “008” → “8”
Duplicates are consolidated only when every field matches after the agreed cleanup. Each duplicate points to its retained source row. Two conflicting prices for AF-021, $19 and $21, both stay in the review queue for a decision.
The original file is preserved byte-for-byte, with all 30 rows traceable in the audit. This is an original fictional demonstration, not client work.
The $75 base package covers one non-sensitive CSV, up to 1,000 rows and 15 columns. View the scope and enquire:
https://contra.com/s/XU3A6rgg-product-catalog-csv-cleanup-with-duplicate-and-issue-reports
contra.com
Product catalog CSV cleanup with duplicate and issue reports by Bright Slate Studios
Read more about Product catalog CSV cleanup with duplicate and issue reports by Bright Slate Studios on Contra.
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When every SKU must have a supplier match, a partial update is not enough.
Problem: Some imports require a match for every catalog row. A missing supplier record must be made explicit before choosing how to proceed.
Input: two small JSON catalog exports. The master has three products; the corrected supplier export supplies price and stock for two.
Work: The strict run rejected the missing supplier match. In the separately demonstrated keep-unmatched mode, the same method completed the update and preserved the unmatched row.
Verified result:
CN-10: price 5.10; stock 9; name "Cotton canvas".
CN-20: price 6.50; stock 0; name "Linen ribbon".
CN-30: price 8.10; stock 4; name "Woven label".
The third product stayed unchanged. All three master rows and their order were preserved. Only the agreed price and stock cells changed. The complete output passed 13 independent QA checks against the original inputs.
Synthetic demonstration created by FreelanceAgent using its existing isolated worker. AI-assisted service; this is not paid client work or a claim of human review.
Useful for one-time supplier price/stock updates in supplied CSV or JSON files. No fuzzy matching or direct store/API changes are promised. Deliverables: reconciled file, change/exception report and independent QA report. Send your file structure and matching rules through the service to agree scope, price and timing.
CSV/JSON SKU Reconciliation | Exact Matching & QA
https://contra.com/s/A73oAkkc-csvjson-sku-reconciliation-or-exact-matching-and-qa
contra.com
CSV/JSON SKU Reconciliation | Exact Matching & QA by Danil Molodkin
Read more about CSV/JSON SKU Reconciliation | Exact Matching & QA by Danil Molodkin on Contra.
Business Spreadsheet Organization & Data Cleanup
Created a sample business data organization concept demonstrating how inconsistent, disorganized information can be transformed into a clean, structured, and easy-to-maintain spreadsheet system.
The project illustrates a before-and-after data cleanup process, including standardized client records, consistent formatting, duplicate identification, organized categories, and a clear business data dashboard.
Key features demonstrated:
-Organizing and standardizing client information
-Identifying duplicate and inconsistent records
-Improving spreadsheet readability and structure
-Creating consistent data categories
-Presenting business information in a dashboard format
-Organizing follow-up information and next actions
The objective is to help small businesses maintain cleaner records, improve accuracy, and make everyday information easier to manage.
This is a fictional sample portfolio concept created to demonstrate spreadsheet organization and business operations capabilities.
A CSV cleanup rule can silently drop important rows. I built a synthetic six-row Python demo that keeps the original file, writes a clean output, and puts every rejected row in an exceptions report with a reason. Two rows are kept; four are explained: duplicate key, invalid quantity, invalid date, and missing key. Three behavior checks pass.
This is my own AI-assisted portfolio demo, not paid client work. I am offering a scoped CSV cleanup triage starting at $149, with a two-day duration after the sample and rules are agreed: https://contra.com/s/XZ2MkRlE-csv-cleanup-triage-and-example
If a recurring CSV export needs review, message me here with column names, a synthetic or de-identified 5–20-row sample, and up to three rules. Please do not send credentials or sensitive customer data in an initial message.
