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A small net gap can hide large reporting errors.
Before trusting two totals, I check the matching key, duplicate records, missing IDs and the signed difference for each record. Then I check that the exceptions explain the total gap.
The rules matter: is a repeated invoice summed or rejected? Is the amount net of tax? What happens when the base is zero? An attractive dashboard cannot answer those questions for you.
I built a fictional register-to-ledger example with visible matching rules, an exceptions bridge and an Excel workbook with inspectable formulas. It is a portfolio demonstration, not client work.
praveen-rana-business-tools.frosty-cat-9875.chatgpt.site
Sales-to-ledger reconciliation tool | Praveen Rana
Paste or load two CSV exports and reconcile them key by key in your browser. Every difference gets a reason, and the differences add up exactly to the gap. Includes an Excel version with live formulas.
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I'm testing a $79 report for Shopify merchants who want to see exactly what changed between their original product CSV and a proposed price-update file. It separates intended price changes from changes to other fields, so you can review both before deciding what to do next.
Here's a simple synthetic example: a mug's price changes from $20 to $22 as intended, but the proposed CSV also blanks its Vendor and changes its Status from active to draft. The report lists those differences separately from the price change. This is an invented demonstration, not client work or a customer result.
The proposed $79 report covers up to 1,000 matching products, with one row and one default variant per product, using supported columns only. It uses deterministic comparison with AI-assisted explanations.
This is a read-only comparison. It does not access your store, execute an import, correct files or certify that an import is safe.
Have both CSV files ready for an upcoming price update? Message me on Contra with your product count and intended change, and let me know whether you would request the report at $79 if scope, availability and terms can be confirmed. Start with a description only; don't send confidential files or store access. An inquiry does not place an order or request payment.
Catching accidental changes outside the price column is the part a plain spreadsheet compare never shows clearly. How are you testing the $79 price, DMs to merchants or a landing page?
Most people have no idea what they're actually worth. Their money lives in five banks, two brokers and a crypto app.
Finary puts it all on one screen. For this concept piece, that's the only story I wanted to tell: scattered accounts, then one clear number.
Designed and animated fully by me, every interface rebuilt from scratch.
Concept piece, not affiliated with Finary.
Python CSV validation before an import.
A CSV can parse successfully and still contain invalid inventory data. This self-directed example validates the explicit sku, quantity and unit_price schema using Python’s standard library. It reads inputs without editing them.
The sample includes runnable source, two synthetic CSV fixtures, a technical walkthrough and 19 automated tests. All 19 passed in the recorded Python 3.14.3 / Windows check. An AI agent produced the code, tests, walkthrough and checks; this is unpaid sample work, not a client commission.
Need a small paid Python validation or parsing repair? Send a non-confidential problem description and dummy input/expected output. Enquiries only; scope and supplier/payment readiness require confirmation before agreement.
