Walkthroughs
How it works
More walkthroughs are coming that explain the broader invoice management features in the app. For now, the Quick Parser examples below focus on one of the most immediate needs for a retailer: getting new products from an invoice into Shopify without having to rebuild the product data by hand.
Quick Parser is designed for the cases where an invoice PDF does not have a clean, consistent table that can be parsed automatically. Invoices often come from different vendors, with different layouts, spacing, line breaks, and column structures. Because of that, the system may be able to identify the line item area, but still need a little human guidance to understand which part of each row is the SKU, description, quantity, unit price, or UPC.
Quick Parser solves that problem by letting the user teach the system from one sample row. Once that sample row is understood, the system can apply the same pattern across the rest of the invoice and build clean exportable line items.
If you connect Shopify in Team Settings, the app can also check parsed line items against your existing Shopify catalog before export. After your rows are confirmed, use the duplicate-check button to search Shopify by SKU and UPC, review any potential matches, and compare the current Shopify price before downloading the CSV.
This is especially useful when a vendor invoice includes products that may already exist in your store under the same SKU, a vendor-prefixed SKU, or the same barcode. If matches are found, the app highlights them so you can decide whether to create a new product, update an existing one, or export with duplicate markers included.
There are two ways to do that. The first is line segmentation, which is best when the row is already in the right order and just needs to be split into fields. The second is token organization, which is best when the row’s pieces are out of order or need to be rearranged before they can be labeled correctly.
Line segmentation answers: “What are the parts of this row?”
Token organization answers: “How should this row be rearranged before we define its parts?”
Video 1: Quick Parser - Line Segmentation
Line Segmentation
This approach is best when the sample row contains the right information in the right left-to-right order, but the system needs help deciding where one field ends and the next begins.
In this example, the row already appears in the correct order, so the fastest option is to segment the row into the fields we want. We simply divide the sample row into meaningful sections, label each section, preview the result, and then confirm the mapping.
Video 2: Quick Parser - Token Organization
Token Organization
This approach is best when the sample row contains the right data, but the pieces are not grouped in a way that makes labeling easy. Instead of segmenting the row as-is, we first reorganize the tokens and then label the grouped result.
In this example, the row is not cleanly organized, so instead of splitting it as-is, we first rearrange the tokens into the correct groups. Once the row is organized, we label each group, preview the output, and confirm the results.
Video 3: Quick Parser - Known Vendor Detection
Known Vendor Invoice (automatic detection)
In some cases, the Quick Parser recognizes that the invoice matches a vendor format the app already understands. When that happens, the rows can be detected and mapped automatically before you do any manual teaching.
This example shows the known-vendor path: the parser identifies the vendor, applies the matching table strategy, and brings you into review with the rows already organized. From there, you can confirm the result or switch to a manual recovery path if something looks off.
Video 4: CSV Mapper
CSV Mapper (column-based product import prep)
This tool is best when the source data already exists in a CSV or spreadsheet, but the column names and structure do not line up with the export format you want to produce. Instead of teaching the parser from one invoice row, you review the detected columns and tell the app which ones represent SKU, description, quantity, unit price, UPC, image URL, or fields that should be ignored.
In this example, the mapper reads the uploaded file, identifies the available columns, shows sample values from each one, and lets you confirm the mapping before export. Once those columns are assigned, the app can turn the original file into a cleaner, ready-to-use CSV without having to restructure the data by hand.
Video 5: Connect Shopify and Check for Duplicates
Shopify connection and duplicate review
Before using duplicate checks, connect your store in Team Settings using your your-store.myshopify.com address. Once connected, the app can compare parsed invoice rows against your current Shopify catalog.
Duplicate checks are available after line items are ready for export. The app searches Shopify for matching SKUs and UPCs, shows potential matches, links to the existing product, and displays the current Shopify price so you can make a cleaner export decision.