Matching Results Compiler
Create the user-facing matching workbook from one or more auto-matching result files.
- Compiles retailer sheets into one workbook
- Preserves user review traffic-light tabs
- Supports optional ALR context
AiCategoryEngine combines workflow, review, and run history in one operating path so catalogues move faster without becoming harder to trust.
Instead of stitching together spreadsheets, prompts, and ad hoc review, teams work through one visible sequence.
These tools sit outside the authenticated categorisation workflow so operators can compile matching results, build dedupe workbooks, audit completed matching, or work through brand fixes quickly from the home page.
Create the user-facing matching workbook from one or more auto-matching result files.
Compare matching results with account-level reports to see what was matched, skipped, deleted, or left unresolved.
Compare incoming products with an account-level report and create the upload-ready workbook.
Open the focused brand-review surface used with the Admin Toolkit helper extension.
The product is strongest when the process stays legible. Each stage has one job, and each handoff is visible.
Upload the product file and workbook logic in one place, with the fields and mappings needed to start cleanly.
Thin evidence and ambiguous rules surface before output is committed, so operators are not debugging blind results.
Completed runs retain timing, usage, output, and lineage so the platform can be assessed as an operating system, not just a tool.
The page shows enough live operating data to prove the system is active and measured without forcing visitors to parse a full dashboard.
The strongest contrast is between hidden automation and visible workflow. Teams can move faster when they do not have to guess how outputs were produced.
Manual files, scattered logic, and black-box classification make it hard to know what changed, what failed, or what should be reviewed.
The workflow is visible from setup to output, with review, runtime, and lineage attached to the run instead of reconstructed later.
The platform is designed so teams can understand who has access, what was reviewed, and which run produced the final output.
Authentication sits in the real product flow rather than as an afterthought around it.
Weak evidence can be surfaced and handled before a run is treated as finished.
Each run keeps useful timing and output detail attached, making repeatability and assessment easier.
The fastest way to assess the platform is with your own product structure, your own logic, and the same review path your team would actually use.