Prepare coding suggestions
Label exported expense lines using your team’s approved categories.
STAYCHARTED AMT / FINANCE TEAMS
Expense and invoice classification for finance teams
Learn expense categories from approved descriptions and supplier data, then suggest consistent labels for new rows.
FROM YOUR EXAMPLES
Annual subscription renewal for the design team’s editing software.Software subscriptionsIllustrative category · Not a live prediction
Where this fits in your work
Review supplier descriptions
Group varied descriptions into categories before posting or reporting.
Keep a correction loop
Use reviewer-approved corrections as examples for the next model version.
PREPARE YOUR EXAMPLES
Start with a file your team has already labeled.
Use exported rows with descriptions, relevant supplier information, and the category your finance team approved. Define the category list before training and resolve inconsistent examples. Include the information that will actually be available when a new row arrives.
Use one row per example with an approved answer. Keep the input representative of what the model will receive later, and agree on how to label ambiguous cases before training.
| Description | Relevant context | Approved category |
|---|---|---|
| Design software annual renewal | Design team | Software subscriptions |
| Train ticket to client workshop | Client delivery | Business travel |
| Printer paper and folders | Office operations | Office supplies |
Illustrative rows and categories. Use your own approved labels.
From examples to reviewed results
Prepare
Resolve inconsistent labels and remove unnecessary sensitive information.
Train
Choose a model on your plan and train on representative examples.
Validate
Inspect held-out accuracy by category and the mistakes that remain.
Use and improve
Publish, fill a file or call the API, then review and feed corrections into the next version.
KEEP REVIEW IN THE WORKFLOW
Know where the model stops.
Check material amounts, unfamiliar suppliers, split allocations, tax treatment, and any line whose classification depends on information absent from the input. AMT does not extract invoice totals or post transactions to the ledger.
Evidence and implementation
Classify existing text, not scanned paperwork
AMT classifies the text or pictures you give it. For invoice descriptions and expense lines, use text already extracted by your accounting workflow. Vision is not OCR, and classification does not extract invoice totals or automatically post accounting entries.
Check the categories that affect reporting
Examine held-out accuracy and errors for each category. Similar descriptions may have different treatment depending on context your model cannot see. Keep accounting judgment and approval with your team and review ambiguous or material expenses.
Use the result in your existing process
Fill a spreadsheet of new rows with category suggestions or connect the model through the API. Review before importing the results into your accounting tools. Retrain with corrected examples as suppliers or internal categories change.
File fills return category suggestions for your existing import process. API calls return results to your own software; your integration decides how to apply them. Plan allowances and workspace rate limits still apply.
Common questions
How is this different from Expense Classification?
Expense Classification explains the task of learning labels from transaction descriptions. This page covers the finance team’s broader preparation, review, and import workflow.
Can it read scanned invoices?
This text workflow uses descriptions already extracted by your accounting process. Vision is not OCR for reading invoices or receipts.
Does confidence replace approval?
No. Confidence is a model signal, not an accounting judgment. Set review rules around materiality, context, and the errors you observe.
Explore the details
Try it on your own examples.
Start with Free for text classification. Compare plans for AI models, Vision, and API access.