How do I fix a misread vendor name?

When to fix at the document vs at the vendor master record

There are two places to correct a vendor name. Both feed into the same data model, but they have slightly different ergonomics.

• At the document (the flow this article is about) — the right place when you see a wrong vendor name on a specific invoice. One click, one edit, the vendor learning kicks in and future invoices benefit. This is the path 90% of corrections take.
• At the vendor master record (Trading Partners → vendor → Edit) — the right place when the canonical info for a vendor is wrong (their legal name has changed, the VAT ID is wrong on the master record, the bank IBAN needs updating). Edits here propagate to all past + future invoices from that vendor.

When in doubt, edit at the document. The system propagates upwards automatically.

The walk-through

Step 1 — open the document

From the Documents list, the in-app notification when extraction finishes, or the search bar (search by partial vendor name or invoice number). The document detail page shows the original on the left and extracted fields on the right.

Step 2 — click the vendor field

The Vendor field is near the top of the right pane. Clicking it converts the label into a text input. As you start typing, an autocomplete dropdown shows existing vendors in your Trading Partners list — both exact matches and fuzzy matches (so typing "Hetz" matches "Hetzner Online GmbH" even if you have it stored under a slightly different exact name).

Step 3 — type the correct name

Two paths:

• Existing vendor: pick one from the autocomplete dropdown. The document is now linked to that vendor's master record, inheriting the vendor's canonical name, address, VAT ID, default category, etc.
• New vendor: just type the name (no match in autocomplete) and press Enter or click outside. A new vendor record is created in Trading Partners, the document links to it, and you'll be prompted to optionally fill in the rest (address, VAT ID) — though you can skip and add later.

The autocomplete is fuzzy-tolerant. If you've stored "Stripe Inc" and you type "stripe", the dropdown matches. If you type "Stripe" with different capitalisation, the existing record is matched (not duplicated).

Step 4 — save

Tab to the next field, click outside the field, or press Enter. The change is saved immediately. Three things happen simultaneously:

1. The document now shows the corrected vendor name.
2. The vendor record is either created (new vendor) or linked (existing vendor) in your Partners registry.
3. The per-vendor learning is recorded — after three confirmed corrections, the learned pattern applies to future scans of that vendor.

The audit trail captures the change with your user attribution.

What happens after your correction — the three propagations

The single click triggers three updates in parallel:

1. This document

The vendor field on the current document is now your value. (Note that a Re-scan runs a fresh extraction and replaces the extracted data — make manual corrections after your last re-scan.)

2. The vendor master record

The vendor in Trading Partners gets created (if new) or updated (if it already existed). If existing, the canonical name on the master record stays as it was (your correction on one document doesn't rename the master record); to rename the master record itself, edit at Trading Partners → vendor → Edit. If you typed a slightly different variant of an existing vendor, the system links to the canonical record without renaming it.

3. Future invoices from the same vendor

Two mechanisms work together here:

• Fuzzy name matching. When a new scan comes in, the extracted vendor name is matched against your Trading Partners registry with fuzzy matching — so small OCR variations ("Helzner" vs "Hetzner", capitalisation, missing legal-form suffix) still link to the right vendor record instead of creating a duplicate.
• Vendor pattern learning. When you correct a scanned invoice, the system records vendor-specific patterns — the right category, currency, VAT rate, net-vs-gross layout. After 3 confirmed corrections a pattern becomes active and is applied to future scans of that vendor. If a vendor's values genuinely vary, the system learns a "varies — don't assume" marker instead of guessing. Learned patterns are country-scoped, so a vendor's German invoices never contaminate its UK ones.

When the learning doesn't kick in yet

• You only have 1–2 corrections for the vendor. The system is cautious about small samples — a pattern activates after the third confirmation.
• The vendor's values genuinely vary (different VAT rates, different currencies across invoices). The system deliberately stops assuming and leaves those fields to the fresh extraction.
• The misread was structural, not textual. E.g. the AI picked up the bill-to name instead of the vendor, on a consolidated multi-party invoice. Correct each occurrence; the fuzzy name match still links your correction to the right record.

In any of these cases, correct each occurrence; the system catches up.

Editing the vendor master record

Beyond per-document corrections, you can edit the vendor itself:

1. Go to Partners in the left nav.
2. Find the vendor and click to open.
3. Edit the canonical name, addresses, VAT ID, bank details (IBAN), default VAT rate, default payment terms, default currency. These apply to future invoices from this vendor and to the vendor's display everywhere.

The partner detail view also surfaces:

• All invoices from this vendor (both manually-linked and auto-detected)
• The vendor's address history (with the newest address as current)
• The vendor's IBAN — used by the matching pipeline's IBAN+amount shortcut

Edits at the master record are audited the same way as document-level edits.

Troubleshooting

AI keeps splitting one vendor into two ("Stripe Inc" and "Stripe Payments Europe Ltd"). These are technically two different legal entities (different VAT IDs, different addresses), and the system creates separate records by default. But for your books they might be one. On the Partners page, select the two records and click Merge — you choose per field which record's value survives, and all invoices and addresses move to the surviving record.

AI auto-created a vendor that's just garbled OCR text. First re-edit the affected invoice(s) to point to the correct vendor, then delete the garbled record from its detail page. A partner that still has invoices linked can't be deleted — merge it into the correct partner instead (the merge moves the invoices over).

I corrected the vendor, but the VAT number on the document was also wrong — was that picked up? Partially. Editing the vendor field doesn't propagate to other fields automatically; you'd need to also click the VAT-ID field on the document. The Trading Partners record stores VAT, address, bank details — once they're right at the master record, future invoices pick them up.

The autocomplete dropdown shows weird old entries from when I was testing. Clean up the Partners list. Delete unused entries (partners without invoices delete directly); for entries with invoices, merge them into the correct partner.

Two team members corrected the same field at the same time. Last-save-wins. Both edits appear in the audit log. We don't surface a "another user is editing this" warning currently; for high-coordination teams, the convention "one bookkeeper" minimises conflicts.

The vendor on the invoice is in a different language script (e.g. Cyrillic, Arabic). Type the correct name in whatever script you want — the system stores Unicode and matches consistently. The autocomplete handles non-Latin scripts; the matching uses character-level fuzziness so transliterations also work.

I want to bulk-rename a vendor across many documents. Rename the partner record (Partners → vendor → Edit). All invoices linked to this partner show the new name. If the documents are linked to different vendor records that should all be one, merge them first, then rename the surviving record.

Related

• What the AI reads, and how to correct it — the broader correction flow
• Understanding confidence scores — what the overall scan score means
• Vendors that bill you every month — how the matching memory learns your regular vendors