AI BUILDERS STACK
Case studyInvoice ProcessingFinance

How Veyra Accounting processes 1,200 invoices a month with zero data entry

A bookkeeping firm was typing every client invoice into the ledger by hand. Now invoices are read, matched, and posted automatically, exceptions flagged.

1,200

invoices/month automated

99.2%

extraction accuracy

0

manual entries

5 wks

to production

01 — The problem

Data entry was the whole job

Veyra's team hand-keyed 1,200 supplier invoices a month across dozens of client ledgers. It was slow, error-prone, and the bottleneck on taking new clients.

The work wasn't judgment — it was typing. That's exactly what should be automated first.

PER INVOICE · BEFORE

Open + read PDFmanual
Key line itemsmanual
Match to POmanual
Post to ledgermanual

× 1,200 a month

02 — The build

Read, match, post — checked at every step

Incoming invoices hit a pipeline that extracts the data, matches it against the right purchase order, and posts it — routing anything uncertain to a human.

W1

Teach it the documents

Trained extraction on Veyra's real invoice formats across their client base.

W2

Match and post

Wired to each client ledger: line items are matched to POs and posted automatically.

W3

Confidence gates

Anything below a confidence threshold — odd totals, missing POs — is flagged for review instead of posted.

BUILD LOG — VEYRA

> abs build --client veyra

Training extraction model…

✓︎ Invoice formats learned: 42

✓︎ Ledger + PO matching connected

✓︎ Confidence gate set: < 0.95 → review

Deployed to production. Day 33.

StackOpenAIn8nXero APIPostgres
03 — The result

The bottleneck became a background task

Invoices now post themselves. The team reviews only the small share the system flags, and Veyra took on new clients without adding headcount for data entry.

Data entry

full-time → exceptions only

Extraction accuracy

human error → 99.2%

Invoices/month

capped → 1,200+

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