Invoice and receipt extraction with LLMs

Extraction covers documents ingested from a shared inbox, a direct upload or a scanning app, with line-item parsing for vendor name, invoice or receipt date, subtotal, tax and total amount, and a suggested GL code based on the vendor and your chart of accounts. Every field carries its own confidence score rather than one score for the whole document, because a clean vendor name next to an unclear amount should not be treated the same as a document that is uncertain end to end. Anything under the agreed threshold is queued for a named reviewer, and the original document image stays attached to the record so anyone checking the transaction later can see exactly what was read.

We start by pulling a sample of your actual invoices and receipts, the messy real mix, not a clean test set, to see how the model performs against your specific vendors and document quality before anything goes live. A confidence threshold is set from that sample and adjusted after the first few weeks of real volume. Once live, documents flow in from email, upload or a scanning app, get extracted and scored, and either post automatically or land in a review queue, where a person on the delivery team corrects and approves before the data reaches your ledger or AP platform.

Businesses receiving a high volume of vendor invoices and receipts relative to their bookkeeping headcount get the most out of this: ecommerce sellers juggling supplier and ad-platform bills, construction and job-cost businesses with paper receipts coming in from multiple sites, and agencies or consultancies processing subcontractor and vendor invoices every week. A business receiving a handful of invoices a month usually gets more value from straightforward monthly bookkeeping than from a dedicated extraction pipeline, since the setup and review overhead is not worth it at low volume.

The most common problem is a bookkeeping team spending hours a week on data entry that a model can do in seconds, with the team's actual judgment reserved for coding decisions a model should not make alone. The second is a rigid OCR tool that breaks every time a vendor switches billing software, forcing someone to rebuild a template before invoices from that vendor can be processed again. The third is tax amounts entered inconsistently because different people interpret an ambiguous tax line differently; extraction flags anything unusual on tax rather than letting a guess pass through unnoticed. The fourth is lost paper trails, where the original receipt gets thrown away after entry and nobody can find it during a later dispute or an IRS inquiry; the source document stays attached to every extracted record here.

Extraction connects to QuickBooks Online, Xero, Dext, Ramp and BILL, either as a layer on top of capture tools you already use or as the capture step itself for businesses starting from a plain inbox. For high-volume AP, extracted data flows directly into an AP automation platform for approval routing and three-way match rather than sitting as an unstructured PDF someone still has to read. The specific stack is scoped to what you already run, since ripping out a working capture tool to install a different one rarely earns back the disruption it causes.

Extraction is priced by document volume as part of your bookkeeping engagement or as an AP automation add-on; current tiers and add-on pricing are published on the pricing page. Setup includes the initial accuracy sample against your real documents and threshold tuning, which is a one-time cost, and ongoing pricing scales with monthly document count rather than a flat fee that overcharges a low-volume month or undercharges a high-volume one.

We track two numbers: the percentage of documents that pass through automatically without a reviewer touching them, and the correction rate on documents that were flagged, meaning how often the reviewer actually had to change what the model proposed versus simply approving it. A rising automatic-pass rate with a stable or falling correction rate means the threshold is well tuned; if correction rates climb, the threshold gets tightened rather than left where it is.

A genuinely new vendor, a new document format, or an unusual line item like a partial credit memo on an invoice will typically score lower on its first few appearances, which is by design rather than a defect. Those documents route to a person, get corrected, and the pattern is learned from that correction, so the same vendor's next invoice scores higher once the format is no longer new to the system. Nothing about this setup assumes a document will be extracted correctly the first time; the review step exists specifically for the documents where that assumption would fail.