How to Automate Invoice Processing with the Dots AI API

Manually retyping invoices is the kind of work that should not exist in 2026. The Dots AI extraction API lets you build a pipeline that reads a PDF, pulls the fields, and pushes them into accounting software with almost no code.

What does the end-to-end pipeline look like?

Five stages: ingest, extract, validate, store, and reconcile. Ingest watches an inbox or folder. Extract sends each document through Dots AI against your invoice schema. Validate checks totals and dates. Store writes the JSON to a database. Reconcile matches the invoice against a purchase order. Most teams already have stages one through three partly done; the API handles the tedious middle.

How do you connect it to a workflow tool?

1. Create a webhook that fires when a new email with a PDF attachment arrives.
2. Send the file to the Dots AI extract endpoint with your schema ID.
3. Parse the JSON response for total, vendor, invoice number, and due date.
4. Run a validation rule: does line-item sum equal the stated total?
5. If it passes, create the record in your accounting API; if not, flag for a human.

Tools like n8n, Make, or a short Python script can all drive this. The Dots AI call is a single HTTP request either way.

How do you keep errors out of the ledger?

• Check field confidence: send anything below your threshold to review.
• Enforce math: reject invoices whose line items do not sum to the total.
• De-duplicate: hash the file and invoice number to catch repeats.
• Log raw responses: keep the original JSON for audit trails.

What does it cost at scale?

Pricing is per document, so a company processing a few thousand invoices a month should model the cost against the person-hours it replaces. Include the human-review minutes for flagged documents — usually 5-10% of the volume — in that math, because that is where the real cost sits.

Where does automation still need a human?

Multi-vendor layouts, handwritten notes, and disputes all need eyes. The goal is not zero touch; it is moving people from data entry to exception handling. Start with your cleanest recurring vendor and expand once the pipeline holds.

Quick Summary

• A Dots AI invoice pipeline runs ingest, extract, validate, store, reconcile.
• One HTTP call per document against your saved schema.
• Validate line-item math and de-duplicate by file hash and invoice number.
• Expect 5-10% of documents to need human review.
• Automate the clean vendors first, then expand.