Invoice and receipt processing

Invoices and receipts are structured documents pretending to be unstructured ones. Every invoice has the same kinds of fields, amounts, dates, line items, vendor details, but they're laid out differently by every supplier, arrive as PDFs and images and scans, and have to be turned into clean structured data before any system can use them. Building that extraction, reliably, across every format a real stream of invoices throws at you, is a surprising amount of infrastructure for something that feels like it should be simple.

This page is for developers building invoice or receipt processing into a product, an AP automation tool, an expense system, a bookkeeping feature, without building the extraction layer themselves. Extract turns invoices and receipts into text, metadata, and structure through one API, Workers process new ones on a schedule, and the results are stored as searchable Resources. You build the product; the extraction is handled.

The problem

The core difficulty is variety. Invoices share a conceptual structure but not a layout, so extraction that works on one supplier's format breaks on another's. Receipts are worse, crumpled, photographed at an angle, thermal-printed and faded. Handling the real range means more than a single parser; it means something that copes with scans, images, and PDFs and pulls the right information out regardless of where on the page it sits. Building and maintaining that is a standing engineering cost, and it's entirely undifferentiated, every product that processes invoices needs the same thing.

Then there's the ongoing nature of it. Invoices and receipts aren't a one-time batch; they arrive continuously. A processing system has to handle a steady stream, picking up new documents as they come rather than waiting for someone to run a job. Building the scheduling and the pipeline to do that reliably, with retries when something fails and notifications when work completes, is more infrastructure on top of the extraction itself.

And the output has to be usable and findable. Extracted invoice data needs to go into whatever system consumes it, and often needs to be searchable afterward, to pull up a past receipt, to find invoices from a vendor, to reconcile against records. Producing structured output, processing on a schedule, and keeping results searchable is the full build standing between raw documents and invoice processing that works. None of it is the product you're trying to ship.

What Exabase unlocks

With extraction as a managed API and scheduled processing built in, invoice handling becomes a feature you call rather than a pipeline you operate.

You submit a document and get structured data back. Invoices and receipts go through one endpoint regardless of format, scan, image, or PDF, and come back as clean text with metadata and structure, so you stop maintaining parsers per layout and stop caring whether a given receipt is a crisp PDF or a phone photo. The variety becomes the service's problem.

New documents get processed automatically. Workers handle the continuous stream, picking up and processing new invoices on a schedule rather than waiting for a manual run, so the processing keeps pace with the documents arriving without you building the scheduling. Failed jobs retry and completion notifications come through, so the reliability machinery is part of it.

And the results are stored and searchable. Extracted invoices land as Resources, indexed so you can search past invoices and receipts when you need to find one. The extraction connects straight into a searchable store rather than dead-ending as output you have to put somewhere yourself.

How it works

Three primitives carry invoice processing: Extract for the structured extraction, Workers for the ongoing stream, and Resources for storage and search.

Extract

Extract is the core. You submit an invoice or receipt in any supported format and get back clean text along with metadata and structure, so the fields you need come out regardless of the document's layout or quality. It handles scans, images, and PDFs through the same interface, and the production concerns are built in: jobs retry on failure and webhooks notify you on completion. The general case is covered in document extraction at scale.

Workers

Workers handle the fact that invoices keep arriving. A Worker runs on a schedule and processes new documents as they come in, so the pipeline keeps up with the stream without anyone triggering each run. This is what turns one-off extraction into standing invoice processing, the same scheduled-processing engine behind self-maintaining knowledge bases, applied to a continuous document flow.

Resources

Resources store the processed invoices and receipts. Each becomes a Resource, indexed for Deep Search, so past documents are searchable, finding a receipt, pulling up a vendor's invoices, reconciling against records, rather than the extracted data vanishing into a one-way pipe. Storage and searchability come with the extraction rather than as a separate system.

Example architecture

The flow is submit, process on a schedule, store.

Submit documents. Send invoices and receipts to Extract as they arrive, individually or in batches, and get back structured text and metadata. Register a webhook to be notified on completion rather than polling.

Process the stream automatically. Set up a Worker to pick up and process new documents on a schedule, so the pipeline keeps pace with incoming invoices without manual runs.

Store and search. Write results to Resources, indexed for Deep Search, so past invoices and receipts are findable later.

Isolate per customer if needed. For a product processing invoices on behalf of multiple customers, give each their own Base so their financial documents stay isolated.

Documents come in through one endpoint, Workers keep the stream processed, and results land as searchable Resources. The invoice-processing infrastructure you'd otherwise build and run is the service.

What compounds over time

Treating invoice processing as infrastructure pays off as volume grows and as the processed history accumulates.

Every invoice processed becomes part of a searchable record, processed once and findable afterward, so the history of what's been handled becomes an asset rather than just throughput. With Workers keeping the stream processed, volume can grow without a matching growth in operational effort, the pipeline that handles ten invoices a day is the same one that handles ten thousand. As the range of formats you encounter widens, that's variety the service absorbs rather than parsers you add.

The build-it-yourself path runs the other way. A parser-per-format extraction layer gets more brittle as suppliers and formats multiply, the scheduling and retry machinery needs more attention as volume climbs, and the whole thing is ongoing engineering that scales with your document flow rather than staying flat, all of it undifferentiated work that isn't your product. Extraction and scheduled processing as a managed service means that work stays the service's problem while your volume grows.

Who's building this

Developers building AP automation, expense management, bookkeeping and accounting tools, and any product feature that has to turn invoices or receipts into structured data, especially where documents arrive continuously and processing has to keep up.

The general extraction case is document extraction at scale, and the scheduled-processing pattern is shared with self-maintaining knowledge bases. If you're building this as a product for multiple customers, multi-tenant memory for SaaS covers keeping each customer's financial documents isolated. For a hands-on starting point, the simple document extraction tool example builds the submit-and-retrieve loop end to end.

Get started

Start with the getting started guide, then about extraction, submitting jobs, and retrieving results for the core flow, extraction webhooks for completion notifications, and creating Workers for scheduled processing. There's a free tier to build against.

FAQs

What do I get back when I submit an invoice?

Clean text along with metadata and structure, extracted through Extract. Because it works on meaning and layout rather than a fixed template, the relevant information comes out regardless of how a particular supplier formats their invoice.

Does it handle receipts and scans, not just clean PDFs?

Yes. Extract handles scans, images, and PDFs through the same API, so a photographed or thermal-printed receipt is processed the same way as a digital invoice.

How do I process invoices that arrive continuously?

Use a Worker to pick up and process new documents on a schedule, so the pipeline keeps pace with incoming invoices without you triggering each run. This is the same scheduled-processing pattern as self-maintaining knowledge bases.

Can I search past invoices and receipts later?

Yes. Processed documents are stored as Resources and indexed for Deep Search, so you can find a past receipt, pull up a vendor's invoices, or reconcile against records rather than losing the extracted data into a one-way pipe.

What happens when an extraction job fails?

Jobs retry on failure, and webhooks notify you on completion, so you don't build the retry and notification machinery yourself. The reliability handling is part of the service.

Can I keep different customers' financial documents separate?

Yes. Give each customer their own Base for full isolation, following the multi-tenant SaaS pattern, if you're building invoice processing as a product for multiple customers.