Accounts payable
Supplier invoices, receipts and scanned paperwork turned into posting-ready records. High volume, low reasoning — the shape where a cheap model wins on arithmetic alone.
Invoice & receipt OCR
Applications
Applications, not case studies.
We are a new provider. There are no customer logos on this page and no client quotes, because there is nobody we could honestly quote yet. What follows is the work itself: what goes in, what comes out, and how you would know whether it fits yours.
Why there are no case studies on this page.
A case study needs a customer who has agreed to be named, and we do not have one yet.
A composite client, a "representative deployment", an industry-average saving — any of
those would be the easiest thing on this site to write and the most expensive thing to be
caught having written. So this page describes the work. Case studies go here as customers
agree to them, with their names on them.
Six places the work shows up
Long files, mixed formats
Claims files, contracts and statements that arrive as a hundred pages with scans stapled in. A 1M-token context means the whole thing goes in one call, not a chunking pipeline.
PDF to structured dataSupport triage
Inbound tickets and email sorted, labeled and routed before a person opens them. The escalation rules stay yours; the model only does the first pass.
Data labeling at volumeCode, in your own tooling
Completion, first-pass review and test generation, inside the editor and CI you already run. No proprietary SDK to adopt and no migration to schedule.
Code assistanceTest data without real data
Synthetic datasets grown from your own seed documents and rules, so a test environment does not need a copy of production to be realistic.
Synthetic dataPhotographs and footage
Photos of goods, site images, scanned handwriting and video frames — read directly by the model. There is no separate OCR service sitting in front of it.
OCR without a separate serviceThe shape of a workload that fits
We would rather you disqualify yourself quickly than spend a fortnight on a trial that was never going to work. Across the six above, the ones that go well share four properties.
It is high volume and low reasoning
Extraction, classification, routing, reformatting. Work where a person is following a rule, and where being right most of the time beats being right slowly. This is where the arithmetic is most obviously in your favor.
You can describe "correct" to a reviewer
Not to a machine — to a colleague. If you can put twenty examples in front of someone and they agree on which are right, you can measure the model on the same twenty. If they cannot agree, the model is not the problem yet.
The input is already digital, or already scanned
Images and video go in directly, so a scan or a photograph is fine. What does not work is a process that only exists on paper and in someone's head — no model reads that.
You need a contract more than you need a sign-up
The technical work is the same either way. What changes by coming to us is that there is a counterparty who can sign, invoice and take a call — which is usually the actual blocker.
And the shape that does not
Three things we will tell you to take elsewhere.
- A handful of requests a day. At that size the official self-service route is cheaper and we would be an unnecessary layer. Go direct.
- You need a dozen different model families. We serve DeepSeek models. If your architecture depends on routing between many vendors, a multi-model router is the right tool and we are not it.
- You need fine-tuning on your own weights. We provide access to hosted models. Training and fine-tuning are a different purchase, and we would be guessing if we said we could scope it.
What we would need to give you a real answer
Not a specification — three numbers and a sentence. Monthly volume in tokens if you know it, and a rough order of magnitude if you do not. What you use today. And one example of a document or a request that is typical for you.
That is enough for us to say something useful, including "this is not a fit". It is also enough for us to answer the one number people ask for first — the rate. Our rate is 35% below DeepSeek's published list price; see how pricing works.
Questions we get about applications
Can I see a case study with numbers in it?
Not yet, and we will not invent one. What we can do is run your own documents through the model before you commit to anything, so the numbers you look at are yours rather than somebody else's. Ask for a trial key in the form.
Do you have industry-specific versions?
No. It is the same model and the same endpoint for everyone. What differs between a finance team and an insurance team is the schema they want back and the examples they judge against — both of which are configuration, not a different product.
How accurate is it on my documents?
Anyone who quotes you a percentage before seeing your documents is guessing. Accuracy depends on scan quality, layout variety and how strictly you define the output. Send twenty typical files and we will run them and show you the misses.
Can it work in languages other than English?
Yes — extraction and translation can happen in the same call, so a non-English original can come back as a record in the schema you defined. Tell us which languages matter and we will run your samples in them.
FINANCE OPERATIONS
Input
Supplier invoices, at volume
accounts payable intake
DeepSeek V4.1 Flash
Output
Posting-ready records
into your ledger workflow
- High-volume, low-reasoning work
- One monthly invoice to book
- Terms your controller can accept
Tell us what you're running.
Send us your monthly volume and what you use today. We usually reply within one business day with a price and a named contact.