Document Processing Services
Documents you can check
A file becomes fields the next system can use. A rule holds a row when the numbers do not support it. Each field cites the page it came from.
Examples
The engineering problem, in a few shapes. A file comes in, a field has to be right, and the next system has to accept the row.
- Invoice and receipt extraction. Line items, totals, dates, and supplier information go straight into the accounting system, with the fields validated.
- Contract analysis. Obligations, risk clauses, renewal dates, and financial figures surface in seconds, each one cited back to the passage it came from.
- Automatic classification and routing. Incoming files land in the right queue based on what they contain.
- Structured output from messy input. JSON, CSV, or direct writes to your database, with validation rules on the fields that actually matter for downstream processing.
Technologies
We pick the stack per job. Most pipelines mix a language model for reading with deterministic code for validation and delivery. That balance keeps accuracy high and cost predictable.
- Language models. OpenAI, Claude, Gemini, and open-source options like Llama, Mistral, Gemma, and Qwen, selected per task based on accuracy, cost, and privacy requirements.
- OCR. Modern open-source options like PaddleOCR, Qianfan-OCR, and Tesseract when we can self-host, Google Vision or AWS Textract when scan quality is bad enough or volume is high enough that the managed route is worth it.
- Small models. We look for the cheapest OCR model that is still good enough for the pages and that someone can run on their own computer. We publish that test as open source, in the repo.
- Classical ML. Scikit-learn and XGBoost for narrow classification problems where a large model is overkill and a smaller one is cheaper and faster.
- Validation pipelines. Confidence thresholds and rule checks that catch uncertain extractions before they reach your downstream systems.
- Delivery. Webhooks, direct database writes, or queue-based handoffs, matched to how your systems actually expect to receive data.
How we'd work on this
A technical situation
A citation does not support the sentence. The row still posts.
How we'd approach it
We take a sample of the real files, build the extraction for those formats, check the fields that matter, and send the output where it has to land.
What you'd get
You leave with that path running on your files, the code.
Questions about document automation
We combine OCR with a language model to read both digital and scanned invoices, extract line items, totals, dates, and supplier information, then deliver the data straight into your accounting system via API, webhook, or direct database write. No fixed templates. It handles supplier-by-supplier layout variation.
Yes. We extract obligations, financial figures, renewal dates, and risk clauses, each one cited back to the source passage. Useful for legal review, due diligence, and renewal tracking.
It depends on scan quality. For native PDFs we use direct extraction. For scans, modern open-source OCR has closed most of the gap. PaddleOCR and Qianfan-OCR handle a wide range of layouts, including rougher scans where Tesseract used to struggle. When requirements push past what open-source can do reliably, Google Vision or AWS Textract are the managed fallback. The choice goes into the technical plan based on your real documents.
We train a classifier on a sample of your documents so that every incoming file (by email, upload, or shared folder) lands in the right queue: accounts payable, legal, HR, and so on. The model learns from the document types you actually receive, not generic categories.
It depends on volume, layout variety, and where the data needs to land. The initial diagnostic (3 to 5 days) locks scope and price before we build. The extraction typically ships in 1 to 3 weeks.
Yes. We sign NDAs, process documents in a controlled environment, and when privacy requires it, we use open-source models (Llama, Mistral, Gemma, Qwen, and others) running on your infrastructure or ours, with no data sent to external APIs.
What you get
- A working fix on your data
- You keep the code
- 2 to 5 weeks when the owner and the files are available
Let's talk about your case
Talk to the Lab
Tell us the problem in a few lines. What the team does today, and where it breaks. We reply the same business day.
What happens next
- We reply the same business day
- Diagnostic in 3-5 days
- Working fix in 1-3 weeks. Technical plan in the last days, with the work done.
Start here