Document Processing Services
Read, extract, and classify documents at scale
If your team reads invoices, contracts, PDFs, and emails by hand and types the data somewhere else, we can automate that. Structured output from unstructured input, no templates required.
What we build
Most document work comes down to someone reading a PDF and typing what they see into another system. That is the work we remove.
- Invoice and receipt extraction. Line items, totals, dates, and supplier information go straight into your accounting system instead of passing through a clipboard.
- 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, not on who happened to be sorting the inbox that day.
- 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.
- Resume screening. Candidates ranked against your criteria, with the reasoning attached so the hiring team can trust the order and challenge it when they disagree.
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.
- 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 common situation
Someone on the team opens each invoice, reads the amounts and dates, and types them into the accounting system. It takes hours, mistakes happen, and the pile only grows.
How we'd approach it
Get a sample of real documents, train an extraction pipeline on the actual formats you receive, add validation rules for the fields that matter most, and connect the output to where the data needs to go.
What you'd get
A pipeline that reads, extracts, and delivers structured data. A technical plan for scaling. Numbers on processing time and accuracy after running against your real documents.
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. For English or Portuguese contracts, 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 Portuguese text well, 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, etc. 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 (1-2 weeks) locks scope and price before we build the prototype. A pilot extraction pipeline typically ships in 2-4 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.
Our differentiators
- Working prototype before any long-term decision
- No lock-in: you keep all code and documentation
- Projects start in days, not weeks
Let's talk about your case
Talk to the Lab
Describe the challenge in a few lines. We'll get back to you to discuss next steps.
What happens next
- 30-min call, no commitment
- Diagnostic in 1-2 weeks
- Working prototype in 2-4 weeks, technical plan in 1 week
Start here