Inconsistent inputs
Invoices, forms, scanned PDFs, photos of paper — every source has its own format, quality, and quirks. A pipeline tuned for one breaks on the next. Real-world documents don't behave like demos.
Document Processing
Your documents contain signal. Extracting it reliably is the hard part.
High-volume document extraction with confidence scoring, quality monitoring, and human review built in — across any format, at any scale, without the runaway API bill.
The reality
Most document pipelines work fine. Until they don't.
Silent failures at scale
At high volume, errors aren't edge cases — they're guaranteed. The question is whether you catch them or your client does. Most pipelines don't tell you when they're degrading. Yours should.
Cost that compounds
Running every document through a frontier LLM works fine at a thousand documents. At a million, the bill becomes a business decision. The architecture has to account for scale from the start.
The approach
Four problems. Four concrete answers.
01 Ingestion
Format-agnostic ingestion
We ingest across formats — structured PDFs, scanned documents, photos, mixed-quality inputs — without assuming clean data. Preprocessing, normalisation, and format detection happen before extraction. The pipeline handles your reality, not a sanitised version of it.
02 Extraction
Confidence-scored extraction
Every extracted value carries a confidence signal. High-confidence outputs pass through automatically. Low-confidence outputs — ambiguous fields, degraded scans, unusual formats — get flagged and routed to human review before they reach your systems. Nothing fails silently.
03 Economics
Scale economics
Not every document needs a frontier model. We route based on complexity: simpler, well-structured documents run on smaller, cheaper models. Edge cases and low-confidence extractions escalate to heavier models only when needed. At a million documents, that routing decision is the difference between viable unit economics and an unsustainable one.
04 Quality
Quality control that runs continuously
Batch-level monitoring tracks error rates across document types, sources, and time. If a new supplier format starts degrading extraction quality, you know before it compounds. Full audit trail on every output — traceable back to source, reviewable at any point.
Data privacy
Your documents don't leave your perimeter. Unless you want them to.
For sensitive data — medical records, legal documents, financial files — we offer:
01
Sovereign hosting
Processing on infrastructure in your jurisdiction, or on-premise where required.
02
NER anonymisation
Named entity recognition strips personally identifiable information before any document reaches an external model.
03
Local model deployment
Where cloud processing isn't acceptable, we deploy models that run entirely within your environment.
What you get
Structured, queryable data. In your systems.
The output isn't a file dump. Extracted data lands in a structured knowledge base — searchable, versioned, and integrated with your existing stack. Whether that's our own retrieval layer or a tool you already use, the data is yours and it's usable from day one.
In practice
From 3 days to 4 hours. Two FTEs redeployed.
A European logistics operator processing hundreds of documents daily — inconsistent formats, no audit trail, two full-time staff doing manual extraction. We deployed a managed OCR pipeline with confidence scoring and human validation checkpoints. Error rate dropped under 1%. Processing time went from 3 days to 4 hours. The two specialists moved to higher-value work.
See all case studies → Not sure where to start?
Tell us what you're processing. We'll tell you what's possible.
A free diagnostic gives you a concrete picture of your current extraction setup — what's working, what isn't, and what a reliable pipeline would cost to build.
- Fixed-price pilot. Paid on delivery.
- 30-day notice. No lock-in.
- Works with your existing stack.
- Production-grade. Not demo-ware.