Document processing automation
Turn PDFs, scans, and email attachments into structured data your systems can act on—contracts, invoices, and compliance packs included.
The Problem
Manual re-keying is slow and error-prone. OCR plus LLMs help, but only with validation steps and clear ownership when the model is unsure.
Our Approach
Python services for extraction and normalisation, confidence scoring, human review queues, and downstream workflows that only trigger when fields pass validation.
How it works
We classify the documents you process — invoices, contracts, KYC packs, bills of lading — and define the structured schema each one feeds into.
Documents are pulled from email, shared drives, or upload portals; OCR runs first for scanned content; LLM extraction maps fields to schema.
Every extracted field carries a model confidence score. Below threshold, the document is routed to a human review queue with the source page highlighted.
Business rules (sums match line items; dates fall within contract window; supplier exists in vendor master) gate downstream actions.
Validated records flow into your accounting, CRM, or document-management system via API — never manual re-keying.
Every extraction, edit, and approval is logged with the user, timestamp, and source document version. Auditors get a clean trail without you assembling it.
Frequently asked questions
What document types do you handle?+
Invoices, purchase orders, contracts, KYC packs, bills of lading, insurance schedules, and compliance certificates are common. New types take 1–2 weeks to add.
How accurate is the extraction?+
97%+ on structured formats (invoices with consistent layouts); 90–94% on semi-structured (contracts). The confidence-routing layer means inaccurate fields never silently flow downstream.
Where does the data live?+
By default, extracted data lives only in your destination system (accounting, CRM). The intermediate processing can run in UK or EEA regions, and we offer self-hosted variants for strict-residency cases.
Can it learn from corrections?+
Yes. Human review corrections are captured and feed weekly evaluation runs that surface systematic extraction errors for prompt or schema tuning.
How does it handle unusual document formats?+
Unknown formats are routed to human review and flagged for taxonomy expansion. We do not silently extract what we cannot validate.
Typical stack
Results you can expect
500 documents processed per hour (vs 30–40 manually)
Field extraction accuracy > 97% on structured invoice formats
Human review queue reduced by 70% after first 30 days
End-to-end processing time cut from 2 days to under 10 minutes
Example locations
Industries
Integrations
Typical timeline
Live in 21 days
From kickoff to a feature-flagged production rollout for a single channel. Multi-channel and regulated deployments take longer; we always agree the cut-off date in the SOW before any code is written.
“The unlock with document processing automation is not the model — it's the evaluation harness, the escalation path, and the audit trail. We build all three from day one so the system holds up under real workload, not just the demo.”
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