Where automation works best
The strongest candidates are repeatable document types with known fields: invoices, purchase orders, delivery notes, onboarding forms, insurance claims, compliance questionnaires, support attachments, and contract summaries. These documents have enough structure for reliable extraction but enough variation that old OCR templates often break.
The best first workflow is narrow. Pick one document type, one team, and one downstream system. Prove accuracy and time savings before expanding to more formats.
Typical architecture
A production workflow usually includes upload or email ingestion, OCR if the file is scanned, layout-aware extraction, LLM-based field interpretation, validation rules, human review for low-confidence fields, and export into a CRM, ERP, database, or ticketing system. Every extracted field should have traceability back to the original document.
Human review is not a failure. It is the control layer that lets the system improve safely. Low-confidence documents are routed for review; high-confidence documents move through automatically.
Cost and timeline
A focused document automation project usually costs £10,000-£30,000 and takes 4-8 weeks. A multi-document workflow with integrations, dashboards, role-based review, and audit trails can cost £30,000-£90,000. Regulated processes cost more because retention, access logs, and validation evidence need to be designed from the start.
Ongoing costs are usually OCR/API usage, model calls, storage, and support. For most SME workflows these are modest compared with staff time saved.
Accuracy and risk management
Do not measure only extraction accuracy. Measure downstream accuracy: did the right data reach the right system in the right format with the right confidence level? A field that is 98% accurate may still be risky if it controls payment amount, delivery address, or compliance status.
Use confidence thresholds by field. Supplier name can tolerate one threshold; invoice total needs another. Critical fields should require validation or human approval until the system has proven itself over enough real examples.
AyTech note: The safest projects start with a narrow, measurable workflow, then expand after real users prove the value. This keeps budgets controlled and gives Google, buyers, and stakeholders clearer proof of expertise.
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