AI workflow proof-of-concept for document processing
Context
An operations team processing a high volume of inbound documents — invoices, contracts, forms — was handling each one manually: opening the file, extracting key fields, entering data into the CRM, and routing to the right team member. The process was accurate but slow and entirely dependent on staff availability.
The problem
Manual document processing created a bottleneck that scaled with headcount, not with volume. Each document took an average of 12–15 minutes of staff time. Errors from manual re-entry were common, and there was no audit trail linking the original document to the data entered downstream.
What we built
An n8n pipeline triggered on inbound email attachments. OCR handling for scanned documents. An LLM extraction step using Claude that pulls structured fields and returns a confidence score per field. Automatic routing to the right team member based on document type and extracted metadata. Low-confidence extractions flagged for human review rather than silently passed through.
Outcome
Processing time per document fell from 12 minutes to under 45 seconds. The team reallocated the recovered hours to higher-value review and exception-handling work. The audit trail linking source document to downstream data resolved a recurring compliance question.