AI Back-Office Automation
Documents that once took hours to key in are now processed in minutes — with an audit trail.
AI Back-Office Automation
Overview
We replaced manual data entry from invoices, contracts, and forms with an automated extraction and validation pipeline. Full document batches that used to take hours now process in minutes, transcription errors are near zero, and no document gets lost in an email thread.
The Challenge
What needed to change
Staff manually keyed data from invoices, contracts, and forms into back-office systems. It was slow, error-prone, and impossible to scale with volume — every new document meant more headcount or a longer backlog. Documents lived in email and got lost, leaving no reliable record of what was processed or approved. Manual transcription errors flowed downstream into payments and reporting, where they are far more expensive to fix.
Our Approach
How we engineered it
We built the pipeline on n8n with an LLM and OCR working together, designed so accuracy is verifiable rather than assumed. Documents are ingested, OCR'd, and passed to the model to extract structured fields, which are then validated against the client's business rules before anything is trusted. Critically, extractions with low confidence are flagged for human review instead of being silently accepted — automation handles the bulk, people handle the edge cases. Every document carries a full audit trail, so there is always a record of what was extracted, validated, and approved.
What We Built
The systems behind the result
Document ingestion and OCR
Incoming invoices, contracts, and forms are ingested and OCR'd automatically, removing the manual hand-off that lost documents in email.
Structured field extraction
An LLM extracts the relevant fields from each document and classifies it by type, turning unstructured paper into clean structured data.
Business-rule validation
Extracted data is validated against the client's business rules before it is accepted, catching errors automatically at the boundary.
Confidence-based human review
Low-confidence extractions are flagged for a person to check rather than passed through blindly, keeping accuracy high without slowing the common case.
Approval routing
Each document is routed to the correct approval queue based on its type, so the right person sees the right document without manual triage.
Full audit trail
Every step is logged, giving a complete, traceable record of what was processed, validated, and approved — and nothing is lost in email.
The Impact
Results after launch
to process a full document batch
transcription errors after automated validation
documents lost in email threads — everything is tracked
Tech & Why
The stack, and the reasoning
n8n orchestrates the flow with visibility into every step, which matters when documents drive payments and contracts. Pairing OCR with an LLM handles real-world document variety that rigid templates cannot, while rule-based validation and confidence thresholds keep the output trustworthy. PostgreSQL stores the structured results and audit trail in a system that can be queried and reconciled.
Your project
What should we build for you?
30 minutes. Tell us what you're building and we'll map exactly what it will take.
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