Part of AI Automation
AI document processing
Extract, classify and validate data from contracts, forms, IDs and reports using OCR plus LLMs, with human review for low-confidence fields.
In one paragraph
AI document processing: Extract, classify and validate data from contracts, forms, IDs and reports using OCR plus LLMs, with human review for low-confidence fields. AI automation services combine large language models with workflow tools such as n8n, Zapier and Make to handle tasks that used to need a person: reading emails, extracting data from invoices and documents, updating the CRM, screening candidates and moving information between systems. Operations, finance and HR teams use it to cut manual hours, reduce errors and speed up processes.
How ai document processing fits into ai automation
Invoice and receipt processing
Extract, validate and post supplier invoices with exceptions routed to a reviewer instead of everything queued for manual entry.
Shared inbox triage
Classify incoming mail by intent and urgency, draft replies from approved content and route the rest to the right person.
CRM hygiene
Keep contacts, deals and activities current automatically from calls, emails and forms.
Candidate screening
Score applications against the job specification, schedule interviews and keep candidates informed.
How we deliver
- 1
Process mapping
We map the current process, volumes and exceptions and rank automation candidates by hours saved, error risk and feasibility.
- 2
Workflow design
Define triggers, AI decision points, validation rules and where a human approves, all documented before we build.
- 3
Build and test
Implement in n8n, Zapier, Make or custom code; test on historical data and edge cases with your team.
- 4
Controlled rollout
Run in shadow or approval mode first, compare against manual results, then switch to autonomous for proven paths.
- 5
Monitor and extend
Alerts, cost tracking and a backlog of next automations, with a runbook so your team owns it.
Frequently asked
High-volume, rules-plus-judgement tasks with clear inputs: inbox triage, data entry between systems, invoice capture, report drafting. We rank candidates by hours saved, error risk and feasibility in a short discovery so the first automation pays back quickly and builds confidence for the next.
It depends on what you already use, volumes and data sensitivity. Zapier is fastest to start; Make handles complex branching well; n8n can be self-hosted, is cheapest at scale and lets us add custom code. Where none fit, we write a small service in Python. We are not tied to any vendor.
Very high on printed, well-structured documents, and lower on poor scans or handwriting. We measure accuracy field by field on your documents, set confidence thresholds, and route uncertain fields to a reviewer. Over time the review rate falls as templates and prompts are tuned.
Scoped credentials, dry-run and approval modes, validation rules for anything that writes to finance or customer systems, and full logging. Autonomy is expanded only after the metrics justify it, and every workflow has an owner and an alert. For finance and HR flows we add reconciliation checks that compare automated output with source records before anything is posted.
Related work
Outcomes, not demos.
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