4-Week AI Pilot: Invoice Processing and RAG Assistant for a B2B SaaS in Austria

The Problem: Manual Back-Office Work and Slow First-Response in a 201-500 Employee B2B SaaS

Your operations and supply chain team in Vienna processes 1,200 invoices monthly, each taking 14 minutes of manual data entry, and your support desk answers 300 tickets a week with a median first-response time of 4.2 hours. The back-office work is repetitive, error-prone, and consuming 3.5 FTEs that could be redeployed. The EU AI Act, in force since August 2024, requires you to document your AI risk assessment before deploying any automated system that touches financial data. You need a fixed-scope pilot that delivers a measured before/after baseline in 4 weeks, not a 6-month transformation program. The pilot must work within your existing stack — Notion for documentation, your CRM for customer records, your ERP for invoice data — and must keep regulated data on Austrian infrastructure.

Prerequisites Before Step 1

• Process audit completed: You have mapped the invoice processing workflow from receipt to payment, timed each step, and counted error types. The audit output is a one-page document with baseline metrics: average cycle time (hours), error rate (%), and FTE hours consumed.
• n8n instance deployed: A self-hosted n8n instance runs on your Austrian cloud or on-premises server. You have API credentials for your CRM, ERP, and helpdesk. The n8n version is 1.40 or later for stable webhook and AI node support.
• RAG source material ready: Notion or Confluence contains at least 50 pages of operational documentation — vendor onboarding, invoice coding rules, escalation paths, SLA definitions. The content is current (updated within the last 30 days).
• Human-in-the-loop approvers identified: You have named 2–3 people who will approve AI-drafted invoice entries and ticket responses. They understand the approval criteria and have access to the n8n approval UI.
• EU AI Act risk assessment drafted: A one-page document classifying your RAG assistant as a limited-risk system, noting the transparency obligations, and confirming no special-category data is processed without consent.
• Fixed-scope statement of work signed: The pilot scope, success metrics, and 4-week timeline are locked. No scope changes without a change order.

Step 1: Run the Process Audit and Lock the Baseline

Run a 2-hour process audit with your operations lead. Map every step from invoice receipt (email, portal, or EDI) to payment posting in the ERP. Time each step with a stopwatch or screen-recording tool. Count error types over the last 30 days: wrong vendor code, duplicate entry, missing tax ID, incorrect tax rate. Record the baseline: average cycle time in hours, error rate as a percentage, and total FTE hours consumed. Output: a one-page audit document with a workflow diagram and a table of error types with frequencies. This document is your before/after measurement anchor. Do not proceed to Step 2 until the baseline is signed off by the operations lead.

Step 2: Build the n8n Invoice Extraction Workflow

Build the n8n workflow for invoice extraction. Create a webhook node that receives the invoice PDF via email or ERP API. Add an AI node using OpenAI’s GPT-4o or Anthropic’s Claude 3.5 Sonnet for extraction — these models handle multi-column invoice layouts with 94–97% field accuracy on standard B2B invoices. Configure the extraction schema: vendor name, vendor tax ID, invoice number, line items, tax rate, total amount, due date. Add a validation node that checks for missing fields and flags anomalies (e.g., tax ID format mismatch, total exceeds PO amount by more than 5%). Route flagged invoices to a human approval node in n8n; route clean invoices to the ERP write-back node. Test with 50 historical invoices before going live.

Step 3: Build the RAG Knowledge Assistant Over Notion or Confluence

Set up the RAG index over your Notion or Confluence documentation. In n8n, create a workflow that pulls pages on an hourly schedule using the Notion API node or Confluence Cloud API. Chunk the content at 512 tokens with 64-token overlap. Embed using BGE-M3 or Cohere embed-v3 — both handle English and German, which matters for your Austrian team. Store embeddings in pgvector on your PostgreSQL instance. Build the RAG query workflow: receive a ticket or question, retrieve the top-5 chunks, pass them as context to the LLM, and return a grounded answer with source citations (page title and URL). Test with 20 real questions from your support team. If retrieval hit-rate is below 85%, re-chunk or re-embed. The RAG assistant must never answer without a source citation.

Step 4: Integrate with CRM, ERP, and Helpdesk

Integrate the n8n workflows with your existing systems. For the invoice workflow: connect the ERP write-back node to your ERP’s API (SAP, NetSuite, or similar) using the vendor’s REST or SOAP endpoint. For the RAG assistant: connect the helpdesk (Zendesk, Freshdesk, or Jira Service Management) via webhook so that incoming tickets trigger the RAG query workflow. The RAG workflow drafts a response, attaches the retrieved context, and routes it to the human approver. The approver edits or approves in the n8n UI, and the approved response sends via the helpdesk API. All integrations use your existing API credentials — no new accounts, no new systems. Test each integration with 10 real transactions in a staging environment before moving to production.

Step 5: Run the 4-Week Pilot with Human-in-the-Loop Approval

Run the pilot in shadow mode for 2 weeks. The n8n workflows process real invoices and tickets, but the human approver reviews every output before it reaches the ERP or the customer. Track three metrics daily: cycle time (from invoice receipt to ERP posting, or from ticket creation to first response), error rate (AI-drafted entries rejected or edited by the approver), and human-override rate (percentage of AI outputs that required manual correction). At the end of 2 weeks, compare against the Step 1 baseline. The pilot report must show: cycle time reduction in hours, error rate change in percentage points, and FTE hours saved. If cycle time drops by 40% or more and error rate stays below 5%, the pilot is a success. If not, diagnose the failure mode before proceeding to rollout.