Swiss E-commerce Cuts Invoice Processing to 3 Hours with On-Premise AI

Background: A Swiss E-commerce Operator at 300 Headcount

This case study is a composite based on patterns observed across Forfis engagements. We do not name real customers. The company described here is a mid-sized Swiss e-commerce operator with roughly 300 employees, running a multi-channel retail operation across DACH and Western Europe. The stack is a mix of a legacy ERP for inventory and finance, a modern CRM for customer relationships, and a helpdesk platform for internal and supplier communications. The operations team handles 1,200 to 1,800 supplier invoices per month, plus a monthly consolidated report that feeds into the finance close. The company is in the AI-native operations stage: leadership has approved AI investment, but the team has not yet built internal capability to deploy and maintain AI workflows. The engagement ran over 8 weeks, delivered by a dedicated Forfis AI team embedded with the client’s operations group.

Challenge: 12 Hours a Week of Manual Invoice Entry and a Fixed Monthly Close

The operations team spent an estimated 12 to 15 hours per week on manual invoice processing: extracting line items from PDFs, matching them against purchase orders in the ERP, flagging discrepancies, and entering validated data. The monthly consolidated report required pulling data from three systems, reconciling it, and formatting it for the finance close. The error rate on the baseline was 4.2 percent on invoice line items, with a 3-day average cycle time from receipt to posting. The pressure was twofold: the monthly close deadline was fixed, and the team had lost two senior operators to attrition in the prior quarter. Leadership wanted to reduce manual back-office work without replacing the existing ERP or CRM, and without sending supplier or financial data to a third-party cloud. The compliance posture was internal: no regulatory mandate, but the finance director required that all financial data remain on-premise.

Approach: On-Premise Open-Weight Models with a Human-in-the-Loop Approval Layer

Forfis ran a two-week process audit to map the invoice workflow end-to-end and capture baseline metrics. The pilot scope was fixed: automate invoice extraction, PO matching, and discrepancy flagging, plus generate the monthly consolidated report from the same data pipeline. The architecture used open-weight models deployed on the client’s own hardware, so all invoice and financial data stayed on-premise. The AI layer connected to the ERP and helpdesk through custom REST APIs and webhooks: the ERP pushed new invoices via webhook, the AI service processed them, and validated records were written back through the ERP’s REST API. Discrepancies were pushed to the helpdesk as tickets for human review. The human-in-the-loop layer was built into the workflow: the model drafted and classified, a person approved anything touching a financial transaction. The dedicated Forfis team handled technical planning, product design, and full-cycle development over the 8-week timeline.

Outcome: Cycle Time Down 75 Percent, Error Rate Under 1 Percent

After the 8-week engagement, the measured results were: cycle time on invoice processing dropped from 12 to under 3 hours per week, a reduction of roughly 75 percent. The error rate on invoice line items fell from 4.2 percent to under 1 percent. The monthly consolidated report, which previously took 2 to 3 days of manual reconciliation, was generated automatically from the same data pipeline and required only a 30-minute human review. The human-in-the-loop approval queue handled roughly 8 to 12 percent of invoices that required manual review, down from 100 percent. The operations team redirected the freed capacity to supplier relationship management and exception handling. The finance director confirmed that all data remained on-premise throughout the pilot and rollout, and the monthly close process was unchanged in structure but faster in execution. The system is now in managed operation with Forfis monitoring model performance and handling drift.

Lessons for Similar Teams

• Baseline before you build. The 4.2 percent error rate and 12-hour cycle time were captured during the audit, not estimated. Without that baseline, the outcome metrics would be unverifiable. Any team automating a back-office workflow should measure the current state before touching the process.
• One workflow, not five. The pilot scope was fixed to invoice processing and monthly reporting. Attempting to automate the entire back-office in 8 weeks would have diluted the team’s focus and made the baseline unmeasurable. Sequence the rollout: prove one workflow, then expand.
• On-premise is not a constraint, it is a design choice. The open-weight model on the client’s hardware was not a compromise. It was the right fit for the data residency requirement, and the model-agnostic architecture meant the team could swap models without re-architecting the integration layer.
• Human-in-the-loop is the default, not a fallback. The approval layer was built into the workflow from day one, not added after a failure. The 8 to 12 percent manual review rate is a feature, not a bug: it keeps the team in control of financial transactions while the AI handles the volume.
• Integration through existing APIs, not replacement. The custom REST API and webhook layer connected to the ERP and helpdesk without requiring data migration. This kept the project within the 8-week timeline and avoided the risk of a parallel system.