1. Start with a Process Audit, Not a Model
A German fintech with 30 employees processes 400 payment-related documents per week. The back-office team spends 12 hours a week manually extracting data from invoices and payment confirmations, with a 4% error rate that triggers reconciliation delays. Forfis starts with a process audit that maps every manual touchpoint, then selects document extraction as the pilot workflow. The fixed-scope pilot runs for six weeks, shipping with a measured baseline: cycle time drops from 18 minutes per document to 4 minutes, and the error rate falls to 0.8%. The pilot’s success criteria are explicit and tied to the audit’s findings, not vague “efficiency gains.”
2. Run the Pilot on Document Extraction
The pilot targets one workflow: extracting line items, amounts, and reference numbers from payment statements and invoices. Forfis uses an open-weight model on the client’s own hardware because PCI DSS requires cardholder data to stay within a controlled environment. The model runs on a single GPU server in the client’s Frankfurt data center. The extraction pipeline feeds directly into the existing ERP via API, so no new data store is introduced. A human reviews every extracted record before it posts to the ledger, satisfying the human-in-the-loop requirement for anything touching money.
3. Layer a Lead-Qualification Assistant on the CRM
With the back-office pilot validated, the second phase adds a customer-facing AI assistant for lead qualification. The assistant pulls from the CRM and a Confluence knowledge base to draft first-response emails for inbound leads. It classifies each lead by intent, budget range, and product fit, then flags high-value prospects for the sales team. A rep approves every outbound message before it sends. The assistant reduces initial qualification time from 25 minutes to under 5 per lead, and the sales team reports a 15% lift in response rate within the first month of rollout.
4. Keep the Stack Model-Agnostic and On-Premise
The architecture is deliberately model-agnostic. OpenAI and Anthropic APIs handle non-sensitive tasks like drafting marketing copy or summarizing meeting notes. Open-weight models on the client’s hardware handle anything touching payment data, health records, or contracts. This split lets the fintech use frontier models where quality matters most while keeping regulated data on-premise. The integration layer plugs into the existing CRM, ERP, and helpdesk through their native APIs, so no system is replaced. For a 30-person team, this means no new vendor lock-in and no migration project.
5. Scale Across Departments in the Third Month
After the pilot, the rollout extends to two adjacent departments: the finance team adopts the document extraction pipeline for vendor invoices, and the support team uses the same RAG assistant for ticket triage. The key is that each new workflow reuses the same architecture, the same on-premise model, and the same human-approval gate. Forfis ships a measured before/after baseline for every workflow: cycle time, error rate, and cost per transaction. By month three, the back-office error rate has dropped from 4% to 0.8% across all automated workflows, and the team has freed up roughly 20 hours per week for higher-value work.
6. Ship a Measured Baseline, Not a Promise
The three-month timeline works because the scope is fixed and the success criteria are measurable. The process audit takes two weeks, the pilot runs six weeks, and the rollout occupies the final four weeks. For a 30-person fintech in Germany, this means no open-ended engagement and no surprise invoices. The human-in-the-loop design means the team never has to trust the model blindly: anything touching money, contracts, or health data gets a human sign-off. The result is a back office that runs on 0.8% error rates, a sales team that responds to leads in under five minutes, and an architecture that keeps PCI DSS-compliant data on the client’s own hardware.
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