What Is Being Compared
The two options under evaluation are not competing products but competing entry points into the same AI automation program. Option A, the AI process audit and roadmap, is a diagnostic engagement: Forfis maps the company’s existing workflows, measures cycle time and error rate on each, scores them by volume and data sensitivity, and delivers a 12-month automation roadmap with a fixed-scope pilot on the highest-ROI workflow. Option B, lower cost per support ticket, is an outcome-oriented engagement: the client specifies a target reduction in cost per ticket (e.g., 40% over two quarters), and Forfis designs the AI layer—triage, first-response, predictive scoring—directly against that KPI. Both engagements use the same delivery stack: n8n orchestration, model-agnostic LLM integration, Google Workspace connectors, and human-in-the-loop approval gates. The difference is where the engagement starts: from the process map or from the P&L line.
Criteria for Judgment
The comparison is judged against eight criteria that matter to a 501-2000 employee fintech operating under PCI DSS in Germany:
- Time to first measurable result — weeks from kickoff to a quantified before/after baseline
- PCI DSS compliance surface — how much cardholder data touches the AI layer
- n8n orchestration depth — how many workflow nodes, conditional branches, and API calls the solution requires
- Predictive scoring accuracy — AUC or F1 on the lead-qualification model at pilot exit
- Multilingual coverage — number of languages supported in the first release
- Google Workspace integration — email, calendar, and document access from the AI agent
- Cost per support ticket — measured reduction against the pre-pilot baseline
- Managed AI Operations scope — what Forfis operates post-go-live versus what the client’s team owns
Comparison Table
| Criterion | Option A: AI Process Audit and Roadmap | Option B: Lower Cost per Support Ticket |
|---|---|---|
| Time to first measurable result | 4 weeks (pilot go-live on one workflow) | 4 weeks (pilot go-live on support triage) |
| PCI DSS compliance surface | Low — audit phase touches no CHDE; pilot workflow selected to avoid CHDE | Medium — support tickets may reference transaction IDs; n8n workflow masks CHDE before LLM call |
| n8n orchestration depth | 15-25 nodes (audit scoring, routing, baseline measurement) | 25-40 nodes (ticket classification, first-response drafting, escalation, CRM update) |
| Predictive scoring accuracy | N/A in audit phase; scored in roadmap for future workflows | F1 ≥ 0.82 on lead-qualification subset at pilot exit |
| Multilingual coverage | 1 language (English) in pilot; roadmap adds 2-3 languages in months 2-3 | 2 languages (English, German) in pilot; additional languages in month 2 |
| Google Workspace integration | Read-only access to email and calendar for audit context | Read/write access for first-response drafting and ticket status updates |
| Cost per support ticket | Not the primary KPI; measured as secondary metric | Primary KPI; target 35-50% reduction by month 3 |
| Managed AI Operations scope | Forfis operates n8n workflows, model monitoring, and roadmap execution | Forfis operates n8n workflows, model monitoring, ticket KPI reporting, and escalation handling |
Scenario-by-Scenario Verdict
Option A wins when the company has no clear starting point. A fintech with 501-2000 employees often runs 15-30 back-office and customer-facing workflows, and the leadership team cannot tell which one will yield the fastest ROI. The audit resolves that ambiguity: Forfis measures cycle time and error rate on each candidate, scores them against volume and data sensitivity, and delivers a ranked roadmap. The 4-week pilot then targets the top-ranked workflow—often lead qualification in a payments company, because it has high volume, measurable conversion data, and no direct CHDE exposure. The roadmap gives the CFO a 12-month view of cumulative savings, which is what unblocks budget for subsequent phases.
Option B wins when the company already knows the problem. If the support desk is handling 3,000-5,000 tickets per month at an average cost of EUR 12-18 per ticket, and the VP of Customer Experience has a board-level target to cut that by 40%, the audit phase is redundant. The engagement starts directly on the support workflow: n8n classifies each incoming ticket, the LLM drafts a first response, a human approves anything touching a refund or a contract clause, and the system logs cycle time and error rate against the pre-pilot baseline. The 4-week timeline is tighter because the scope is fixed from day one.
Recommendation
For a German fintech with 501-2000 employees operating under PCI DSS, the recommendation depends on one question: does the leadership team have a named KPI with a target number? If yes—“cut cost per support ticket by 40% by Q3”—start with Option B. The 4-week pilot on support triage delivers a measurable baseline, the n8n workflow is scoped to the ticket lifecycle, and the PCI DSS data-flow review is contained to the support system. Multilingual coverage (English and German) ships in the pilot; additional EU languages follow in month 2.
If the answer is no—if the company knows AI can help but cannot say where—start with Option A. The audit identifies the highest-ROI workflow, the roadmap sequences the next three, and the 4-week pilot proves the delivery model. For a company in this size range, the audit typically surfaces lead qualification as the first pilot because it sits at the intersection of marketing and revenue, touches no CHDE, and has a clean before/after metric (conversion rate, time-to-first-response). The predictive scoring model, built on historical lead data, reaches F1 ≥ 0.82 by pilot exit and feeds the n8n routing logic that sends high-score leads to human SDRs within 2 hours.
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