The Problem: 4-Hour First-Response Times in a Swiss Fintech
A 2,000+ employee fintech in Switzerland was running support on a legacy helpdesk with a 4-hour first-response SLA. The legal and compliance team flagged that every support interaction touching payment disputes or customer PII required manual review, creating a bottleneck that scaled linearly with ticket volume. The AI maturity stage was running isolated pilots: the team had tested a single chatbot on a sandbox channel but had not measured cycle time or error rate against a baseline. The goal was to cut first-response time to under 15 minutes for routine queries while keeping human approval on anything touching money, contracts, or regulated data. The constraint was strict: regulated data could not leave the building, and the system had to satisfy ISO 27001 audit requirements for access control and logging.
Architecture: pgvector RAG with Model-Agnostic Inference
The architecture used pgvector for embeddings search over the company’s policy documents, product manuals, and CRM records. When a ticket arrived, the system generated an embedding for the query, retrieved the top-5 most similar document chunks, and passed them to the model as context. The model was model-agnostic: OpenAI’s GPT-4o handled non-sensitive drafting tasks via API, while an open-weight Llama 3 70B model ran on the client’s own GPU hardware for anything involving customer PII or transaction data. The integration layer used custom REST APIs and webhooks to pull ticket data from the existing helpdesk, push drafted responses back, and trigger approval workflows. No existing system was replaced; the AI layer sat on top of the CRM, ERP, and helpdesk through their native APIs.
The 4-Week Pilot: Scope, Baseline, and Approval Workflow
The pilot ran for 4 weeks on a single support channel with a limited document set of 200 policy and product documents. Week 1 covered the process audit: mapping ticket categories, identifying the top 5 highest-volume workflows, and defining the approval rules. Weeks 2-3 handled integration and model tuning: wiring the REST API to the helpdesk, building the pgvector index, and calibrating the retrieval threshold. Week 4 measured the before/after baseline: cycle time, error rate, and escalation rate. The workflow orchestration layer ensured that any ticket flagged as high-risk (payment dispute, contract amendment, health data) routed to a human before any response was sent. Routine queries were auto-approved after the model’s confidence score exceeded 0.92.
Results: 38% Error Reduction and 11-Minute First Response
The pilot measured a 38% reduction in error rate on routine queries and a 72% drop in first-response time from 4.2 hours to 11 minutes. The cost per support ticket fell by 22% in the pilot channel, driven by fewer escalations and reduced manual drafting time. The legal and compliance team reviewed every model output during the pilot and flagged 3 cases where the RAG retrieval had pulled an outdated policy document; the fix was a versioning tag on the pgvector index so the model always retrieved the current document. The candidate screening use case, tested in parallel, reduced time-to-screen from 3 days to 6 hours, with a recruiter approving every shortlist decision. The pilot’s success criteria were met on all three metrics: cycle time, error rate, and compliance audit trail completeness.
Rollout and Managed Operations: From Pilot to Production
Post-pilot, the organization moved to managed AI operations: continuous monitoring of model performance, drift detection on the pgvector index, prompt and embedding updates, and SLA management. The vendor handled model versioning, retraining when accuracy dropped below the 0.92 threshold, and compliance reporting for ISO 27001 audits. The rollout expanded to three additional support channels over 8 weeks, with each channel running as an isolated pilot before scaling. The legal and compliance team reviewed each new use case’s data handling, model selection, and approval workflow before go-live. The managed operations contract included monthly accuracy reports, quarterly compliance reviews, and a 4-hour incident response SLA for model degradation or data breach events.
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