B2B SaaS in Austria Cuts First-Response Time 94% with RAG Ticket Triage

Background: A 30-Person B2B SaaS Firm in Vienna

This case study is a composite based on patterns observed in the field. We do not fake named customers. The company is a 30-person B2B SaaS vendor based in Vienna, selling a project-management tool to mid-market clients across DACH. The stack runs on AWS, with a custom helpdesk built on top of a commercial ticketing platform. Google Workspace handles email, calendar, and document storage. The team is lean: four engineers, two product managers, one operations lead, and a part-time compliance officer. The company holds ISO 27001 certification, which constrains where customer data can be processed and stored. The operations team handles roughly 180 support tickets per week, with a median first-response time of 4 hours and a 12% mis-routing rate. The CEO had set a target: cut first-response time below 30 minutes within a quarter, without adding headcount.

Challenge: 4-Hour First-Response Time and ISO 27001 Constraints

The operations team was drowning in repetitive triage work. Every incoming ticket required a human to read it, classify it by product area, assign it to the right engineer, and draft a first response. The 12% mis-routing rate meant tickets bounced between teams, adding 2-3 hours of dead time per mis-routed ticket. The compliance officer flagged that any AI solution had to respect ISO 27001 controls: customer data could not be sent to unvetted third-party processors, and the data-processing agreement had to be in place before any model touched production data. The deadline was tight: the CEO wanted a measurable improvement within two weeks, not a six-month transformation. The team had no in-house ML expertise. They needed a partner who could audit the process, build a working pilot, and hand over a managed operation without requiring the client to hire a data-science team.

Approach: RAG Assistant on OpenAI API with Human-in-the-Loop

Forfis started with a process audit that mapped the ticket lifecycle from intake to resolution. The audit identified three high-leverage automation points: ticket classification, routing, and first-response drafting. The pilot scope was fixed: a retrieval-augmented knowledge assistant that ingested the company’s product documentation, past resolved tickets, and Google Workspace emails. The assistant used the OpenAI API for classification and drafting, with a human-in-the-loop approval step for any ticket touching billing, data deletion, or contract terms. The integration plugged into the existing helpdesk and Google Workspace through their APIs, not a replacement. The architecture was model-agnostic: if the compliance officer later required on-premises processing, the stack could swap to an open-weight model without re-architecting the integration layer. The pilot ran for two weeks, with a measured before/after baseline on first-response time, mis-routing rate, and escalation rate.

Outcome: 94% Faster First Response in Two Weeks

After two weeks, the pilot showed a 94% reduction in median first-response time, from 4 hours to 22 minutes. The mis-routing rate dropped from 12% to 3%. Agent escalation rate fell by 40%, because the assistant handled routine queries without human intervention. The human-in-the-loop approval step caught 14 tickets that required manual review, all of which were billing or data-deletion requests. The compliance officer confirmed that no customer data left the approved processing boundary. The operations lead reported that the team could now focus on complex escalations instead of triage. The CEO approved full rollout to all product lines. The engagement moved to managed AI operations, with Forfis monitoring model performance, updating the knowledge base, and handling API changes. The client did not hire a data-science team; the managed operation absorbed that responsibility.

Lessons for Similar Teams

• Fix the process before the model. The audit identified that 60% of mis-routes came from ambiguous ticket categories, not from model error. Renaming three categories cut mis-routes by half before the model even ran.
• Human-in-the-loop is not optional for compliance. The approval step for billing and data-deletion tickets was the difference between a compliant pilot and a liability. ISO 27001 auditors accepted the design because the human approval was logged and auditable.
• Model-agnostic architecture protects you from regulatory shifts. The client could swap from OpenAI to an on-premises open-weight model if a regulator required it, without rewriting the integration layer. This flexibility was a selling point in the compliance review.
• Two weeks is enough for a pilot if the scope is fixed. The team resisted the urge to expand the pilot to include voice or email drafting. Staying on ticket triage and routing kept the timeline realistic and the metrics clean.
• Managed operations beat one-off delivery. The client did not have the in-house capacity to maintain the model, update the knowledge base, or handle API deprecations. The managed operation model removed that burden and kept the system running at pilot-level performance.