Case Study / AI Support Automation

AI-Supported Helpdesk Automation

A SaaS support team was burning senior time on the same onboarding, billing, and feature questions every week. Floges built an AI helpdesk layer on top of existing docs and ticket history, cutting repetitive volume by 58% in six weeks.

Client: Confidential SaaS Company Industry: SaaS & Support Operations Region: Southeast Asia Delivery: AI Helpdesk System
AI helpdesk automation system
6 Weeks
Deployed
-58%
Repetitive Ticket Volume
11 hrs → 3 hrs
Escalation Response Time

A growing SaaS company's support team was handling over 300 tickets per week. Around 65% were identical questions about onboarding, feature usage, and billing — repeated by different users every week. Senior support staff were answering the same questions daily instead of handling complex cases or running onboarding calls.

By the time a person opened a ticket, the team still had no quick way to tell whether it was routine or urgent. Expensive human attention was being spent where a reliable retrieval workflow would have done the job.

1. Diagnose Signal · Locate · Orient · Root

Analyzed 3 months of support ticket history. Identified the top 40 question patterns that accounted for 65% of total volume. Most required no human judgment to answer — just accurate retrieval from existing documentation.

2. Architect Frame · Calibrate · Blueprint · Define

Scoped the solution around an AI layer trained on the company's documentation, knowledge base, and resolved ticket history. Clear escalation paths for questions outside the AI's confidence threshold were built from day one.

3. Forge Engineer · Synthesize · Construct · Execute

Built the helpdesk AI layer connected to the client's existing support platform via API. Trained on current documentation and ticket history. Escalation routing configured with confidence thresholds and category-based routing.

4. Deploy Activate · Commission · Ignite · Release

Launched in parallel with the existing team for a 2-week shadowing period. Support team reviewed AI responses and flagged edge cases for retraining.

5. Compound Evolve · Calibrate · Refine · Optimize

Monthly retraining cycles on new tickets. Dashboard added showing unresolved volume by category, allowing the team to identify documentation gaps proactively.

AI Query Resolution
Trained on company documentation and resolved ticket history. Handles routine queries without human involvement.
Confidence-Based Escalation
Queries below the confidence threshold automatically escalated to a human agent with category tagging.
Category Routing
Escalated tickets routed to the right team member based on question type.
Unresolved Volume Dashboard
Real-time view of ticket volume by category — surfaces documentation gaps and edge case patterns.
Monthly Retraining
Continuous model refinement using new resolved tickets to improve coverage over time.

Support team answering the same questions every week? That's a retrieval problem, not a headcount problem.

Let's audit your ticket history and scope an AI layer that handles the repetitive volume automatically.