Project Name
Scaling a B2B SaaS Support Desk to 79% Ticket Auto-Resolution via AI Agents
Our client is a mid-market B2B SaaS vendor serving customers across multiple industries, with a growing installed base and a lean support organisation. As the customer base scaled, ticket volume grew faster than the support team could hire, and leadership needed a way to absorb routine ticket volume without sacrificing response quality on complex cases or creating the consistency problems that come with headcount-driven scaling.
The organisation had a well-developed help-centre knowledge base and an established helpdesk platform but no mechanism to surface that content automatically or to apply consistent triage and escalation logic across the full ticket queue.
As ticket volumes grew, the support team faced increasing pressure to respond faster without letting routine requests consume the time needed for complex customer issues.
- Tier-1 Tickets Consuming Specialist Time: Password resets, plan questions, and basic configuration requests were taking time away from specialists handling complex technical issues, with no mechanism to separate the two.
- Response Times Slipping Under Volume: Average first-response time reached 6.5 hours during peak periods as ticket volume outpaced support capacity, with no sustainable short-term hiring solution.
- No Consistent Triage Before Assignment: Tickets were assigned largely by queue order rather than urgency or complexity, delaying attention on high-severity issues.
- Knowledge Base Underused: Existing help-centre content was not surfaced effectively to agents or customers, leaving documented answers underused at critical points in the support workflow.
- Escalation Criteria Were Inconsistent: Agents relied on individual judgment for escalations, resulting in inconsistent handling and delayed attention for high-value or high-risk accounts.
- Limited Reporting on Resolution Drivers: Support leadership lacked visibility into which ticket categories consumed the most time, making it difficult to prioritise automation and process improvements.
Ksolves, an AI-first technology company offering AI and ML consulting services, combined AI-driven triage, knowledge-grounded responses, and human oversight to automate routine support while keeping complex issues with the right specialists.
- Ticket Triage and Classification Agent: Incoming tickets are classified by category and urgency using historical data, routing each to the right resolution path before human review.
- Knowledge-Base-Grounded Response Generation: Responses are generated from the existing help-centre content using Retrieval-Augmented Generation, keeping answers accurate, supported, and traceable to source documents.
- Autonomous Resolution for Defined Low-Risk Categories: A defined set of low-risk requests, such as password resets and billing FAQs, can be resolved automatically without human review.
- Human Review Queue With Pre-Drafted Responses: Tickets requiring human judgment are routed to agents with a pre-drafted response, reducing handling time and eliminating the need to start from scratch.
- Escalation Rules Engine: Account value, sentiment, and repeat-contact signals trigger defined escalation rules, ensuring complex or sensitive cases reach specialists consistently and on time.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | Agentic AI Orchestration Framework |
| AI/ML | Retrieval-Augmented Generation (RAG) |
| Integration | Helpdesk Platform API |
| AI/ML | Large Language Model |
| Infrastructure | Cloud Hosting & Monitoring |
The solution transformed support from a specialist-heavy, queue-driven model into a faster, more automated workflow focused on the issues that truly need human expertise.
- 79% of Eligible Tickets Auto-Resolved: The agent now resolves 79% of defined low-risk tickets automatically, compared with 0% before implementation.
- First-Response Time Cut by 70%: Average first-response time fell from 6.5 hours during peak periods to under 2 hours across ticket categories, without adding support headcount.
- Specialist Time Reclaimed by 30 Percentage Points: Specialist time spent on Tier-1 tickets dropped from an estimated 35% to roughly 5%, freeing capacity for complex and high-value cases.
- 95% Escalations Within Defined SLA: 95% of qualifying tickets are now escalated within the defined time window, replacing inconsistent agent-led decisions with a measurable, standardized process.
Ksolves transformed a specialist-heavy support model into an AI-assisted workflow that resolves routine tickets automatically and equips agents with knowledge-grounded responses for everything else. With 79% of eligible tickets auto-resolved, first-response time reduced by 70%, and 95% of qualifying escalations meeting SLA, specialists can now focus on complex, high-value customer issues while the support operation scales more efficiently.
How Much of Your Support Queue Could Resolve Itself Before It Reaches a Specialist?
