Outcome
Expected results within 4 to 6 months:
- 30% of incoming inquiries handled without human intervention
- 40% faster first response time
- 25% faster average ticket resolution time
- 25% fewer escalations
- 20% higher agent productivity
- Deferred need to hire 2–4 additional support staff
Business impact
At 4,500 tickets per month, 30% automated handling means roughly 1,350 fewer tickets for the human team each month.
If average ticket handling time is 12 minutes, that saves approximately 270 hours per month.
In practice, that means:
- less repetitive workload
- experienced agents focusing on complex cases
- customers getting faster service
- the company growing without proportionally growing support costs
What AIIA actually did
AIIA didn't just configure software.
The team designed the service model, structured the knowledge base, identified where automation makes sense and where it doesn't, and deployed an AI system tied directly to operational metrics.
The end result isn't "an AI chatbot." The end result is a customer support operation that can absorb growth without breaking.
In brief
AIIA helped this SaaS company reduce repetitive support work, speed up service, and increase team capacity — without hiring at the same pace the business was growing.
Details
AI customer support for a mid-sized SaaS company
Summary
A growing B2B SaaS company wanted to improve customer support efficiency without scaling the team at the same rate as incoming volume. AIIA designed and deployed an AI system in Intercom that reduced repetitive work, improved response speed, and made the team more scalable.
Client
- Mid-sized B2B SaaS company
- 120 employees
- 18-person support team
- 4,500 tickets per month
- Customers across Europe and North America
Challenge
Customer support was becoming increasingly difficult to scale.
A large proportion of incoming tickets were repetitive. Agents were searching through help articles, internal notes, and old tickets to answer the same questions. Escalations were too frequent, response times were growing, and leadership felt pressure to hire just to maintain the current service level.
The company wasn't looking for another chatbot. They were looking for a working system that would improve support by measurable indicators.
Solution
AIIA deployed an AI customer support layer directly within the Intercom environment.
The solution had three main parts:
AI customer-facing assistant Handled typical questions — login issues, basic billing and subscription questions, onboarding steps, product usage questions, and standard policy and process cases.
AI agent assistant Summarised conversations, suggested responses, surfaced relevant knowledge base content, and prepared escalation notes.
Automatic triage and routing Classified incoming tickets, distinguished routine from complex cases, routed tickets to the correct queue, and sent unclear or risky cases directly to a human.
Why this approach worked
AIIA didn't treat AI as a plug-in tool.
The project started with analysis of the actual support process, cleaning and structuring the knowledge base, clarifying escalation rules, and setting clear automation boundaries. This meant the AI layer only worked with verified knowledge and only where it could reliably add value.
Intercom was chosen as the platform to keep customer communication, agent work, and AI operations in one place.
Implementation
AIIA delivered the project in five steps:
- Analysis of ticket types, support bottlenecks, and main escalation causes
- Knowledge base structuring and cleaning
- AI rollout for the most frequent and lowest-risk ticket types
- Agent AI assistant deployment
- Fine-tuning based on real results, escalations, and team feedback
Starting point
Before the deployment:
- First response time: 7 hours
- Average resolution time: 19 hours
- Monthly volume: 4,500 tickets
- Repetitive or partially repetitive cases: approximately 60%
- Escalations: high enough to burden senior team members
- Outlook: likely need for new hires within 12 months