Company Profile
Company Type
Fintech startup
Team Size
20-100 employees
Industry
Finance
Key Challenge
Struggling with inefficient manual ticket routing processes that were slowing growth and increasing operational costs. Their primary concern was audit readiness.
Tools Connected
The Challenge
Manual ticket routing was the biggest bottleneck in this fintech startup's operations. Their team of 20-100 employees processed hundreds of ticket routing requests weekly, each requiring multiple steps, cross-referencing against finance-specific requirements, and coordination between departments. The average ticket routing request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their ticket routing output was inconsistent. Different team members followed different procedures, and there was no standardized way to handle edge cases that are common in finance. A recent audit revealed that 12% of completed ticket routing records contained errors that required rework — costing the organization an additional $50K annually in correction and remediation efforts. The leadership team recognized that continuing to throw people at the problem wasn't viable and began searching for an AI-powered solution.
The Solution
Arahi AI provided the automation backbone this finance team needed. They deployed a multi-agent workflow that breaks the ticket routing process into discrete, automated steps — each handled by a specialized AI agent. The first agent monitors triggers from QuickBooks and Stripe. The second agent analyzes and processes incoming requests using finance-specific business logic. The third agent executes actions across connected tools and notifies team members via Slack.
The beauty of the no-code approach was speed of implementation. The team had their first agent live within 90 minutes, and the full ticket routing workflow was operational within a single afternoon. They used Arahi AI's template for finance ticket routing as a starting point, customized the business rules to match their specific process, and connected their existing tool stack without writing a single line of code. Within the first week, the agents had processed over 200 ticket routing instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key finance ticket routing metrics.
Average Routing Time
99.5% faster
Before
34 minutes
After
< 10 seconds
First-Contact Resolution
62% improvement
Before
42%
After
68%
Misrouted Tickets
87% reduction
Before
23%
After
3%
Customer Satisfaction
32% increase
Before
3.4/5
After
4.5/5
Support Cost per Ticket
59% savings
Before
$22
After
$9
“The difference is night and day. Our finance clients used to wait days for ticket routing to be completed. Now it happens in minutes, and the quality is consistently higher than what we achieved manually. Customer satisfaction scores went through the roof.”
VP of Customer Success
Fintech startup
Key Takeaways
The most important lessons from this finance ticket routing automation project.
This finance team proved that ticket routing automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling ticket routing capacity by 10x without adding headcount fundamentally changed the economics of their finance operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling ticket routing differently.
Real-time visibility into ticket routing metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in Half a day — here's how they did it.
Step 1: Mapped the existing ticket routing workflow
Documented every step of the current manual ticket routing process, including decision points, exceptions, and handoffs between team members. Identified which steps could be fully automated versus those needing human oversight.
Step 2: Built the automation in Arahi AI
Used Arahi AI's no-code builder to create the ticket routing workflow: connected QuickBooks and Plaid as data sources, configured AI decision logic for finance-specific requirements, and set up automated actions and notifications.
Step 3: Parallel run with manual process
Ran the AI agents alongside the manual process for one week to compare outputs. The AI matched or exceeded human accuracy on 98% of ticket routing instances, with the 2% of edge cases automatically flagged for human review.
Setup Time
Half a day
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating ticket routing in finance.
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