Company Profile
Company Type
Financial services provider
Team Size
30-120 employees
Industry
Finance
Key Challenge
Struggling with inefficient manual appointment scheduling processes that were slowing growth and increasing operational costs. Their primary concern was regulatory compliance.
Tools Connected
The Challenge
This financial services provider had reached a breaking point with their manual appointment scheduling process. With 30-120 employees managing daily finance operations, the team was spending an average of 25+ hours per week on repetitive appointment scheduling tasks that added no strategic value. The workload was unsustainable, and errors were becoming more frequent as volume grew.
The consequences extended beyond wasted time. In their finance business, delayed appointment scheduling created a cascade of downstream problems — missed deadlines, frustrated stakeholders, and data quality issues that undermined decision-making. The team had tried hiring additional staff, but the cost was prohibitive and training new employees on their complex finance processes took months. They needed a solution that could handle their current volume and scale with their growth, without requiring a proportional increase in headcount.
The Solution
The team selected Arahi AI to automate their finance appointment scheduling workflow end-to-end. Implementation began with connecting their core tools — QuickBooks, Stripe, and Slack — to the Arahi AI platform. Using the no-code builder, they configured AI agents that replicate their best-performing team member's decision-making process, but at machine speed and consistency.
The AI agents handle every step of the appointment scheduling process: receiving incoming requests or triggers, analyzing the context using finance-specific rules, making intelligent routing decisions, executing the core actions, and notifying the right stakeholders. What previously required 45+ minutes of manual work per instance now completes automatically in under 2 minutes. The agents also learn from corrections, continuously improving their accuracy. The team connected Salesforce for tracking and reporting, giving leadership real-time visibility into appointment scheduling performance metrics for the first time.
The Results
Measurable improvements across key finance appointment scheduling metrics.
Processing Time
96% faster
Before
45+ minutes per task
After
< 2 minutes
Manual Hours per Week
88% reduction
Before
25+ hours
After
< 3 hours
Error Rate
92% fewer errors
Before
8-12% manual errors
After
< 1% with AI
Operational Cost
88% savings
Before
$6,500/month
After
$800/month
Team Capacity
10x scale
Before
Limited by headcount
After
10x throughput
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our finance appointment scheduling workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Director of Business Operations
Financial services provider
Key Takeaways
The most important lessons from this finance appointment scheduling automation project.
Automating appointment scheduling in finance delivered immediate, measurable results: faster processing, higher accuracy, and lower costs.
The key to success was connecting existing finance tools to AI agents rather than replacing the entire tech stack.
24/7 automated processing eliminated backlogs and ensured consistent service quality regardless of volume fluctuations.
Starting with a pre-built template and customizing for finance-specific requirements dramatically reduced time-to-value.
Implementation Timeline
From zero to production in 3 hours — here's how they did it.
Step 1: Connected finance tools to Arahi AI
Integrated QuickBooks, Xero, and Plaid with Arahi AI using pre-built connectors — no API keys or custom code required. The team verified data flow between systems in under 15 minutes.
Step 2: Configured AI agent business rules
Defined the finance-specific rules for appointment scheduling: scoring criteria, routing logic, escalation thresholds, and exception handling. The team used Arahi AI's visual rule builder to translate their existing process into automated workflows.
Step 3: Tested with live finance data
Ran the AI agents on a week's worth of historical appointment scheduling data to validate accuracy and identify edge cases. Made minor adjustments to scoring weights and routing rules based on the results.
Step 4: Launched and monitored
Deployed the AI agents to production with the entire team notified via Salesforce. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
3 hours
AI Agents
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating appointment scheduling in finance.
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This case study represents a typical customer scenario. Individual results may vary.