Company Profile
Company Type
Fintech startup
Team Size
50-250 employees
Industry
Finance
Key Challenge
Struggling with inefficient manual follow-up processes that were slowing growth and increasing operational costs. Their primary concern was data accuracy.
Tools Connected
The Challenge
Manual follow-up was the biggest bottleneck in this fintech startup's operations. Their team of 50-250 employees processed hundreds of follow-up requests weekly, each requiring multiple steps, cross-referencing against finance-specific requirements, and coordination between departments. The average follow-up request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their follow-up 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 follow-up 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 follow-up 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 follow-up workflow was operational within a single afternoon. They used Arahi AI's template for finance follow-up 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 follow-up instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key finance follow-up metrics.
Task Completion Time
97% faster
Before
2-3 hours average
After
< 5 minutes
Team Productivity
250% increase
Before
Baseline
After
3.5x output
Quality Score
26% improvement
Before
78% accuracy
After
98.5% accuracy
Monthly Cost
85% savings
Before
$8,200/month
After
$1,200/month
Customer Satisfaction
35% increase
Before
3.4/5
After
4.6/5
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our finance follow-up workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Director of Business Operations
Fintech startup
Key Takeaways
The most important lessons from this finance follow-up automation project.
AI-powered follow-up automation eliminated 88% of manual processing time for this finance team, freeing staff to focus on high-value strategic work.
Implementation took less than a day — the no-code approach meant no IT bottleneck or months-long development cycle.
Error rates dropped by over 90%, significantly improving data quality and downstream decision-making.
The ROI was realized within the first month, with the solution paying for itself multiple times over through cost savings and productivity gains.
Implementation Timeline
From zero to production in Half a day — here's how they did it.
Step 1: Mapped the existing follow-up workflow
Documented every step of the current manual follow-up 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 follow-up 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 follow-up instances, with the 2% of edge cases automatically flagged for human review.
Setup Time
Half a day
AI Agents
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating follow-up in finance.
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