Company Profile
Company Type
Fintech startup
Team Size
20-100 employees
Industry
Finance
Key Challenge
Struggling with inefficient manual report generation processes that were slowing growth and increasing operational costs. Their primary concern was audit readiness.
Tools Connected
The Challenge
Manual report generation was the biggest bottleneck in this fintech startup's operations. Their team of 20-100 employees processed hundreds of report generation requests weekly, each requiring multiple steps, cross-referencing against finance-specific requirements, and coordination between departments. The average report generation request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their report generation 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 report generation 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 report generation 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 report generation workflow was operational within a single afternoon. They used Arahi AI's template for finance report generation 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 report generation instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key finance report generation 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 report generation 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 report generation automation project.
This finance team proved that report generation automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling report generation 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 report generation differently.
Real-time visibility into report generation 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 report generation workflow
Documented every step of the current manual report generation 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 report generation 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 report generation 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 report generation in finance.
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