Company Profile
Company Type
Insurtech company
Team Size
20-100 employees
Industry
Insurance
Key Challenge
Struggling with inefficient manual follow-up processes that were slowing growth and increasing operational costs. Their primary concern was claims processing speed.
Tools Connected
The Challenge
Manual follow-up was the biggest bottleneck in this insurtech company's operations. Their team of 20-100 employees processed hundreds of follow-up requests weekly, each requiring multiple steps, cross-referencing against insurance-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 insurance. 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 insurance 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 Applied Epic and Gmail. The second agent analyzes and processes incoming requests using insurance-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 insurance 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 insurance 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
“The ROI was almost immediate. Within the first month, our follow-up throughput increased by over 300% while our error rate dropped to near zero. For a insurance business of our size, that translates directly to the bottom line. Arahi AI paid for itself in the first week.”
Operations Director
Insurtech company
Key Takeaways
The most important lessons from this insurance follow-up automation project.
Automating follow-up in insurance delivered immediate, measurable results: faster processing, higher accuracy, and lower costs.
The key to success was connecting existing insurance 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 insurance-specific requirements dramatically reduced time-to-value.
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 Applied Epic and DocuSign as data sources, configured AI decision logic for insurance-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
3 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating follow-up in insurance.
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