Company Profile
Company Type
Corporate law firm
Team Size
20-80 staff
Industry
Legal
Key Challenge
Struggling with inefficient manual customer retention processes that were slowing growth and increasing operational costs. Their primary concern was client intake efficiency.
Tools Connected
The Challenge
This corporate law firm had reached a breaking point with their manual customer retention process. With 20-80 staff managing daily legal operations, the team was spending an average of 25+ hours per week on repetitive customer retention 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 legal business, delayed customer retention 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 legal 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 legal customer retention workflow end-to-end. Implementation began with connecting their core tools — Clio, Google Drive, and Gmail — 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 customer retention process: receiving incoming requests or triggers, analyzing the context using legal-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 Slack for tracking and reporting, giving leadership real-time visibility into customer retention performance metrics for the first time.
The Results
Measurable improvements across key legal customer retention metrics.
Monthly Churn Rate
Major reduction
Before
Elevated
After
Significantly lower
At-Risk Detection Lead Time
Proactive vs. reactive
Before
After cancellation
After
Weeks before churn
Retention Intervention Success
Significant improvement
Before
Low
After
Much higher
Annual Revenue Saved
Meaningful impact
Before
No proactive program
After
Significant recovery
NPS Score
Major improvement
Before
Below target
After
Above target
“Before Arahi AI, our customer retention process was the bottleneck that every legal team complained about. Now it's our competitive advantage. We process faster, more accurately, and at a fraction of the cost. Our competitors are still doing this manually.”
Head of Strategy
Corporate law firm
Key Takeaways
The most important lessons from this legal customer retention automation project.
AI-powered customer retention automation dramatically reduced manual processing time for this legal 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 significantly, improving data quality and downstream decision-making.
The ROI was realized quickly, with the solution paying for itself through cost savings and productivity gains.
Implementation Timeline
From zero to production in 3 hours — here's how they did it.
Step 1: Connected legal tools to Arahi AI
Integrated Clio, LawPay, and DocuSign 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 legal-specific rules for customer retention: 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 legal data
Ran the AI agents on a week's worth of historical customer retention 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 Slack. Monitored the first 48 hours closely, confirming high accuracy before reducing oversight to weekly reviews.
Setup Time
3 hours
AI Agents
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating customer retention in legal.
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