Company Profile
Company Type
Specialty healthcare practice
Team Size
30-150 employees
Industry
Healthcare
Key Challenge
Struggling with inefficient manual chat support processes that were slowing growth and increasing operational costs. Their primary concern was HIPAA compliance.
Tools Connected
The Challenge
Manual chat support was the biggest bottleneck in this specialty healthcare practice's operations. Their team of 30-150 employees processed hundreds of chat support requests weekly, each requiring multiple steps, cross-referencing against healthcare-specific requirements, and coordination between departments. The average chat support request took 45 minutes to complete manually, and the backlog was growing by 15% each quarter.
Beyond the time drain, the quality of their chat support output was inconsistent. Different team members followed different procedures, and there was no standardized way to handle edge cases that are common in healthcare. A recent audit revealed that 12% of completed chat support 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 healthcare team needed. They deployed a multi-agent workflow that breaks the chat support process into discrete, automated steps — each handled by a specialized AI agent. The first agent monitors triggers from Epic and Kareo. The second agent analyzes and processes incoming requests using healthcare-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 chat support workflow was operational within a single afternoon. They used Arahi AI's template for healthcare chat support 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 chat support instances with 99%+ accuracy — more than the team typically handled in a month.
The Results
Measurable improvements across key healthcare chat support metrics.
Average Response Time
99% faster
Before
8 minutes
After
< 5 seconds
Queries Resolved by AI
New capability
Before
0%
After
72%
Customer Satisfaction
42% increase
Before
3.1/5
After
4.4/5
Support Cost per Interaction
86% savings
Before
$8.50
After
$1.20
After-Hours Coverage
Always on
Before
0% (business hours only)
After
100% 24/7
“What impressed me most was the setup speed. I expected a months-long implementation, but we had AI agents handling our healthcare chat support workflow within a single afternoon. The no-code approach meant our team could configure everything themselves without waiting on IT.”
Director of Business Operations
Specialty healthcare practice
Key Takeaways
The most important lessons from this healthcare chat support automation project.
Automating chat support in healthcare delivered immediate, measurable results: faster processing, higher accuracy, and lower costs.
The key to success was connecting existing healthcare 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 healthcare-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 chat support workflow
Documented every step of the current manual chat support 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 chat support workflow: connected Epic and Athenahealth as data sources, configured AI decision logic for healthcare-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 chat support 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 chat support in healthcare.
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