Company Profile
Company Type
Commercial real estate company
Team Size
25-100 agents
Industry
Real Estate
Key Challenge
Struggling with inefficient manual chat support processes that were slowing growth and increasing operational costs. Their primary concern was agent productivity.
Tools Connected
The Challenge
Manual chat support was the biggest bottleneck in this commercial real estate company's operations. Their team of 25-100 agents processed hundreds of chat support requests weekly, each requiring multiple steps, cross-referencing against real estate-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 real estate. 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 real estate 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 MLS and HubSpot. The second agent analyzes and processes incoming requests using real estate-specific business logic. The third agent executes actions across connected tools and notifies team members via Gmail.
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 real estate 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 real estate 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
“Before Arahi AI, our chat support process was the bottleneck that every real estate 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
Commercial real estate company
Key Takeaways
The most important lessons from this real estate chat support automation project.
AI-powered chat support automation eliminated 88% of manual processing time for this real estate 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 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 MLS and Follow Up Boss as data sources, configured AI decision logic for real estate-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
2 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating chat support in real estate.
Related Case Studies
Explore more AI automation success stories.
More Real Estate Case Studies
Chat Support in Other Industries
This case study represents a typical customer scenario. Individual results may vary.