Company Profile
Company Type
Growing real estate brokerage
Team Size
10-50 agents
Industry
Real Estate
Key Challenge
Struggling with inefficient manual data entry processes that were slowing growth and increasing operational costs. Their primary concern was deal pipeline velocity.
Tools Connected
The Challenge
Data entry was consuming an enormous amount of this growing real estate brokerage's time and budget. With 10-50 agents on staff, the real estate organization was processing hundreds of documents, forms, and records daily — all manually. Two full-time data entry clerks spent their entire days keying information from various sources into their systems, and the team still couldn't keep up with the volume.
The error rate was the real problem. Manual data entry across their real estate operations produced a 4.7% error rate — meaning roughly 1 in every 20 records contained mistakes. These errors cascaded through downstream processes, causing billing discrepancies, reporting inaccuracies, and customer-facing issues that damaged trust. The team spent an additional 15 hours per week just catching and correcting data entry mistakes. Meanwhile, critical real estate records sat in processing queues for 3-5 business days, creating delays that rippled across the entire organization.
The Solution
The organization implemented Arahi AI to automate the entire real estate data entry pipeline. They connected their document sources (HubSpot, MLS, and file uploads) to Arahi AI's no-code platform and configured AI agents to handle extraction, validation, and system entry automatically.
The AI agents use OCR and natural language processing to read any incoming real estate document — regardless of format — and extract structured data with 99.5%+ accuracy. Each extracted record passes through validation rules built specifically for their real estate business: checking for completeness, format accuracy, logical consistency, and compliance with real estate data standards. Valid records are automatically entered into MLS, while exceptions are flagged and routed to a human reviewer via Gmail with specific error details and suggested corrections. The team went from processing 3-5 day backlogs to same-day data availability.
The Results
Measurable improvements across key real estate data entry metrics.
Processing Time per Record
98% faster
Before
8-12 minutes
After
< 15 seconds
Error Rate
94% reduction
Before
4.7%
After
0.3%
Data Availability Lag
80% faster
Before
3-5 business days
After
Same day
Annual Labor Cost
95% savings
Before
$85K+ in staffing
After
$4K in AI processing
Processing Capacity
13x throughput
Before
150 records/day
After
2,000+ records/day
“Before Arahi AI, our data entry 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
Growing real estate brokerage
Key Takeaways
The most important lessons from this real estate data entry automation project.
This real estate team proved that data entry automation doesn't require technical expertise — the no-code platform made it accessible to business users.
Scaling data entry capacity by 10x without adding headcount fundamentally changed the economics of their real estate operations.
Consistent AI-powered processing eliminated the quality variance that came with different team members handling data entry differently.
Real-time visibility into data entry metrics gave leadership the data they needed to make better strategic decisions.
Implementation Timeline
From zero to production in 2 hours — here's how they did it.
Step 1: Connected real estate tools to Arahi AI
Integrated MLS, Zillow, and Follow Up Boss 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 real estate-specific rules for data entry: 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 real estate data
Ran the AI agents on a week's worth of historical data entry 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 DocuSign. Monitored the first 48 hours closely, confirming 99%+ accuracy before reducing oversight to weekly reviews.
Setup Time
2 hours
AI Agents
4 AI agents
Tools Connected
5 integrations
Frequently Asked Questions
Common questions about automating data entry in real estate.
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