Case Study: UWM
FINANCIAL: MORTGAGE
UWM equips 12,000 + mortgage brokers with an AI-powered “Agentic Workforce” built on raia
Snapshot
Metric
Before raia
After raia (AI Agents)
Lead-response time
3 – 5 hours, business hours only
< 90 seconds, 24 / 7 (chat + SMS)
Broker pipeline coverage
≈ 40 % of new inquiries contacted
100 % of leads qualified by AI
Application-to-close cycle
32 calendar days
22 days (-31 %)
Equivalent human workload
1 LO assistant per 4 brokers
AI capacity equal to ≈ 10 FTEs
Borrower CSAT
4.0 / 5
4.6 / 5 (+0.6)
“With raia, every independent broker on our platform gets a virtual assistant that never sleeps—qualifying borrowers, gathering docs and keeping deals on track so loan officers can focus on closing.” — SVP Partner Experience, UWM
1 | Company & Challenge
United Wholesale Mortgage (UWM) is the largest wholesale mortgage lender in the U.S., originating $139 billion in 2024 and serving over 12,000 independent brokers nationwide. Explosive lead volume exposed three bottlenecks:
Lead overload – Too many borrower inquiries for loan officers (LOs) to vet in real time.
Document drag – Borrowers stalled on gathering pay-stubs, W-2s, bank links and disclosures.
Status questions – Brokers spent hours chasing underwriting updates rather than prospecting.
2 | Why UWM Chose raia
Omnichannel coverage – Chat, SMS, email and voice in one timeline.
Rapid, compliant training – 20,000 + pages of agency guidelines and transcripts ingested in hours.
Human-in-the-loop CoPilot – 60-day shadow allowed brokers to fine-tune answers to 95 % accuracy.
No-code LOS hooks – Direct connections to BOLT AUS, Encompass LOS and broker CRMs.
3 | Solution Design
AI Agent
Primary Role
Key Integrations
Sales-Qual Agent
Greets inbound leads, collects mini-1003, runs pricing, books call.
BOLT API, Calendly
LO Assistant Agent
Guides borrowers through doc collection, sends secure upload links, nightly chasers.
Encompass, email, SMS
Pipeline Status Agent
Answers “Where’s my loan?” for brokers and borrowers, surfaces conditions.
LOS webhook, chat / voice
4 | Implementation Timeline
Weeks 1–2: Upload guidelines, overlays, 5 years of tickets.
Weeks 3–8: Shadow mode with 50 pilot brokers.
Week 12: Sales-Qual Agent live nationwide.
Month 6: LO Assistant and Pipeline Status agents deployed; Spanish pack added.
5 | Results (First 6 Months)
≈ 85,000 borrower conversations handled; 62 % fully resolved by AI.
Remaining 38 % pre-qualified, trimming LO handle time 45 %.
Lead-to-pre-qual shrank from 5 h to < 30 min, boosting win-rate +18 pp.
Application-to-clear-to-close cut from 32 days to 22 days.
Avoided hiring 10 support staff, saving ≈ $900 k annually.
6 | Key Takeaways
Automate top-funnel pain first for quickest ROI.
Use Packs for guideline changes – new DU matrix? Drop the PDF, agent updates in an hour.
Maintain the human touch – borrowers can escalate to their LO at any point.
Bottom Line
By embedding raia, UWM turned AI into the ultimate LO assistant—qualifying prospects, collecting documents and answering status questions at lightning speed—while cutting cycle times and boosting CSAT.
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