Case Study: US Eye
HEALTH CARE
US Eye slashes caller wait-time with an AI “Agentic Workforce” powered by raia
Snapshot
Metric
Before raia
After raia (AI Call-Assist Agent)
Average time to find an answer during a call
4 min 30 sec on-hold while staff searched manuals
< 55 sec (-80 %) — answer surfaced instantly in the agent console
First-call resolution rate
62 %
89 %
Caller abandonment
11 %
4 %
Locations, doctor rosters & insurance plans indexed
Manual lists for 60+ clinics
Dynamic vector store refreshed nightly
Equivalent human workload offset
—
Capacity equal to ≈ 5 FTEs
“With raia, every support rep has a virtual expert at their fingertips. Calls are shorter, answers are accurate, and patients feel the difference.” – VP Patient Services, US Eye
1 | Company & Challenge
US Eye is a physician-led network of ophthalmology and optometry practices with 60 + locations across Florida, the Carolinas, and Virginia. Rapid growth left the support centre juggling:
Varying clinic hours, specialties, and doctor schedules
Differing pre-op and post-op procedures by location
Dozens of accepted insurance plans that change each quarter
Agents spent minutes rifling through PDFs or transferring callers—driving long hold times and caller frustration.
2 | Why US Eye chose raia
Bulk document ingestion — 8 000 + policy PDFs, doctor bios, insurance matrices, and EMR FAQs were drag-and-dropped into Launch Pad; auto-vectorised for semantic search.
Real-time console integration — the AI sits in a side panel of US Eye’s Five9 softphone, surfacing answers as agents type or speak keywords.
Nightly sync pipeline — any update in the corporate SharePoint refreshes the knowledge base before the next business day.
CoPilot feedback loop — during a 30-day shadow phase, supervisors thumbed up/down AI suggestions, pushing precision above 96 % before going live.
3 | Solution Design
AI Agent
Primary Role for Support Reps
Key Integrations
Call-Assist Agent
Listen (text stream) to caller’s question, search vector KB, display top answer & citation instantly; log interaction.
Five9 CTI, SharePoint doc feed
Insurance-Lookup Micro-agent
Autocomplete payer name, verify plan acceptance by clinic, display copay notes.
EMR API, nightly payer feed
Procedure-Prep Micro-agent
Retrieve location-specific pre-op & post-op instructions, text/email to patient on demand.
Twilio SMS, SendGrid email
4 | Implementation Timeline
Weeks 1–2 – Collect & convert policies, schedules, insurance lists to clean Markdown; bulk upload to raia Launch Pad.
Weeks 3–4 – Shadow mode in CoPilot across two call-pods; gather feedback, tune prompts.
Week 5 – Go-live for all agents; nightly SharePoint sync turned on.
Month 3 – SMS / email integration added for instant patient hand-offs.
5 | Early Outcomes (first 90 days)
Call answer-search time cut 80 % (4 m 30 s → < 55 s).
First-call resolution up 27 percentage points (62 % → 89 %).
Caller abandonment nearly halved (11 % → 4 %).
AI capacity equals ≈ 5 full-time agents, allowing US Eye to absorb seasonal peaks without overtime.
6 | Next Steps
Expand the knowledge base to include LASIK financing FAQs and clinical trial enrollment.
Deploy Spanish language pack for bilingual service lines.
Add analytics dashboard to surface most-asked questions and drive policy updates.
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