
Claims CRM
CLAIMS
CRM
Launched in 2025, Wave AI is an intelligent estimation platform that transforms the restoration industry’s traditional assessment workflow. While conventional estimation processes require days of manual calculations, multiple site visits, and extremely documentation review, Claims AI delivers professional-grade estimates in 10-15 minutes with 95% accuracy. Claims AI’s mobile-first approach allows estimators to complete their entire workflow directly on-site using only their smartphone, fundamentally changing how restoration companies deliver customer service.
Role
UX/UI Designer
Duration
May 2025 - August 2025
Tools & Skills
Figma, User Research, User Testing,
Wireframing, Prototyping
pROBLEM
SERVEPRO Chula Vista operates without a centralized CRM (Customer Relationship Management) system, resulting in significant operational inefficiencies that directly impact project delivery and customer satisfaction. Currently, client information is scattered across multiple disconnected platforms, from third-party estimation software to ServiceM8, an outdated system that remains severely underutilized despite its available features.
This estimation process requires a multi-day workflow involving multiple team members who must continuously cross-reference and relay information between platforms. This manual handoff process not only extends project cycle times but also introduces opportunities for miscommunication and data loss.
pROCESS
RESEARCH:
To understand the specific operational challenges facing damage restoration teams, I conducted user interviews across multiple roles at SERVPRO Chula Vista. From estimators and damage technicians to administrative staff, these interviews revealed insights into current information tracking processes, workflow inefficiencies, and the real-world impact of fragmented data management.
IDEATION:
To understand the specific operational challenges facing damage restoration teams, I conducted user interviews across multiple roles at SERVPRO Chula Vista. From estimators and damage technicians to administrative staff, these interviews revealed insights into current information tracking processes, workflow inefficiencies, and the real-world impact of fragmented data management.
Results
Due to timeline constraints, this concept represents design exploration and initial ideation rather than user-validated solutions.
The final CRM design consolidated all functionality from previously fragmented systems into a unified platform, while introducing automation features specifically designed to streamline internal workflows and reduce manual processes.
Key features include:
CYCLE TIME OPTIMIZATION:
Real-time monitoring system that helps employees meet project milestones and deliver faster turnaround times for clients
AI-POWERED PRODUCTIIVTY ENHACEMENT:
AI recommendations for optimizing client information management and process efficiency
Chatbot for instant CRM queries (e.g., "What tasks is Albert working on?" or "Show me this week's pending estimates")
INTEGRATED CLIENT RELATIONSHIP MANAGEMENT:
Centralized system for logging client touchpoints, including gift deliveries, in-person visits, and business card distributions
Automated reminders and suggestions for client engagement opportunities
SEAMLESS INTERGRATION:
Automatic import of detailed damage assessments and cost estimates, eliminating manual data transfer and reducing estimation cycle time directly from Claims AI
Reflection
I do my best work when I connect with the people I'm designing for
I felt a genuine connection with the SERVPRO team members when conducting user interviews and became genuinely interested in their daily challenges and how I could contribute to improving their workflows with my design skills.




I learnt to be a better UX/UI designer and saw real improvement
The project continuously challenged my design thinking as I worked to understand complex restoration processes and translate them into efficient, intuitive solutions. Learning to combine the separate workflows taught me to think holistically about user experience, considering how individual features contribute to broader operational efficiency.
Thanks for being curious (:
Mobile version is currently
under construction.
Feel free to check out my site on
desktop meanwhile!

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