Guest Vision is a facial-recognition check-in platform that South Reno Athletic Club and ASCENDING built together on AWS to modernize entry at a high-traffic fitness facility. South Reno Athletic Club partnered with ASCENDING to replace manual and easily misused check-ins with Guest Vision, a facial-recognition web application built on AWS. The solution delivered faster member entry, stronger membership-use controls, and a practical AI path for a small-to-medium business with limited internal AI engineering capacity.
BackgroundA High-Traffic Fitness Club Needing Secure, Low-Friction Check-Ins
South Reno Athletic Club (SouthRAC) is Northern Nevada's largest fitness club, supporting more than 1,500 daily active users across a 91,000-square-foot facility. As traffic grew, the club needed a more consistent and secure way to validate member access while minimizing front-desk friction.
SouthRAC's leadership identified facial recognition as a way to modernize operations without adding staff burden. The vision for Guest Vision was a browser-based check-in experience that would be easy for both members and administrators to use and maintain.
The ChallengeLimited AI Expertise and Budget for a High-Traffic Front Desk
The challenge is a set of SMB-scale constraints that limited how quickly SouthRAC could put facial recognition into production. SouthRAC needed to adopt AI quickly, but faced practical constraints common to SMB organizations.
- Limited in-house AI/ML expertise and constrained development bandwidth.
- Budget limits that made custom model development and long tuning cycles unrealistic.
- A need for lightweight architecture and straightforward operations, without heavy ongoing overhead.
- Security concerns around unauthorized membership usage and identity validation at entry.
An AWS Advanced Consulting Partner for Applied AI Delivery
ASCENDING is the AWS-focused delivery partner SouthRAC brought in to turn its facial-recognition concept into a production system. SouthRAC selected ASCENDING for its applied AI delivery experience and AWS specialization. As an AWS Advanced Consulting Partner with implementation depth across machine learning and cloud architecture, ASCENDING provided a practical, cost-aware path from concept to production.
The team focused on a managed-services-first approach to reduce risk, compress delivery time, and avoid overengineering. This helped SouthRAC launch a production-ready solution while preserving flexibility for future enhancements.
The SolutionA Browser-Based Guest Vision App Built on AWS Rekognition
The solution is Guest Vision, a browser-based check-in application assembled from managed AWS services. ASCENDING designed Guest Vision around AWS Rekognition as the primary facial-recognition service, integrated with cloud-native components for reliability and maintainability.
- Built a web-based check-in workflow with frontend and backend services deployed on Amazon EKS.
- Implemented facial recognition with AWS Rekognition, including Multi-Vector capabilities to improve matching reliability.
- Applied configurable similarity thresholds so facial data links to user profiles only when confidence requirements are met.
- Stored structured user and check-in data in Amazon RDS for PostgreSQL for operational reporting and administration.
- Enabled image-processing flow through AWS-managed components to keep the platform lightweight and cost-efficient for ongoing operation.
Faster, More Secure Check-Ins with Lower AI Development Overhead
The outcome is a faster, more secure check-in experience that replaced manual verification at SouthRAC's front desk. Guest Vision transformed SouthRAC's member entry flow from a manual checkpoint into a streamlined, AI-assisted process. Staff gained better control over access events, while members experienced faster and more consistent check-ins.
- Reduced barriers to AI adoption by replacing custom model buildout with managed AWS recognition services.
- Lowered development and maintenance effort, with potential overhead reduction of up to 90% compared with building and tuning from scratch.
- Improved flexibility by enabling threshold-based tuning for factors such as pose, brightness, and occlusion to raise image quality and match accuracy.
- Strengthened protection against unauthorized membership use while preserving a user-friendly front-desk experience.
Built with AWS Computer Vision and Cloud-Native Platform Services
Frequently Asked Questions
How long does it take to deploy a facial-recognition check-in system like Guest Vision?
Timeline is typically a phased rollout that starts with a pilot at one location, validates match accuracy and threshold tuning, then extends to full front-desk operations once the workflow is proven in production.
How is member biometric data protected in a system built on AWS Rekognition?
Security is enforced through AWS-managed infrastructure controls, encrypted data storage, and configurable similarity thresholds, so facial data is only linked to a member profile when confidence requirements are met. See the Amazon Rekognition service overview for details on how AWS secures and operates the underlying recognition service.
Can a facial-recognition check-in platform like Guest Vision scale to multiple gym locations?
Scale is supported by the same managed AWS services used at SouthRAC's single facility — Amazon EKS, Amazon RDS for PostgreSQL, and AWS Rekognition all scale independently, so additional locations can be onboarded without re-architecting the platform.
