Reported impact
Business outcome
Spryt increased booking conversion, reduced no-shows and call-center load, and gave clinics a compliant way to turn scheduled care into attended care.
- Customer Experience
- Productivity Improvement
- Risk Reduction
Google CloudHealthcareCustomer Service AutomationAI and Machine LearningGenerative AI / LLMIndustry CloudCustomer ExperienceProductivity ImprovementRisk ReductionUndated Google CloudGC#4285f4HealthcareIrelandhealthcare providers were missing booked appointments because patients faced fear, confusion, and transport barriers, while Spryt also needed to replace a fragmented AI stack with a secure platform that could satisfy…Enables a 28% reduction in no-showsArchived · Oct 8, 20265Customer Service AutomationCloud SQL · Gemini · Gemini Enterprise Agent Platform0 2026-10-08 5
Spryt
Customer Service Automation
healthcare providers were missing booked appointments because patients faced fear, confusion, and transport barriers, while Spryt also needed to replace a fragmented AI stack with a secure platform that could satisfy major healthcare buyers.
Enables a 28% reduction in no-shows
Helps clinics save +8 admin hours per week
Increased booking conversion by 160%
Increased patient adherence and preparation for their appointment by 35%
Solution
Spryt uses Google Cloud to address this challenge: healthcare providers were missing booked appointments because patients faced fear, confusion, and transport barriers, while Spryt also needed to replace a fragmented AI stack with a secure platform that could satisfy major healthcare buyers. Gemini powers Cortex and Sentinel to reason over patient conversations, check responses for safety and compliance, and decide when a human should take over, serving as the compute layer. TensorFlow runs prediction models that identify patients at risk of missing or canceling appointments before outreach begins, serving as the compute layer. Google Kubernetes Engine runs the multi-agent Asa platform and scales patient engagement services for production healthcare deployments, serving as the orchestration layer. Cloud SQL supports Spryt’s MySQL database for the scheduling and engagement application, serving as the storage layer.
Data flow
Patient outreach begins in messaging channels such as WhatsApp and SMS, TensorFlow models score no-show risk, Gemini-powered Cortex governs the response and escalation path, GKE hosts the running agents, and Cloud SQL stores application data while Gemini Enterprise Agent Platform serves the underlying models.
Solution architecture
- Compute
- Gemini powers Cortex and Sentinel to reason over patient conversations, check responses for safety and compliance, and decide when a human should take over
- TensorFlow runs prediction models that identify patients at risk of missing or canceling appointments before outreach begins
- Storage
- Cloud SQL supports Spryt’s MySQL database for the scheduling and engagement application
- Serving
- Gemini Enterprise Agent Platform provides the shared environment for developing, evaluating, and serving the models behind Asa, Cortex, Sentinel, and the prediction systems
- Orchestration
- Google Kubernetes Engine runs the multi-agent Asa platform and scales patient engagement services for production healthcare deployments
Architecture clues
- Asa is a multi-agent patient engagement system with Cortex as the decision layer and Sentinel as the monitoring model
- Patient data is processed in-region and encrypted at rest and in transit by default
- Prediction models were developed and evaluated with more than 224 million de-identified Mayo Clinic Platform records
Evidence from the source
Enables a 28% reduction in no-shows
Gemini powers Cortex, the decision layer that governs everything Asa says.
Helps clinics save +8 admin hours per week
Increased booking conversion by 160%
Increased patient adherence and preparation for their appointment by 35%
Reduced call center load by 42%
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value100/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact25/255 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/206 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://cloud.google.com/customers/sprytGoogle CloudFirst-party
Spryt customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
Date evidence
- First archived Oct 8, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.Open date source
- Sitemap last modified Sep 1, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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