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Google Cloud Healthcare Singapore

Lillia

Customer Service Automation

Lillia needed to reduce the friction of manual meal, medication, and vital tracking for chronic disease patients while freeing human care coaches from routine tasks and still meeting enterprise reliability and data residency requirements.

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Automates 95% of early patient care interactions using Gemini APIs
Autonomous AI agents now handle 95% of routine interactions during a patient’s first two weeks
Lillia continues to scale its digital healthcare solutions into the United States, Southeast Asia, and beyond

Reported impact

Business outcome

Lillia now automates 95% of routine patient interactions during the first two weeks of care, reduces operational work for coaches, and can embed its platform into enterprise healthcare clients while meeting regional data requirements.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

Lillia rebuilt its digital care model around agentic AI on Google Cloud so patient onboarding, routine question answering, and health logging could be automated safely during the first weeks of care. Gemini APIs supply the intelligence layer for these autonomous workflows, while Google Kubernetes Engine Autopilot runs the containerized microservices that execute them with lower operational overhead. Cloud Build and Artifact Registry support deployment consistency, and Cloud Monitoring plus Cloud Logging provide the operational visibility needed as the platform scales. Around those core services, Lillia uses Cloud SQL, Google Cloud Storage, Identity and Access Management, Secret Manager, and Cloud Load Balancing to support application data, security, secrets, and traffic handling. Regional Google Cloud infrastructure also helps the company satisfy Middle East data residency requirements as it embeds the platform into enterprise clients and expands globally.

Data flow

Patient questions and health-tracking interactions enter Lillia's care platform, where Gemini APIs power autonomous onboarding and logging workflows running on GKE Autopilot microservices, with application data held in managed stores and monitoring, logging, and deployment tooling keeping the environment reliable and auditable.

Solution architecture

12 components · 5 layers
  1. Compute
    • Gemini APIs Drive the autonomous patient-interaction workflows for onboarding, queries, and health logging.
    • Gemini platform Provides the underlying AI platform Lillia uses to build its agentic care model.
    • Google Kubernetes Engine (GKE) Autopilot Runs Lillia's containerized microservices with reduced operational management.
  2. Storage
    • Cloud SQL Stores structured application data for the care platform.
    • Google Cloud Storage Provides supporting storage for the digital health platform.
  3. Serving
    • Cloud Load Balancing Distributes traffic across the production application services.
  4. Orchestration
    • Cloud Build Supports consistent deployment pipelines for agentic care services.
    • Artifact Registry Stores deployment artifacts for Lillia's microservices.
  5. Governance
    • Identity and Access Management (IAM) Applies access controls across the healthcare platform.
    • Secret Manager Protects secrets used by the platform's services.
    • Cloud Monitoring Provides reliability visibility for the new AI capabilities.
    • Cloud Logging Captures runtime telemetry for ongoing operational oversight.

Architecture clues

  • Cloud Build and Artifact Registry support deployment consistency.
  • Cloud Monitoring and Cloud Logging are explicitly part of the operating model.
  • Google Cloud regional infrastructure is used to satisfy Middle East data residency requirements.
  • The platform uses containerized microservices on GKE Autopilot.

Evidence from the source

Automates 95% of early patient care interactions using Gemini APIs
Autonomous AI agents now handle 95% of routine interactions during a patient’s first two weeks
Cloud Monitoring Cloud Logging providing ongoing visibility
Google Cloud regional infrastructure satisfied Middle East data residency requirements
Lillia runs containerized microservices on Google Kubernetes Engine (GKE) Autopilot
Transparency Evidence & methodology 100/100 reference value · 79% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Google Cloud

    Lillia customer story

    First-party
    https://cloud.google.com/customers/lilliacare
    Source type
    Vendor-published 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

  • Sitemap last modified Oct 9, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Oct 9, 2026 · Google Cloud · Unknown Legacy extracted date without source-page confirmation.
    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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