What challenges did Pienomial face before AI implementation?

Needed reliable, low-cost infrastructure to support always-on processing of dense scientific and regulatory documents. Deployments were manual and could take half a day, with limited logging and alerting that slowed issue detection and resolution. Previous infrastructure created higher complexity and less predictable costs.

What was the impact of the AI solution?

Cloud infrastructure costs were reduced by 40%. Deployment time dropped from half a day to 15 minutes. Zero downtime was achieved with Cloud Run autoscaling and managed services. Engineering time shifted away from operations toward AI agent development and innovation.

Source

GCP

Pienomial case study - Google Cloud

Use case typeCloud migrationUpdated Jun 13, 2026

Pienomial's Knolens platform delivers always-on AI for life sciences at lower prices while developers focus on products, not ops. Knolens combines proprietary evidence-structuring technology with generative AI to help organizations work with dense scientific and regulatory information so that high-stakes decisions can move forward with confidence. Pienomial migrated Knolens fully to Google Cloud to improve reliability, scalability, and cost predictability for time-sensitive scientific work.

Organization: Pienomial
Industry: Pharma
Location: United States
Published: May 2026

Reported outcomes

−40%

costCost savings

15 minutestime

Strategic outcomes

Cost efficiencyReduced cloud infrastructure costsSpeed & agilityAccelerated deployment rolloutsCustomer experience & trustEnabled zero-downtime serviceInnovation & cultureShifted engineering focus to innovation

Catalog median for cost savings deployments: −44.5% across 344 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

1Cloud migration
2Infrastructure modernization
3MLOps

Needed reliable, low-cost infrastructure to support always-on processing of dense scientific and regulatory documents.
Deployments were manual and could take half a day, with limited logging and alerting that slowed issue detection and resolution.
Previous infrastructure created higher complexity and less predictable costs.

Migrated Knolens fully to Google Cloud.
Built a managed, cloud-native architecture using Cloud Run and Compute Engine, with secure networking through Virtual Private Cloud, Identity and Access Management, and Certificate Manager.
Automated CI/CD with Cloud Build for zero-downtime rollouts and centralized observability with Cloud Monitoring and Cloud Logging.
Worked with Google Cloud partner Enhub to redesign secure networking, codify infrastructure, and establish repeatable patterns for growth.

Technologies

Cloud Run Compute Engine Virtual Private Cloud Identity and Access Management Certificate Manager Cloud Build Cloud Monitoring Cloud Logging Google Kubernetes Engine

Cloud infrastructure costs were reduced by 40%.
Deployment time dropped from half a day to 15 minutes.
Zero downtime was achieved with Cloud Run autoscaling and managed services.
Engineering time shifted away from operations toward AI agent development and innovation.

Architecture

Knolens runs on a managed Google Cloud architecture using Cloud Run and Compute Engine behind VPC boundaries, with IAM-based access control, Certificate Manager, Cloud Build-driven deployments, and Cloud Monitoring/Cloud Logging for operations. Enhub helped redesign the environment and codify infrastructure.

Implementation partners1

Enhub

Sources & evidence1

Groundedness: 4/5Type: Customer StoryPublished: May 27, 2026Publisher: Google CloudEvidence: PrimaryConfidence: High

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