What challenges did CellPoint Digital face before AI implementation?

Rebuild a legacy payment orchestration platform to handle complex multi-currency and multi-bank payment integrations at scale. Reduce engineering effort and time-to-market for a small team. Support operational ingestion, analytics, governance, and secure self-service data access.

Source

GCPScaled productionEvidence: Medium65/100

Cellpoint Digital scales payment orchestration data platform with BigQuery, Pub/Sub, and GKE

Use case typeCompliance automationUpdated Jun 13, 2026

CellPoint Digital is rebuilding its payment orchestration infrastructure on Google Cloud to improve scalability and become more data-driven. The company supports travel payments across multiple currencies, banks, and payment methods, and uses the new platform to streamline data access, reporting, and decision-making. A three-tier microservices architecture on Google Kubernetes Engine ingests operational events through Pub/Sub into analytical services, with raw data written to BigQuery and then transformed with Dataflow and SQL/dbt for reporting and analytics.

Organization: CellPoint Digital
Industry: Finance
Location: Denmark
Published: May 2026

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Quantified impact: 20%

Google Cloud Customer StoryMay 23, 2026Customer storyInferred claimMedium evidence strength

The fall-through success feature delivers an average 20% lift in conversions.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: CellPoint Digital
Provider: GCP
Maturity: Scaled Production
Linked source: Google Cloud Customer Story

Rebuild a legacy payment orchestration platform to handle complex multi-currency and multi-bank payment integrations at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Payments
2Analytics
3Workflow Automation

Google Kubernetes Engine runs the platform's microservices architecture.
Pub/Sub streams real-time operational data into analytical services.
BigQuery stores raw data directly for durability and analytics.
Dataflow and SQL/dbt transform raw data into structured reporting datasets.
Looker enables non-technical users to explore data securely through dashboards and self-service analytics.
Cloud Build, Cloud Run, Cloud Scheduler, and Cloud Storage are included in the broader platform stack.

Technologies

BigQuery Pub/Sub Dataflow Looker Google Kubernetes Engine Cloud Run Cloud Scheduler Cloud Storage Cloud Build Cloud Deploy

Architecture

A three-tier microservices platform runs on Google Kubernetes Engine. Operational data is streamed via Pub/Sub into analytical services. Raw data is written to BigQuery and backed up to object storage, then transformed using Dataflow and SQL/dbt into structures for dashboards and reporting. Looker serves downstream self-service analytics, while GCP platform services such as Cloud Build and Cloud Run support engineering workflows.

Sources & evidence1

Evidence: Medium65/100Evidence strength