What challenges did FlowX.AI face before AI implementation?

Modernizing complex legacy enterprise platforms with faster, cost-effective R&D cycles. Enabling AI-powered natural-language building, documentation, recommendations, and support while experimenting with large language models in controlled environments.

What was the impact of the AI solution?

Creates fully provisioned development environments in about five minutes. Rolls out hot fixes in under two hours. Saved 50% of cloud bills through Google Kubernetes Engine autoscaling. Scaled from around 100 workloads to 700 workloads across R&D environments.

Source

GCP

FlowX.AI case study: AI-assisted application modernization using Vertex AI and Kubernetes Engine

Use case typeCode modernizationUpdated Jun 13, 2026

FlowX. AI builds an AI-powered application modernization platform for global financial institutions. The company uses Google Cloud to scale R&D, prototype AI-assisted features, and create secure single-tenant environments for developing and testing customer functionality.

Organization: FlowX.AI
Industry: Finance
Location: Romania
Published: May 2026

Reported outcomes

−50%

costCost savings

Strategic outcomes

Speed & agilityCreated fully provisioned development environmentsSpeed & agilityRolled out hot fixes fasterCost efficiencyReduced cloud operating costsScale & capacityScaled R&D workloads significantly

Catalog median for cost savings deployments: −45% across 345 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Application modernization
2AI-assisted development
3Machine learning prototyping

Modernizing complex legacy enterprise platforms with faster, cost-effective R&D cycles.
Enabling AI-powered natural-language building, documentation, recommendations, and support while experimenting with large language models in controlled environments.

FlowX prototyped on Google Kubernetes Engine to create isolated, quickly provisioned development environments.
The company used Vertex AI to test and fine-tune machine learning algorithms before going live.
It also layered AI over open-source LLMs in secure single-tenant environments to support natural-language interaction and feature optimization.
Google Workspace was used for email and account management.

Technologies

Vertex AI Google Kubernetes Engine Google Workspace

Creates fully provisioned development environments in about five minutes.
Rolls out hot fixes in under two hours.
Saved 50% of cloud bills through Google Kubernetes Engine autoscaling.
Scaled from around 100 workloads to 700 workloads across R&D environments.

Architecture

FlowX runs isolated development environments on Google Kubernetes Engine and uses autoscaling to adjust usage. The company uses Vertex AI to test and fine-tune machine learning algorithms before production, and exposes AI-assisted functionality in secure single-tenant environments built around open-source LLMs.

Sources & evidence1

Groundedness: 5/5

Primary source

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Customer evidence