Back office ops · Production

FlowX.AI uses Google Kubernetes Engine and Vertex AI to deliver AI-powered application modernization at scale

The problem

Large enterprises run critical software on aging legacy systems that consume up to 80% of IT budgets just to maintain, leaving organizations with fragmented, costly-to-navigate systems and a poor omnichannel experience for end users.

Workflow diagram · grounded in source
1
Platform layered on existing infrastructure
integration
“FlowX provides enterprises with a platform on top of their existing infrastructure to quickly build, run, and maintain new digital systems”
2
AI generates user interfaces
ai_action
“The platform generates state-of-the-art user interfaces with AI, meaning enterprises don't need coding or design skills to create their solutions, and are able to use natural language commands to easily build and optimize their customer …”
3
LLM optimization for natural language interaction
ai_action
“adding optimization layers on top of open-source large language models in secure, single tenant environments, which enables FlowX to create natural language functionality for its clients”
4
Vertex AI model training and fine-tuning
ai_action
“FlowX also uses Vertex AI to customize some of its large language models, as well as to train models for specific platform functionality. For example, FlowX recently developed machine learning algorithms to provide customers with real-ti…”
5
Automated client documentation and support
output
“Clients are also able to gain documentation and recommendations, as well as receive automated support to optimize and test their features”
Reported outcome

Using Google Kubernetes Engine autoscaling, FlowX saves over 50% of cloud infrastructure costs, provisions new development environments in five minutes, and deploys hot fixes in under two hours, while scaling from 100 to 700 R&D workloads.

Reported metrics
Cloud infrastructure cost savingsover 50%
Development environment provisioning timefive minutes
Hot fix deployment timeunder two hours
enterprise IT budget on legacy systems (industry estimate)up to 80%
Show all 5 reported metrics
cloud infrastructure cost savingsover 50%
development environment provisioning timefive minutes
hot fix deployment timeunder two hours
enterprise IT budget on legacy systems (industry estimate)up to 80%
R&D workloads running700
Reported stack
Google Kubernetes EngineVertex AIGoogle Workspacelarge language models
Source
https://cloud.google.com/customers/flowxai
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Using Google Kubernetes Engine autoscaling, FlowX saves over 50% of cloud infrastructure costs, provisions new development environments in five minutes, and deploys hot fixes in under two hours, while scaling from 100…

What tools did this team use?

Google Kubernetes Engine, Vertex AI, Google Workspace, large language models.

What results were reported?

Cloud infrastructure cost savings: over 50%; Development environment provisioning time: five minutes; Hot fix deployment time: under two hours; enterprise IT budget on legacy systems (industry estimate): up to 80% (source-reported, not independently verified).

How is this back office ops AI workflow structured?

Platform layered on existing infrastructure → AI generates user interfaces → LLM optimization for natural language interaction → Vertex AI model training and fine-tuning → Automated client documentation and support.

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