Back office ops · Production

Microsoft ISE builds a reusable GenAI project template to eliminate per-project setup overhead for enterprise customers

The problem

An enterprise customer had to rebuild all infrastructure, pipelines, and configurations from scratch for every new GenAI project, and faced repeated approval board and security review cycles for architectures that were nearly identical across projects.

Workflow diagram · grounded in source
1
User initiates via template-starter
trigger
“the template-starter receives the new project name and configuration, then it automagically replaces those in the template code and creates a new repository with the new project code that just works out-of-the-box”
2
Azure infrastructure deployment
integration
“The user leverage the template-starter to deploy the infrastructure into Azure. This initial step sets up the necessary Azure infrastructure, including key vaults, service principles, and other configurations.”
3
Project repository creation
output
“The template-starter also creates a new project repository by cloning the template repository. The template-starter makes initial, project-specific, code augmentations as well as setting up the configurations needed for it to work with t…”
4
Document processing pipeline
ai_action
“we chunked PDF files and created search index for them”
5
PR validation pipeline
validation
“PR Validation Pipeline: This pipeline checks the validity of the code before merging it into the main branch by running tests.”
Reported outcome

A reusable template and automated starter project lets the customer launch new GenAI projects without repeating setup work, reducing errors, improving quality, and minimising cross-team dependencies.

Reported metrics
Project setup timesaved time
Error riskreduced the risk of errors
Cross-team setup dependenciesminimised the need for different teams to be involved in the setup process
Reported stack
promptflowAzure Machine LearningGitHub APIGitHub CLITerraformDockerAzure SQL
Source
https://devblogs.microsoft.com/ise/scaling-genai-projects/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

A reusable template and automated starter project lets the customer launch new GenAI projects without repeating setup work, reducing errors, improving quality, and minimising cross-team dependencies.

What tools did this team use?

promptflow, Azure Machine Learning, GitHub API, GitHub CLI, Terraform, Docker, Azure SQL.

What results were reported?

Project setup time: saved time; Error risk: reduced the risk of errors; Cross-team setup dependencies: minimised the need for different teams to be involved in the setup process (source-reported, not independently verified).

How is this back office ops AI workflow structured?

User initiates via template-starter → Azure infrastructure deployment → Project repository creation → Document processing pipeline → PR validation pipeline.

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