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Deploy with Azure PipelinesAutomate | Test | Iterate

Automating your deployments to both test and live environments allows you to iterate faster, do less work, reduces the risk of human error and improves your security.

Azure Pipelines makes it easy to deploy your code anywhere and is particularly well integrated with Azure. NewOrbit can help you get started with your automated deployments and give you a solid foundation to build on.

Deploy

We will help you configure build and deploy pipelines, which can deploy your code to test and/or production environments.

We will also help you integrate this to your source control system and set up approval gates so you can automate your deployments – or show you how to trigger deployments manually.

As you get more used to automated deployments, you will likely want to use more advanced patterns, such as deploying the same build artefacts to multiple environments with approval gates in between. This requires careful planning around how configuration is done, for example. We will discuss these advanced patterns with you, so that you have clarity on the next steps to take.

Tests

Azure Pipelines can run your unit tests for you. If you have a code review process, for example in GitHub, Azure Pipelines can automatically build and test all Pull Request and block them from being approved if the tests fail.

We will discuss the options with you, in the context of your current setup, and help you implement the right setup for you.

Interested in learning more?

Contact us to streamline your deployment process

Azure in Action

Discover how our Azure services have helped clients across industries tackle challenges and innovate faster:

Design, Code, AI: Behind the Scenes of Our Craft IT Logo Generator and Gallery

by Marcin Prystupa | 07/07/2025

A behind-the-scenes look at how we built two interactive apps for the Craft IT conference booth – and how I, a UX designer, ended up deep in React code with a little help from AI.

AI Isn’t Magic: Why Predictive Accuracy Can Be Misleading

by Frans Lytzen | 15/04/2025

One of the biggest misconceptions in AI today is how well it can actually predict things – especially things that are rare. This is most directly applicable to Machine Learning (as they are just statistical models) but the same principle applies to LLMs. The fundamental problem is the same and AI is not magic. In reality, AI’s predictive power is more complicated. One of the key challenges? False positives—incorrect detections that can significantly undermine the value of AI-driven decision-making. Let’s explore why this happens and how businesses can better understand AI’s limitations.

From Figma Slides to Svelte Page in Under an Hour – How I Accidentally Proved My Own Point

by Marcin Prystupa | 10/04/2025

A quick case study on how I went from a Figma presentation to a working Svelte page in less than an hour – with the help of AI and some clever tooling.

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