Boost Engineering Productivity by 400% with the AI DevEx Platform.

Many teams miss out on the full potential of AI to accelerate and improve the software development lifecycle and that’s exactly where the AI DevEx Platform, powered by AWS, comes in.

AI in Software: Promises vs. Hurdles

AI comes with big promises, but unlocking its full potential requires tackling real challenges.

Everyone talks about AI writing code. Many have built flashy prototypes without ever touching a legacy stack. But integrating AI-generated code into a 10-year-old system and running it at scale? That’s a different story. AI can generate and validate code faster than humans, if, and only if, we can trust it’s generating the right code. Individual engineers experiment, but true enterprise-wide assisted coding is still rare.

A core challenge is trust, explainability, and validation. How can you rely on code you didn’t fully write? How do you know it works with all dependencies? And how do you justify decisions that weren’t really yours?

AI can improve code quality and enforce best practices, but only if those practices are clearly defined. Often, that means exposing internal logic or “engineering trade secrets” to external tools, raising data privacy and intellectual property questions.

Finally, while AI can elevate the developer experience, from natural-language queries to auto-documentation, teams still need training, support, and guidance. Tools must fit seamlessly into workflows, and leadership is required to drive the cultural shift that makes it all stick.

AI has enormous potential, but realizing it is as much about people, processes, and trust as it is about the technology itself.

Empowering Engineers with AI

We are convinced: organizations that remove or lower the hurdles for their engineers will gain a clear competitive advantage.

By centrally provisioning a developer experience (DevEx) platform, you can:

Build trust

Facilitate AI-assisted code generation with the right guardrails and context.

Create safe space

Give engineers legal and technical boundaries to safely expose code to AI assistants, preventing leaks to public environments.

Integrate seamlessly

Deliver tools at the right time and place within existing workflows, lowering barriers and encouraging adoption.

The organizations that get this right won’t just use AI, they’ll accelerate innovation, safely and confidently.

AI-Powered Developer Experience (DevEx) Platform

Our AI-powered DevEx platform is an integrated suite of tools and practices that enhances every stage of the software development lifecycle (SDLC), from coding and testing to deployment. It delivers intelligent, actionable insights to accelerate delivery and improve software quality.

The graphic shows the standard SDLC stages, but at every step AI can drive productivity and efficiency. Examples include:

Automate Automated testing and code reviews
Code generation & bug detection
Faster understanding of legacy systems
Auto-generated documentation
Reduction of manual errors and labor-intensive tasks

When implemented correctly, the platform not only speeds up development but also boosts quality and frees engineers to focus on higher-value work.

Zooming In: Automate Automated Testing

One area with huge potential for quick wins is automated testing. Our AI-DLC framework has three main components:

1. Ecosystem Context

One area with huge potential for quick wins is automated testing. Our AI-DLC framework has three main components:

2. Playbook

The Playbook encodes all the rules, standards, and best practices for your DevEx framework: naming conventions, test datasets, coding languages, and do’s and don’ts. These elements are transformed into intelligent prompts to guide the AI and ensure high-quality output, just like you would expect from your own engineers.

3. AI-DLC Interface

This is where everything comes together. The AI-DLC combines the ecosystem context and Playbook and exposes a natural language interface directly inside the engineer’s IDE. Engineers can interact with the AI from the console, review its output, and perform a “human in the loop” inspection before acceptance. The goal isn’t instant perfection, it’s maximizing productivity while keeping engineers in control.

Watch our Product Demo

Making AI Work: Lessons from the Demo

If you watched the video, you saw a brief demonstration of how a well-configured framework can deliver real benefits. While our Engineer makes it all look effortless, the quality of the output depends on a carefully crafted Playbook and a properly connected MCP ecosystem.

It’s crucial to remember: AI tools and LLMs cannot be held accountable for mistakes. Human inspection remains essential, the “human in the loop” ensures quality and reliability.

Key hurdles can be addressed with the right approach:

Explainability

Inject guardrails, best practices, and context to make AI outcomes predictable and trustworthy.

Data & privacy

Operate within your own ecosystem and landing zone to stay compliant with legal agreements and internal policies.

Adoption & culture

Drive adoption and behavioral change by centrally providing these capabilities, exactly where engineers need them in their workflow.

With the right framework, AI doesn’t replace engineers, it empowers them to work faster, safer, and smarter.