Mechanical Rock earns AWS AI Services Competency

Mechanical Rock has achieved the AWS AI Services Competency, recognising our experience building AI solutions on AWS.

The competency validates our ability to take AI beyond proof of concept and into production, with the engineering practices needed to make these systems secure, reliable and maintainable.

That work builds on more than a decade of software engineering at Mechanical Rock. Since 2014, we've focused on solving hard problems with software, and while AI introduces new engineering challenges, it doesn't remove the need for sensible engineering.

Our approach to generative AI builds on the practices we've developed over years of delivering production software, and this competency recognises that experience.

What the competency recognises

The AWS AI Services Competency recognises AWS Partners with demonstrated expertise in helping customers apply generative AI to real business problems.

Earning the competency requires partners to meet AWS's eligibility criteria and undergo technical validation of their capabilities and customer outcomes. The assessment looks beyond demonstrations and prototypes to solutions built on foundation models and delivered with appropriate security, reliability and operational controls.

For organisations evaluating AI partners, this provides an additional point of confidence when deciding who to work with. It helps identify teams with validated experience delivering generative AI solutions, not just experimenting with the technology.

Building AI on solid data foundations

AI introduces new capabilities, but it doesn't change the foundations of strong software engineering. Software running in production needs the appropriate access controls, robust testing, operational monitoring and clear governance. Without these foundations, a promising prototype can be difficult to trust, maintain or operate reliably.

These practices underpin our work across Enterprise DevOps, Data Platforms and Product Development. We apply the same engineering discipline to AI as we do to any other software, thinking beyond what a model can do to how the complete solution will behave in practice.

We've put this into practice with Happi, an AI wellbeing companion developed with Happiness Co. Happi is an example of applying AI to a real-world service, with a focus on delivering accessible, personalised support around the clock.

As Doug Hull, Mechanical Rock Consulting Practice Lead, puts it:

“Ideas are cheap. AI makes them easier to prototype, but execution is what sets you apart. Building systems that people can rely on requires a combination of best-practice engineering and a sensible approach to the use of AI. This competency recognises the work our team has put into both.”

What this means for our customers

The competency provides independent validation of our AI capabilities on AWS. More importantly, it reflects how we approach delivery: start with the problem, establish the right foundations, and build something that can operate reliably beyond the initial release.

Depending on the use case, that can mean:

• Identifying practical applications: Helping you find opportunities in areas where AI can address a specific business need and make a proper impact.
• Connecting AI to trusted data: Designing the data pipelines, retrieval mechanisms and access controls needed to give AI systems relevant information without compromising on your governance.
• Engineering for production: Addressing security, privacy, evaluation, monitoring and operational requirements throughout development, rather than treating them as a final step.
• Building capability within your team: We work alongside your people and share knowledge, so you can operate, maintain and evolve the solution independently.

The right solution isn't necessarily the one with the most sophisticated model. It's the one that solves the problem, fits the operating environment and delivers value over time.

As Richard Bowden, CTO at Mechanical Rock, shares:

“Our customers are looking for practical outcomes, not AI for its own sake. That might mean reducing manual work, making information easier to access or improving how decisions get made. Our job is to work out where the technology fits, engineer a solution that is fit for purpose, and make sure their team can run it at the end."

Thank you, and what's next

A milestone like this is never the work of one person. A huge thank you to our software engineers, operations and delivery team, to our customers who trusted us with their ideas, and to our friends at AWS for their ongoing support.

We're continuing to develop our AI capabilities and help organisations move from experimentation to production, with the data foundations and software engineering practices to support the next step.

If you're exploring a generative AI use case or working through the foundations needed to make one viable, let's have a chat.

You can also find Mechanical Rock in the AWS Partner Solutions Finder.