Foundations of AI Augmented Architecture with Kernaro Hub and Sparx EA
Your architecture lives in Sparx EA. Kernaro AI Hub is the layer over it — natural-language intelligence, and a way to put architecture answers in the hands of the stakeholders who never open the tool. This private training course teaches your team to make that real, on your own repository.
Format: private team training course · Prerequisites: Sparx EA + Kernaro Hub
We start by understanding your context
This isn't generic training. Before we begin, we meet with your architecture leader to understand the context your team works in — your tools, your processes, your repository structure, and the use cases you've prioritized. Then we tailor the course content to fit, so your architects learn AI Augmented Architecture on the way your practice actually works — not in the abstract.
Access isn't capability
Switching on Kernaro AI Hub doesn't make an organization good at AI augmented architecture, any more than buying Sparx EA makes someone an architect. The hub can answer questions about your models in plain language and hand insight to stakeholders who never open EA — but only if someone knows which questions matter, what a trustworthy answer looks like, and how to shape the repository so the answers hold up.
That's what this course builds. Not a feature tour — the craft of running the hub as a shared front door to your architecture, so the right people get clear, defensible answers without waiting on a custom report or a specialist's calendar.
What the course covers
The Hub layer
Working fluently with Kernaro AI Hub: exploring your models in natural language, asking architectural questions, and getting context-driven answers rather than digging through diagrams, documents, and repositories by hand.
Stakeholder access over your repository
The hub sits over your Sparx EA repository and brings architecture to the people who need it — analysts, BI teams, decision-makers. We teach you to publish crisp narratives and shareable answers so stakeholders self-serve instead of queuing for reports.
The four use cases
Modeling, analysis, governance, and stakeholder engagement — practiced on your real models. With the Hub, stakeholder engagement is the centre of gravity: this is where architecture finally reaches the business in language it can act on.
Module by module
Orientation & the hub over your repository
Connect Kernaro AI Hub to your Sparx EA repository and run your first natural-language exploration. Understand what the hub can answer, where its answers come from, and how to read a result critically rather than taking it at face value.
Asking good questions & tracing impact
Frame architectural questions the hub can answer well — what supports a capability, which applications a change touches, where dependencies and risks sit. Use conversational exploration and impact analysis to replace manual hunting, and learn where a confident answer still needs an architect's check.
Answers stakeholders can use
Turn model context into business-readable outputs — summaries, narratives, and reports your stakeholders can act on. Set up the hub as a shared front door so analysts, BI teams, and decision-makers get clarity without waiting on custom work.
Governing the hub & making it stick
Roll the hub out responsibly: who gets access, how answers stay trustworthy, and how repository quality underpins everything the hub says. Build the working habits that keep stakeholders engaged after the training ends.
Why this beats generic AI training
A generic AI course teaches you to chat with a model. This teaches you to run an architecture intelligence layer — on your repository, for your stakeholders, with the accountability that comes from answers grounded in your governed model rather than a best guess. You leave with the hub configured for real use and a team that can keep it valuable, not a demo that goes quiet a week later.
Kernaro AI Hub is a Sparx Systems product. Sparx Services teaches it; we are independent of Sparx Systems Pty Ltd.
Other Foundations
Bring the course to your team.
A conversation first — we'll confirm prerequisites and scope a course to your repository, your stakeholders, and your goals.
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