This is not another webinar about AI hype or code generation demos.
This webinar focuses on a real enterprise problem: how to make AI-assisted software development predictable, maintainable, and safe for production systems.
What slows down AI adoption in enterprise development?
AI writes code faster, but faster code does not always mean better software.
Rules and prompts alone are not enough to keep development consistent.
Generic AI tools often do not understand the real business context of enterprise systems.
Many engineering teams start building with AI agents without guardrails and end up creating an expensive IT mess that becomes difficult to maintain and scale.
In this webinar, we will break down the five main blockers to enterprise AI development and explore how Java teams can use AI in a more controlled way.
Who this webinar is for
This webinar is for technology and product leaders building enterprise software and evaluating how AI can be used in real development processes.
CTOs and Heads of Engineering
Product and Engineering Managers
Enterprise Architects
Java teams building enterprise business systems
After this webinar, you will better understand how to:
Reduce the “garbage in → garbage out” effect in AI-assisted development
Build enterprise software with AI without losing control over the result
Get real productivity gains from AI development tools
Integrate AI-assisted development into real production workflows
Most importantly: How to make AI a practical engineering tool, not a source of chaos.
*“Garbage in, garbage out” is a common phrase in software development and AI. It means that poor or unstructured input usually leads to poor-quality results.
AI and Jmix are already changing enterprise development
Join the webinar and learn how to reduce
and build AI-assisted software with more confidence and control.
Why Jmix?
Jmix helps teams reduce dependence on prompt quality and generic AI output by giving AI development more structure and control, without limiting experienced developers.
Structured application architecture
Code is generated within a consistent application model instead of being created in an ad hoc way.
Structured project context
Application structure, data models, and development patterns are clearly defined and available to AI tools.
Jmix Studio
Validation and development controls are built directly into the workflow.
Jmix AI Assistant
The AI assistant understands the platform structure, architecture, and project artifacts.
What matters in Jmix
Works with LLMs and AI agents in secure enterprise environments
Built on Java and Spring Boot
Includes built-in security and access control
Based on an enterprise architecture model refined over 10+ years
Integrated into the OpenIDE ecosystem
No hidden limitations - just a clear and structured architecture model.
Jmix defines the project architecture and provides the structured context AI tools need to generate more consistent and maintainable software
Agenda
Where AI-assisted development stands today.
Why vibe coding spreads fast and where it starts to break.
Why enterprise and public-sector reality make AI development harder.
What controlled AI-assisted development looks like in practice.
What helps Jmix support scalable enterprise AI development.
The future of AI-assisted enterprise development with Jmix.
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