AI and Low-Code: Are We Ready for a Visual Co-Creation Lab for Applications?

Today, Artificial Intelligence presents itself as an ally—a design companion capable of suggesting solutions and automatically generating models and logic. While many are attempting to use it in traditional development, it becomes challenging in that context to detect logical errors or flaws often hidden within thousands of lines of code.

This is exactly where low-code technologies like Mendix put the human at the center: within a visual, transparent, and easily modifiable environment, designers can build applications while leveraging AI features to examine, understand, refine, and continuously improve domain models, application flows, and functionalities. These naturally emerge and evolve through visual modeling. In this way, low-code proves to be the technology best suited to enabling true symbiosis between humans and AI—facilitating a co-creation process that combines automation, control, and human understanding.

Low-code provides pre-built, tested components that help build high-quality software while reducing errors. The result is a system that is immediately visible, inspectable, and easy to understand and validate.

In this context, having AI integrated into the IDE allows those designing and building applications to interact—throughout the Software Development Life Cycle (SDLC)—with advanced Large Language Models (LLMs), trained on a knowledge base managed by the technology provider, and potentially expandable to include the customer’s local LLMs. This knowledge includes domain models, entities, attributes, associations, and best development practices.

Everything happens naturally through a visual and conversational interface that allows the AI to be queried in parallel with creation. Users can ask for suggestions or clarifications, make changes, optimize, or automate when needed.

A Real-World Example: MAIA 

At WEGG, we are analysts who support clients in their digital transformation journey, translating their needs into application flows and requirements that are then implemented using low-code technologies—with all the benefits of speed, standardization, and development automation.

Whether it’s single applications or multiple solutions for diverse needs—managed by us or in collaboration with the client’s internal development team—we have seen how AI-assisted development within low-code platforms significantly improves development time and quality.

Specifically, we work with Mendix, a low-code technology recognized as a leader by Gartner, which has integrated an AI assistant called MAIA (Mendix AI Assistance) with three core support goals:

• Guide: Helps developers and analysts by enabling them to ask questions and receive immediate, detailed responses via chatbots that provide summaries, flow insights, and suggestions on how to best build applications.

• Assist: Offers real-time recommendations, ensures adherence to best practices, and automates repetitive tasks that slow down traditional development, like bulk updates.

• Generate: Leverages generative AI to accelerate development by creating both front-end and back-end logic, generating application components, and automating tasks such as documentation writing, SQL command generation, and test data creation.

Supporting the Entire Development Lifecycle 

As mentioned, AI-assisted development in Mendix is seamlessly embedded within a unified, visual, and conversational interface. It spans the entire software lifecycle—from ideation to implementation—including deployment, testing, governance, monitoring, and feedback. Here are some examples.

Ideation Phase 

In the ideation phase, AI can assist in creating a prototype by generating a first version of the application from a requirements document (in PDF format). This document is uploaded into the conversational interface, where specific, contextual prompts can also be added to refine and guide the result more precisely.