ACA Blog

The IT chain that makes your assets profitable: why market integration is crucial for energy producers

tom.claus@acagroup.be (Tom Claus) — Tue, 23 Jun 2026 09:52:04 GMT

Wind turbines, solar parks and batteries get most of the attention when it comes to the energy transition. Understandable, because that's where the investment sits. But for energy producers and asset managers, the real challenge isn't in the hardware itself. It's in the layer in between: market integration.

Meet AWS Quick: the AI agent that actually does the work

Stefan Faes — Wed, 17 Jun 2026 09:29:06 GMT

AWS Quick is Amazon's answer to a problem nearly every business user knows: AI tools that can answer questions, but can't take action. AWS Quick is different. It's an agentic AI platform built to automate multi-step workflows across your entire tech stack, without writing a single line of code.

DXPs: Digital experience at the center - ACA Group

ACA Group Team — Tue, 28 Apr 2026 09:23:42 GMT

In today's digital world, it's crucial to offer a smooth and personalized experience to your customers, no matter which channel they use to communicate with you. For this, Digital Experience Platforms (DXPs) are the solution. DXPs act as a central hub for creating, managing, and optimizing digital touchpoints. They are the game-changers you need to provide a consistent and engaging experience to your audience.

What is a Digital Experience Platform (DXP)?

A Digital Experience Platform (DXP) is a comprehensive software platform that combines the power of various tools into an all-encompassing digital ecosystem. Typically, this include:

Content Management System (CMS): Easily manage and publish content across multiple channels, such as websites, mobile apps, and social media.
Digital Commerce: Build and manage online stores with smooth payment processes and advanced product data management.
Marketing Automation: Automate marketing campaigns, personalize customer interactions, and optimize conversions.
Data Analytics: Collect, analyze, and interpret customer data for data-driven decisions.
Artificial Intelligence (AI): Personalize content and offers, automate tasks, and optimize customer experiences with AI-driven insights.

The challenges of a fragmented digital landscape

Today, companies face a complex digital maze with diverse platforms, channels, and technologies. This can lead to:

Inconsistent customer experiences: Different touchpoints can offer unequal user experiences, causing confusion and frustration among customers.
Lack of centralization: Managing content, data, and processes across various systems is inefficient and error-prone.
Limited data insights: Fragmented data hinders a holistic view of customer behavior and market trends.
Scalability issues: Expanding digital initiatives is challenging with silos in technology and processes.

DXP: The key to a centralized digital strategy

A Digital Experience Platform (DXP) provides a centralized solution to address the mentioned challenges:

Consistent customer experience: Create uniform and personalized experiences across all digital touchpoints, both desktop and mobile.
Centralized management: Manage content, data, and processes from one cohesive platform, for optimal efficiency and consistency.
360-degree data insights: Collect and analyze data from all channels for deep insights into customer behavior and market trends.
Scalable infrastructure: Effortlessly scale your digital initiatives to meet growing demand and changing needs.

DXP in practice: proven successes

Leading organizations around the world already rely on DXPs to achieve their digital goals and impress their customers. From leading retailers offering seamless omnichannel experiences to innovative media companies engaging their audiences with personalized content, Digital Experience Platforms deliver tangible results in customer satisfaction, revenue growth, and brand loyalty.

ACA Group has also helped various clients make their digital processes more efficient and user-friendly with DXPs. Here are some inspiring cases:

Case 1: Innovative and modular intranet for Nelissen Steenfabrieken

Nelissen Steenfabrieken, a leading manufacturer of facade bricks, needed an efficient internal communication tool. ACA Group developed IntraBLOX, a flexible and mobile-accessible intranet. This platform improves communication and automates processes, resulting in a more user-friendly and efficient company.

➡️ Read the full case study

Case 2: Flemish Government simplifies housing management with digital Woningpas

The Woningpas, a project of the Flemish government, bundles all information about a house into one digital platform. This simplifies administration for construction, renovation, rental, and sale. ACA Group used Liferay DXP and Angular to build a modular, expandable, and secure system that integrates data from various sources. The agile approach with Scrum and Kanban ensures flexibility and transparency, resulting in an efficient and user-friendly platform for both citizens and government employees.

➡️ Read the full case study

Case 3: Dynamic web experiences for better service at UZ Brussels

UZ Brussels, a leading university hospital, sought improved service and consistent user experience. ACA Group developed a central content hub and new portal site 'Insite' using Liferay DXP. This solution offers centralized and personalized content, improved authorization, a knowledge base, and a mobile-first experience. The result is higher employee engagement, better training, faster communication, and more engaged visitors and patients.

➡️ Read the full case study

Will you choose DXP?

DXPs are powerful tools that enable organizations to achieve a centralized, data-driven, and scalable digital strategy. By implementing a DXP, companies can create seamless and personalized customer experiences, increase efficiency, make data-driven decisions, and gain a competitive edge in the digital age.

Do you want to know more about the possibilities of Digital Experience Platforms? Or do you have a specific challenge for which a DXP is the ideal solution?

Let’s talk!

Architecting for AI: how to prevent runaway code

Stijn Huygh — Wed, 25 Mar 2026 00:00:00 GMT

We need to talk about the "illusion of speed” in the age of AI.

In times where every post on LinkedIn seems to show that all your competitors are making a shift from traditional coding to 100% AI agentic software development, reality is somewhat more complex. Although AI demos and PoCs can give impressive first results, embedding AI Agentic software development in a long-term application lifecycle, with production-grade requirements, is still a different ballgame.

At ACA, we start from defining and measuring our quality standards and objectives, structures, controls and guardrails. Then, as we add AI agentic capabilities we can verify and control outcomes in a factual and continuously monitored way. If our AI setup helps us to speed up within those boundaries it’s great. But if AI hits our guardrails, we immediately detect it and we pull back.

As a result, today AI agentic software development gives us efficiency gains of 20 to 50% compared to traditional coding. All while maintaining our quality standards in terms of performance, security and maintainability.

In this article by Stijn Huygh, we take a deeper dive in how we realize this.

Why fast-looking AI output can quietly undermine software quality

Every developer has experienced the initial rush of working with a coding LLM. You ask for a feature, and seconds later, you have code. It compiles, it runs, and it looks mostly correct.

But speed can be deceptive.

The biggest risk of AI-assisted development is not that the code will fail. It is that it will work while quietly degrading your architecture. Without guardrails, AI does not follow your engineering culture. It follows the statistical average of the internet. That is rarely what you want in a production-grade system.

If you do not control the system, you do not control the output.

Therefore, it is important to engineer a software development lifecycle that focuses on maintaining the integrity of your code base. You want the end result to be performant, secure and definitely maintainable.

The real problem: AI-induced technical debt

AI does not understand your architecture, it reads code, it predicts patterns and outputs what is statistically the most likely answer.

This becomes a problem when your system relies on intentional structure. The model does not know your architectural decisions or the reasoning behind them. It just tries its best to generate working code within the instructions it has.

The result is code that technically works, but slowly drifts away from the architecture you designed.

That drift often shows up in subtle ways:

unused methods
hard-coded strings
Importing code across logical boundaries
DTOs leaking into the UI
logic that compiles but violates your rules

None of these issues immediately break the application. The code runs and the feature works, but over time, these small deviations accumulate. Technical debt introduced by AI can grow faster than human code review can realistically catch. And that is where the real problem begins.

So we changed the approach. Instead of chatting with AI, we started engineering the constraints around it.

The framework for high-integrity AI engineering

1. Enforce architecture physically, not just in documentation

The very first step to improve the quality of the code base, even without using AI to generate code, is determining how you want your project to be structured. The goal is to enforce consistency within your project, but also over all your other projects. When a developer opens the codebase they should immediately be able to know where everything is, based on your conventions. Determine your application architecture!

The most common mistake teams make is relying on PDF documentation or a wiki page to enforce architecture. AI (and humans) will forget there is a standard and just ignore it. It will become a lot easier to maintain a high quality codebase when your architecture is also enforced in code and not only in documentation.

One of the ways we enforce this, is by separating our layered architecture in different projects/packages. If code is not exposed to another layer, or the layer you are in does not have access to a specific package it will not be used by that layer.

Think of folders like painted lines on a road; they are suggestions that are easy to drive over by accident. Separate Projects (or Packages) are concrete walls. By structuring your layers as distinct packages with pubspec.yaml rules, you physically prevent the AI from importing code from the wrong layer.

If the AI tries to import the API layer directly into the UI, the compiler (not a human reviewer) blocks it immediately.

Our layered architecture looks like this:

_p4_ui: Purely presentational. No business logic allowed, MVVM pattern.
_p3_application: Orchestrates use cases. Never throws exceptions. Returns Response objects.
_p2_data: Handles DTOs. Rule: DTOs never leave this layer; they are mapped immediately to the domain. DTO’s are therefore never leaked in other layers.
_p1_domain: Pure business logic.
_p0_*: Shared foundations such as DI, translations, utilities and design system.

If something violates the architecture, the compiler flags it. This removes ambiguity and reduces discussions during code review.

2. Define strict coding standards and enforce them

Your application architecture should also be enriched with coding standards to further improve quality and consistency. Some examples of code standards we enforce:

Naming

Interfaces start with I
Services end with Service
DTOs end with Dto
ENums start with E

Complexity limits

Max 3 nested if statements
Max 75 lines per file
Max 500 lines per file

Completeness

All switch cases handled
No use of dynamic

Application layer rule

No exceptions. Only Response objects.

Ordering

Properties, fields, constructor, public and private functions are always in specific order in your class.

Most of these rules are enforced through linters and static analysis. This is not about strictness for the sake of it. But it is about predictability. When structure is predictable, deviations stand out immediately.

3. Use deterministic scaffolding

One lesson we learned quickly: never let an LLM generate your boilerplate. It gets "creative" with file names and folder structures, leading to a messy project tree.

We use "dumb tools" for structure so the "smart tools" (AI) can focus on logic. Tools like Mason allow you to generate deterministic templates using the mustache template system. A command like “mason make viewmodel” creates the perfect folder structure, filenames, and class definitions in mere seconds. The view, viewmodel, interfaces and services are all created with exactly our project structure and coding guidelines in mind.

The strategy: when you integrate AI, do not ask it to "write the file." Instead, give the AI access to your CLI via the Model Context Protocol (MCP) or directly via terminal. The AI becomes the operator of your scaffolding tools, executing the mason command to ensure 100% consistency before it writes a single line of logic.

4. Treat warnings as real issues

AI-generated code often compiles, but it may still introduce warnings such as unused variables, implicit casts or minor performance issues. On their own, these seem harmless. Over time, however, if those warnings are ignored, they accumulate and gradually reduce the overall quality and maintainability of the codebase.

You should treat AI code with the same suspicion as human code. We apply a zero-warnings policy in our CI/CD pipeline. If warnings are present, the build fails.

On top of that:

Static analyses must pass
Naming rules are validated
Unit tests must pass before merging

Tests are especially important. AI is very good at refactoring. It is less good at understanding business impact.

Automated checks help us keep confidence high.

5. Structure documentation for AI: the 3-tier documentation system

"Generic training data produces generic code." If you want the AI to write code that looks like your team wrote it, you must feed it your context.

We use a 3-tier documentation system to feed the context window efficiently.

Tier 1: golden rules (always on)

A lightweight file with non-negotiables. For example:

The Application layer never throws exceptions.
DTOs never leave the Data layer.
New views are generated through the approved scaffolding tools.

Tier 2: Architecture deep dive

Reference documents that explain:

Layer responsibilities
Testing patterns
Design system usage
Coding guidelines

Tier 3: Workflows and agent instructions

Step-by-step instructions for how AI agents should operate.

Clear documentation reduces ambiguity. And reduced ambiguity leads to more consistent output.

6. The Agentic Workflow: split the brain

The biggest leap in quality comes from moving away from a single chat window to specialized AI Agents. This separation of concerns prevents hallucinations by limiting the scope of each agent.

Instead of one "Coder," we deploy a team:

1. The architect (the thinker)

Constraint: NO CODING.
Role: Reads the user story, explores the codebase, and writes a plan.md. This is a step-by-step execution plan (e.g., "1. Create DTO, 2. Map to Domain").
Benefit: “Human in the loop“ - You review the plan before the code is written, preventing over-engineering.

2. The coder (the do’er)

Constraint: Works one step at a time.
Role: Reads plan.md, implements Step 1, runs tests, updates the plan, and STOPS for approval. It cannot skip ahead.

3. The reviewer (the gatekeeper)

Constraint: Max 15 comments, prioritized by severity.
Role: Specialized in spotting tier-violation and copy-paste errors that humans often miss.

From writing code to designing systems

High-integrity AI engineering isn't about writing better prompts, but about building better systems.

When AI repeatedly makes the same mistake, the solution is not a better prompt. It is a better system.

Strengthen architectural boundaries
Add or refine linter rules
Improve scaffolding
Clarify documentation

Over time, this creates an environment where AI becomes a reliable accelerator instead of a source of noise.

Key takeaways

AI accelerates development. It can also accelerate technical debt.
Architecture should be enforced in code, not only described in documentation.
Deterministic scaffolding improves consistency.
Automated quality checks protect long-term maintainability.
Clear documentation and structured workflows improve AI output.

AI is powerful. But it performs best inside well-defined systems.

Structure does not slow you down. It allows you to move fast with confidence.

The AI train: where does the real AI impact lie for your company today?

alexander.frimout@acagroup.be (Alexander Frimout) — Mon, 23 Mar 2026 00:00:00 GMT

Has your company already settled into the seats of first class, or have you not yet found your place on the AI train?
Our AI expert Alexander Frimout explains which processes are ideally suited for your first AI business case.
And why some companies aren’t sitting in the best wagons…

The possibilities are enormous. The use cases that prove this are expanding at a rapid pace. Many companies therefore feel the pressure to do something with AI.

But what exactly is that something? Where do you start as a company with your first AI business case? How do you identify the tools that truly add value to your business? Or put differently: in which wagon of the train below should you take a seat?

For many companies, that means the doors of the first wagon right behind the locomotive: chatbots.

Why there?

Probably because the ChatGPTs and Geminis of this world are the AI technologies we feel most familiar with. Of all the tools, platforms, and apps, chatbots are the most widely known to the general public. That makes the threshold to use them in a professional context just a little bit lower.

Chatbots: the AI business case without ROI

Yet this very wagon is the least interesting for companies, according to Alexander.

“Chatbots improve your personal efficiency. But their impact at company level? Limited. I’ve never spoken with a company that could present a concrete return on investment.”

“And yet, this is exactly what happens in many companies: we roll out 100 licenses of Microsoft Copilot and call it a day. What those colleagues actually do with it remains vague. As a company, you can tick the AI box, but in my view, it doesn’t deliver real value. It’s not a полноценный business case with a clearly defined ROI.”

Technology: the last four wagons

According to Alexander, the real gains lie at an intersection. The intersection between the last four wagons and back-office applications.

First, a quick explanation of those wagons.

AI Products: ready-made AI integrations within platforms and tools such as your Customer Relationship Management (CRM), Enterprise Resource Planning (ERP), or Warehouse Management System (WMS).
Workflows: automation of tasks (for example with n8n) for those who are less technically inclined. Think of automatic lead follow-ups or invoice reminders.
Custom Solutions: tailor-made software that seamlessly fits your unique business processes. Here, the sky’s the limit.
AI Development: tools such as Claude or Lovable that help your developers write code faster and with fewer errors.

Technology: check. Now let’s look at the business processes that are most relevant for your very first AI business case.

Processes: integrated back-office applications

The most interesting AI business cases are concentrated in back-office applications that are well integrated with existing software.

Think of LLMs that help your customer service teams respond faster. Or custom software that scans delivery notes and automatically converts them into a standard template.

Why are these processes the most interesting?

They happen on a computer and are therefore digital.
There is interaction with other software systems. Think of a mailbox, a CRM system, or warehouse management. There is already a certain structure in the data. And that data already lives within your company’s ecosystem.
Language is still the best input for language models.
They are repeatable processes. Such as entering delivery notes, answering simple emails, or creating invoices.
The added value of human expertise is often limited for many of these tasks. Precisely because they are the digital equivalent of manual assembly line work. Why should your colleague answer 49 similar customer emails if that means they have less time for the one email where their expertise truly matters?

AI business case in practice: Decomecc

Let’s make that intersection between AI technology and back-office processes more concrete.

Meet Decomecc. This company from Genk processes aluminum and (stainless) steel: cutting to size, sawing, applying coatings…

At their site, trucks constantly come and go, delivering coils like the one in the image above. Decomecc processes around 20 to 30 deliveries per day.

The situation before…
Administrative staff had to check every delivery and delivery note:

What exactly is being delivered?
Is the delivery correct?
Which project/customer does this coil belong to?
And so on and so on.

This meant: reading the delivery note, searching in the database, manually retyping the information…

In the best case, this takes about 15 minutes. But not every driver immediately has the correct paperwork. And not every colleague finds the right data just as quickly. On top of that, manual work always comes with a margin of error.

The situation now
The employee scans the delivery note. It is automatically sent to the custom AI tool we built for them. The tool then searches for the correct information and matches it with the internal database.

Within a minute, the employee receives a proposal:

This is, in my view, the project this delivery note belongs to. With a confidence score out of 100%.
These are the details I will complete after your approval.

As a result, the employee now has time to focus on the delivery notes the AI tool cannot immediately identify. For example, because the delivery note is incorrect, the driver delivered on the wrong day, or the order itself is wrong.

The end result?

Lower error rate
Faster processing
Internal projects can start sooner
Better stock management
And fewer frustrated drivers at reception

🎥 Watch the full video to see how we helped Decomecc automate their administrative paperwork.

Ready for departure: your AI checklist

Do you already have an idea which back-office processes would be ideal for your first AI business case? The more boxes you can tick on the checklist below, the more relevant it becomes.

What is the cost of the problem today?
Are there quality risks (impact of human/AI errors)?
Is there “reference data” to compare with, or does the system need to create data (e.g. orders)?
What is the role of the human reviewer?
Are the systems the AI needs to interact with accessible?
How standardized is the work / how many exceptions are there?
Are there other constraints? (security, processing speed, …)

On the rails…

All boxes checked? Then we’ll help you in the next step to determine the ROI and translate your case into a technical solution.

More than code: building, growing and creating impact together. Welcome to our mobile team!

jurgen.geys@acagroup.be (Jurgen Geys) — Fri, 13 Mar 2026 08:01:38 GMT

A great mobile app isn’t just about flashy features or sleek design. It’s about a team that knows how to write maintainable code, make smart decisions, and -most importantly- always puts the user first. That’s exactly what our mobile team does every single day.

We sat down with Jurgen Geys (Mobile Team Lead) to talk about how the team works, their technical choices, and why ACA is the perfect place for developers who want to make an impact.

Energy Mission 2025: the energy transition is accelerating. Can your data keep up?

tom.claus@acagroup.be (Tom Claus) — Fri, 13 Mar 2026 07:54:31 GMT

Our Business expert in Energy & Utilities Tom Claus headed to Energy Mission, and came back with some new insights… and a wake-up call for companies and (government) agencies.

From intern to core team member: Jelle's story 📱

jelle.schrijvers@acagroup.be (Jelle Schrijvers) — Fri, 13 Mar 2026 00:00:00 GMT

Imagine starting as an intern, building your first Flutter app, learning from experienced colleagues... and gradually growing into a developer with increasing responsibility. That is exactly the path Jelle has taken at ACA. We sat down with him to talk about his growth, key learning moments, and what it is like to work as a Mobile Developer at ACA.

How Model Context Protocol can automate the integration for AI applications (and save you hours of coding)

kamen.zhekov@acagroup.be (Kamen Zhekov) — Wed, 18 Feb 2026 00:00:00 GMT

For every external tool you want to connect to your AI system, you have to build a custom integration. This comes with two major downsides. One: it takes up a lot of your time. Two: you can not scale this way of working. Luckily, the Model Context Protocol (MCP) can solve this. In this article, we explain how MCP works, what happens on the server side, and talk about security.

ACA is ISO 27001 certified: what this means for secure, compliant software delivery

Simon Vercruysse — Fri, 19 Dec 2025 00:00:00 GMT

ACA Group is officially ISO 27001 compliant. For our customers, this certification is more than a formal milestone: it is clear, independent proof that information security is embedded in how we design, build and deliver software. Information Security Manager Simon Vercruysse explains what ISO 27001 entails and what the benefits are for your (future) projects.