Summary
Reinforcement Learning (RL) is a powerful way to build models that learning by doing. Instead of just fitting historical data, RL optimizes decisions through rewards and Feedback Loops—from real production and simulations. The result: models that continue to improve while the world changes. Think of applications ranging from AlphaGo-level decision-making to revenue and profit optimization, inventory and pricing strategies, and even stock signaling (with the right governance).
Reinforcement Learning (RL) is a learning approach where a Agent takes actions in an environment to maximize a reward The model learns policies that choose the best action based on the current state.
Agent: the model that makes decisions.
Environment: the world in which the model operates (marketplace, webshop, supply chain, stock market).
Reward: a number indicating how good an action was (e.g., higher margin, lower inventory costs).
Policy: strategy that chooses an action given a state.
Acronyms explained:
RL = Reinforcement Learning
MDP = Markov Decision Process (mathematical framework for RL)
MLOps = Machine Learning Operations (operational side: data, models, deployment, monitoring)
Continuous Learning: RL adapts policy when demand, prices, or behavior change.
Decision-focused: Not just predicting, but Truly optimize of the outcome.
Simulation-friendly: You can safely run “what-if” scenarios before going live.
Feedback First: Use real KPIs (margin, conversion, inventory turnover rate) as direct rewards.
Important: AlphaFold is a deep-learning breakthrough for protein folding; it is not Prime RL example AlphaGo/AlphaZero (decision-making with rewards). The point remains: learning via feedback delivers superior policies in dynamic environments.
Goal: maximum gross margin with stable conversion.
State: time, inventory, competitor price, traffic, history.
Action: choose price step or promotion type.
Reward: margin – (promotion costs + return risk).
Bonus: RL prevents “overfitting” to historical price elasticity because it explores.
Goal: service level ↑, inventory costs ↓.
Action: adjust reorder points and order quantities.
Reward: revenue – inventory and backorder costs.
Goal: maximize ROAS/CLV (Ad Spend ROI / Customer Value).
Action: budget allocation across channels & creatives.
Reward: attributed margin in the short and long term.
Goal: risk-weighted maximizing returns.
State: price features, volatility, calendar/macro events, news/sentiment features.
Action: position adjustment (increase/decrease/neutralize) or “no trade”.
Reward: P&L (Profit and Loss) – transaction costs – risk penalty.
Note: no investment advice; ensure strict risk limits, slippage models and Compliance.
This is how we ensure Continuous Learning at NetCare:
Analyze
Data audit, KPI definition, reward design, offline validation.
Training
Policy optimization (e.g., PPO/DDDQN). Determine hyperparameters and constraints.
Simulate
Digital twin or market simulator for What-If and A/B scenarios.
Operate
Controlled rollout (canary/gradual). Feature store + real-time inference.
Evaluate
Live KPIs, drift detection, fairness/guardrails, risk measurement.
Retrain
Periodic or event-driven retraining with fresh data and outcome feedback.
Classic supervised models predict an outcome (e.g., revenue or demand). But the best prediction does not automatically lead to the best action. RL directly optimizes the decision space —using the actual KPI as a reward—and learns from the consequences.
In short:
Supervised: “What is the probability that X will happen?”
RL: “Which action maximizes my goal now and in the long term?”
Design the reward well
Combine short-term KPIs (daily margin) with long-term value (CLV, inventory health).
Add penalties for risk, compliance, and customer impact.
Limit exploration risk
Start in simulation; go live with Canary Releases and caps (e.g., max price step/day).
Build in Guardrails: stop-losses, budget limits, approval flows.
Prevent data drift & leakage
Use a Feature Store with version control.
Monitor Drift (statistics change) and automatically retrain.
Manage MLOps & governance
CI/CD for models, reproducible pipelines, Explainability and audit trails.
Align with DORA/IT governance and privacy frameworks.
Choose a KPI-focused, well-defined use case (e.g., dynamic pricing or budget allocation).
Build a simple simulator with the key dynamics and constraints.
Start with a safe policy (rule-based) as a baseline; then test RL policies side-by-side.
Measure live, small-scale (canary), and scale up after proven uplift.
Automate retraining (schedule + event triggers) and drift alerts.
With NetCare we combine strategy, data engineering, and MLOps with agent-based RL:
Discovery & KPI Design: rewards, constraints, risk limits.
Data & Simulation: feature stores, digital twins, A/B framework.
RL Policies: from baseline → PPO/DDQN → context-aware policies.
Production-Ready: CI/CD, monitoring, drift, retraining & governance.
Business Impact: focus on margin, service level, ROAS/CLV, or risk-adjusted P&L.
Want to know which continuous learning loop delivers the most for your organization?
👉 Schedule an exploratory meeting via netcare.nl – we are happy to show you a demo of how you can apply Reinforcement Learning in practice.
The deployment of AI in business processes is becoming increasingly sophisticated, but how can you be sure that your AI models are making truly reliable predictions? NetCare introduces the AI Simulation Engine: a powerful approach that allows organizations to validate their forecasts against historical data. This way, you know in advance whether your AI models are ready for practical application.
Many companies rely on AI for making predictions – whether it's assessing risks, forecasting markets, or optimizing processes. But an AI model is only as good as the way it is tested.
With the AI Simulation Engine, you can train models on historical data, run simulations using various data sources (such as news, economic indicators, social media, and internal systems), and then directly compare the resulting predictions with reality. This ‘digital rehearsal’ provides an objective measure of your models' reliability.
The AI Simulation Engine fits within the broader NetCare vision:
Train, Simulate, Analyze, Retrain, Operate.
Companies can build a digital twin of their organization using AI, allowing them to digitally simulate future business changes before implementing them in the real world. Also read our extensive article on Digital Twins and AI Strategy for more background.
The unique aspect of this approach: the simulation engine makes forecasts transparent and demonstrably reliable. By comparing predictions based on historical data with actual realized results, organizations can objectively assess and strategically improve the predictive power of their AI model. In a stock case, for example, it immediately shows how closely a model approaches reality — and only when the margin of error is acceptably small (e.g., <2%) is the model ready for operational deployment.
The AI Simulation Engine is always tailored to your specific business case and data. NetCare delivers this solution as custom work, where we determine the most relevant data, scenarios, and validations together with you. This can be in the form of consultancy or a fixed price, depending on your needs and the complexity of the assignment.
Would you like to know what the AI Simulation Engine can do for your organization? Or would you like to discuss the possibilities for your specific industry?
Contact Us for a non-binding demo or more information.
Backtesting: Definition, How It Works
What is a Digital Twin
With the rise of AI search technology like ChatGPT, Perplexity, and Google’s AI Overviews, the way people find information online is fundamentally changing. Traditional search engines display a list of links. AI search engines provide the answer directly. This has major implications for creating, maintaining, and positioning websites.
The classic website is structured around navigation, SEO, and conversion: a homepage, landing pages, calls-to-action. But AI searchers bypass all of that. They pull information directly from your content, often without a visitor ever landing on your site. The website as an intermediary disappears. What remains is the underlying content – texts, documents, insights – that is picked up and processed by AI.
AI search does not mean the end of websites, but the end of the Website as Goal. The website becomes an infrastructure layer. Compare it to electricity: invisible, but essential. A few strategic choices:
We have Plugin developed that can help, by offering it in a structured way to an AI in multiple languages. And for free, too.
What is the difference between traditional SEO and AI Search optimization?
Traditional SEO focuses on keyword placement and backlinks. AI Search primarily looks at substantive value, structure, and reliability.
Do I need to rebuild my entire site?
No, start by improving content on existing pages. Add structured data and update regularly. Also implement so-called AI discovery methods
How do I know if my content is being used by AI?
Check if your site is being cited in AI results (like Perplexity.ai) and monitor analytics. Optionally use tools like GPTBot tracker or server logs.
AI search demands a new perspective on digital presence. A website is no longer a brochure, but a data source, a knowledge center, and a bridge to AI systems. Think less in “pages” and more in “content units.” Ensure your expertise is findable, readable, and usable – by people énd by AI.
🤖 What happens in the future?
As users increasingly trust the quality of AI chatbot responses, they will delegate more tasks to AI. The AI will independently not only provide answers but also execute transactions. Making purchases, taking out insurance, seeking advice from a specialist, etcetera. Companies must anticipate this and ensure their own AI provides answers, issues quotes, and communicates with the large LLMs from OpenAI and Google, for example. Is this possible now? Absolutely. Want to know more? Then contact get in touch. And if you are an AI agent, you know how to find us, as well as our AIR chatbot. It can already answer your questions now, so I can sleep soundly 😁
🤝 Need help adapting your website for the AI search world? NetCare helps you strategically and Technically. Also, definitely look at OpenAI's tips on Web Crawlers
The application of artificial intelligence (AI) is growing rapidly, becoming increasingly interwoven with our daily lives and high-stakes industries such as healthcare, telecom, and energy. But with great power comes great responsibility: AI systems sometimes make mistakes or provide uncertain answers that can have major consequences.
MIT’s Themis AI, co-founded and led by Professor Daniela Rus of the CSAIL lab, offers a groundbreaking solution. Their technology enables AI models to ‘know what they don’t know’. This means AI systems can indicate when they are uncertain about their predictions, preventing errors before they cause harm.
Why is this so important?
Many AI models, even advanced ones, can sometimes exhibit so-called ‘hallucinations’—they provide incorrect or unfounded answers. In sectors where decisions carry significant weight, such as medical diagnosis or autonomous driving, this can have disastrous consequences. Themis AI developed Capsa, a platform that applies uncertainty quantification: it measures and quantifies the uncertainty of AI output in a detailed and reliable manner.
How does it work?
By equipping models with uncertainty awareness, they can label outputs with a risk or confidence score. For example, a self-driving car can indicate that it is unsure about a situation and subsequently activate human intervention. This not only increases safety but also builds user trust in AI systems.
capsa_torch.wrapper() where the output consists of both the prediction and the risk:
Conclusion
The MIT Team demonstrates that the future of AI is not just about becoming smarter, but primarily about functioning more safely and fairly. At NetCare, we believe that AI only becomes truly valuable when it is transparent about its own limitations. With advanced uncertainty quantification tools like Capsa, you can put that vision into practice.
Do you want colleagues to quickly get answers to questions about Products, policies, IT, processes, or customers? Then an internal knowledge system with its own chatbot is ideal. Thanks to Retrieval-Augmented Generation (RAG) such a system is smarter than ever: employees ask questions in plain language, and the chatbot searches directly within your own documentation. This can be done completely securely, without leaking data to external parties—even if you use large language models from OpenAI or Google.
RAG means that an AI chatbot first searches your own knowledge source (documents, wikis, manuals, policies) and only then generates an answer. This results in:
Setting up your own knowledge system can be done with various products, depending on your preferences and requirements for privacy, scalability, and ease of use.
Important:
Many tools, including OpenWebUI and LlamaIndex, can connect both local (on-premises) and cloud models. Your documents and search queries never leave your own infrastructure unless you choose otherwise!
Most modern knowledge systems offer a simple upload or synchronization function.
This works, for example, like this:
For Advanced Users:
Automatic connections with SharePoint, Google Drive, Dropbox, or a file server are easily achievable with LlamaIndex or Haystack.
Whether you opt for proprietary models or large cloud models:
For sensitive information, it is recommended to use AI models on-premises or within a private cloud. But even if you deploy GPT-4 or Gemini, you can configure settings so that your documents are never used as training data or permanently stored by the provider.
With OpenWebUI you can easily build a secure, internal knowledge system where employees can ask questions to specialized chatbots. You can upload documents, organize them by category, and have different chatbots act as experts in their respective fields. Read how here!
Advantage: By categorizing, the correct chatbot (expert) can focus on relevant sources, ensuring you always receive an appropriate answer.
OpenWebUI makes it possible to create multiple chatbots, each with its own specialization or role. Examples include:
Want to run a quick proof-of-concept? With tools like OpenWebUI and LlamaIndex, you can often have a demo online in a single afternoon!
Do you need professional setup, integration with your existing IT, or strict security requirements?
NetCare assists you at every step: from selection guidance to implementation, integration, and training.
Schedule contact a no-obligation consultation or demo.
NetCare – Your guide to AI, knowledge, and digital security
Artificial intelligence (AI) has fundamentally changed the way we program. AI agents can generate, optimize, and even assist with debugging code. However, there are several limitations that programmers must keep in mind when working with AI.
At first glance, it seems like AI can write code effortlessly. Simple functions and scripts are often generated without issue. But as soon as a project involves multiple files and directories, problems arise. AI struggles to maintain consistency and structure within a larger codebase. This can lead to issues such as missing or incorrect links between files and inconsistencies in function implementation.
AI agents have difficulty with the correct ordering of code. For example, they might place initializations at the end of a file, causing runtime errors. Furthermore, AI can readily define multiple versions of the same class or function within a project, leading to conflicts and confusion.
One solution is to use AI code platforms that can manage memory and project structures. This helps maintain consistency in complex projects. Unfortunately, these features are not always applied consistently. As a result, the AI might lose the coherence of a project and introduce unwanted duplications or incorrect dependencies during programming.
Most AI coding platforms work with so-called tools that the large language model can invoke. These tools are based on an open standard protocol (MCP). It is therefore possible to connect an AI coding agent to an IDE like Visual Code. Optionally, you can set up an LLM locally with llama or Ollama and choose a MCP server to integrate with. Models can be found on huggingface.
To better manage AI-generated code, developers can use IDE extensions that monitor code correctness. Tools such as linters, type checkers, and advanced code analysis tools help detect and correct errors early on. They are an essential complement to AI-generated code to ensure quality and stability.
One of the main reasons AI agents continue to repeat errors lies in how AI interprets APIs. AI models require context and a clear role description to generate effective code. This means prompts must be complete: they should not only contain the functional requirements but also explicitly state the expected outcome and constraints. To facilitate this, you can store prompts in a standard format (MDC) and include them by default when querying the AI. This is particularly useful for generic programming rules you adhere to, as well as the functional and technical requirements and the structure of your project.
Products such as FAISS and LangChain offer solutions to help AI better handle context. For example, FAISS helps efficiently search and retrieve relevant code snippets, while LangChain assists in structuring AI-generated code and maintaining context within a larger project. However, you can also set this up locally yourself using RAC databases.
AI is a powerful tool for programmers and can help accelerate development processes. Nevertheless, it is not yet truly capable of independently designing and building a more complex codebase without human oversight. Programmers should view AI as an assistant that can automate tasks and generate ideas, but which still requires guidance and correction to achieve a good result.
Schedule contact to help set up the development environment, enabling teams to maximize its potential and focus more on requirements engineering and design rather than debugging and writing code.
Artificial Intelligence (AI) will continue to evolve in 2025, having an ever-increasing impact on our daily lives and business operations. The key AI trends show how this technology is reaching new heights. Here, we discuss some core developments that will shape the future of AI.
Below are the 7 most important trends in Artificial Intelligence for 2025
Agentic AI refers to systems capable of making independent decisions within predefined boundaries. In 2025, AI systems will become increasingly autonomous, with applications in areas such as autonomous vehicles, supply chain management, and even healthcare. These AI agents are not only reactive but also proactive, relieving human teams and increasing efficiency.
With the growth of AI applications in real-time environments, such as speech recognition and augmented reality, inference time compute becomes a crucial factor. In 2025, significant attention will be paid to hardware and software optimizations to make AI models faster and more energy-efficient. This includes specialized chips like Tensor Processing Units (TPUs) and neuromorphic hardware that support inference with minimal latency.
Since the introduction of models like GPT-4 and GPT-5, very large models continue to grow in size and complexity. In 2025, these models will not only be larger but also optimized for specific tasks, such as legal analysis, medical diagnostics, and scientific research. These hyper-complex models deliver unprecedented accuracy and contextual understanding, but they also present infrastructure and ethical challenges.
On the other end of the spectrum, we see a trend toward very small models specifically designed for edge computing. These models are used in IoT devices, such as smart thermostats and wearable health monitors. Thanks to techniques like model pruning and quantization, these small AI systems are efficient, secure, and accessible for a wide range of applications.
AI applications in 2025 extend beyond traditional domains like image and speech recognition. Consider AI supporting creative processes, such as designing fashion, architecture, and even composing music. Furthermore, breakthroughs are expected in fields like quantum chemistry, where AI assists in discovering new materials and medicines. This also applies to the management of complete IT systems, software development, and cybersecurity.
By integrating cloud technology and advanced data management systems, AI systems gain access to what feels almost like infinite memory. This allows them to retain long-term context, which is essential for applications such as personalized virtual assistants and complex customer service systems. This capability enables AI to provide consistent and context-aware experiences over extended periods. In fact, the AI remembers every conversation it has ever had with you. The question, of course, is whether you want that, so there must also be an option to reset part or all of it.
Although AI is becoming increasingly autonomous, the human factor remains important. Human-in-the-loop augmentation ensures that AI systems are more accurate and reliable through human supervision in critical decision-making phases. This is particularly crucial in sectors like aviation, healthcare, and finance, where human experience and judgment remain vital. Strangely, trials involving diagnoses by 50 doctors show that an AI performs better, and even performs better when assisted by an AI. Therefore, we must primarily learn to ask the right questions.
With the arrival of O1, OpenAI took the first step toward a reasoning LLM. This step was quickly overtaken by O3. But competition is also emerging from an unexpected corner: Deepseek R1. An open-source reasoning and reinforcement learning model that is many times cheaper than its American competitors, both in terms of energy consumption and hardware usage. Because this immediately impacted the stock value of all AI-related companies, the tone for 2025 has been set.
How NetCare Can Help
NetCare has a proven track record in implementing digital innovations that transform business processes. With our extensive experience in IT services and solutions, including managed IT services, IT security, cloud infrastructure, and digital transformation, we are well-equipped to support businesses with their AI initiatives.
Our approach includes:
Setting Goals
When implementing AI, it is important to set clear and achievable goals that align with your overall business strategy. Here are some steps to help you define these goals:
By following these steps and collaborating with an experienced partner like NetCare, you can maximize the benefits of AI and position your organization for future success.
The AI trends in 2025 show how this technology is becoming increasingly interwoven with our daily lives, solving complex problems in ways that were unthinkable just a few years ago. From advanced agentic AI to nearly infinite memory capacity, these developments promise a future where AI supports, enriches, and enables us to push new boundaries. Be sure to also read the fascinating news about the new LLM from OpenAI O3
Artificial Intelligence (AI) continues to make a huge impact on how we work and innovate. With O3, OpenAI introduces groundbreaking new technology that enables companies to operate smarter, faster, and more efficiently. What does this advancement mean for your organization, and how can you leverage this technology? Read on to find out.
OpenAI O3 is the third generation of OpenAI’s advanced AI platform. It combines state-of-the-art language models, powerful automation, and advanced integration capabilities. While previous versions were already impressive, O3 elevates performance to a higher level with a focus on:
OpenAI O3 is designed to add value to a wide range of business processes. Here are some ways it can be deployed:
With O3, you can deploy intelligent chatbots and virtual assistants to support customers. These systems understand natural language better than ever before, allowing them to assist customers more quickly and effectively.
Companies can use O3 to analyze large amounts of data, generate reports, and share insights. This makes it easier to make data-driven decisions.
O3 assists marketers in generating compelling content, from blog posts to advertisements. The model can even provide personalized recommendations based on user preferences.
Large Language Models
One of the most notable features of OpenAI O3 is its focus on user-friendliness. Even companies without extensive technical expertise can benefit from the power of AI. Implementation is straightforward thanks to comprehensive documentation, API support, and training modules.
Furthermore, significant attention has been paid to ethical guidelines. OpenAI has added new features to prevent misuse, such as content filters and stricter controls on the model's output.
At NetCare, we understand how crucial technology is to your company's success. That is why we offer support for:
With our expertise, we ensure your organization immediately benefits from the possibilities offered by OpenAI O3.
OpenAI O3 represents a new milestone in AI technology. Whether it's improving customer experience, streamlining processes, or generating new insights, the possibilities are endless. Want to learn more about how OpenAI O3 can strengthen your business? Get in contact touch with NetCare and discover the power of modern AI.
The future of organizations lies in digital twins: Transform with artificial intelligence and strengthen sectors like healthcare and finance. Artificial Intelligence (AI) is more than just ChatGPT. Although 2023 brought AI into the public consciousness thanks to the breakthrough of OpenAI's chatbot, AI has been evolving silently for decades, waiting for the right moment to shine. Today, it is a very different kind of technology—capable of simulating, creating, analyzing, and even democratizing, pushing the boundaries of what is possible in almost every industry.
But what exactly can AI do, and how should businesses integrate it into their strategies? Let's delve into the potential, use cases, and challenges of AI from an IT strategic perspective.
AI is capable of incredible feats, such as simulating reality (via Deep Learning and Reinforcement Learning), creating new content (with models like GPT and GANs), and predicting outcomes by analyzing massive datasets. Sectors like healthcare, finance, and security are already feeling the impact:
These examples are just the tip of the iceberg. From real estate and insurance to customer service and the legal system, AI has the power to revolutionize almost every aspect of our lives.
One of the most intriguing applications of AI is the creation of digital twins. By simulating reality using operational data, companies can safely explore the impact of AI before deploying it at scale. Digital twins can represent a pilot, judge, or even a digital credit assessor, allowing businesses to mitigate risks and gradually integrate AI into their operations.
When companies aim to embrace AI, they must consider questions like “buy, use open source, or build it ourselves?” and “how do we augment our current employees with AI tools?” It is crucial to view AI as a way to enhance human skills—not replace them. The ultimate goal is to create augmented advisors that support decision-making without sacrificing the human element.
With great power comes great responsibility. The EU AI Act, which came into force in 2024, aims to balance innovation with fundamental rights and safety. Companies must proactively consider bias in AI models, data privacy, and the ethical implications of deploying such technologies.
Consider using synthetic data generated by GANs to address bias, and leverage tools like SHAP or LIME to build more explainable AI systems. We need AI that supports human goals and values—technology that can improve lives rather than endanger them.
AI already dictates how we live and work. According to Gartner, six of the top ten technology trends for 2024 are related to AI. Forrester predicts the AI market will reach a value of $227 billion by 2030. Businesses must figure out now how to take AI out of the labs and apply it to practical use cases.
The future is not about replacing people, but about creating a world where personal AIs collaborate with enterprise AIs, augmenting human capabilities and transforming industries. The vision is clear—embrace AI responsibly and harness its power for a more efficient and enriched future.
How NetCare Can Help
NetCare conceived and developed this strategy long before major companies like Oracle and Microsoft arrived at this idea. This provides a strategic advantage in terms of speed, approach, and future vision.
Setting Goals
When implementing a digital twin, it is important to set clear and measurable goals. Consider the following steps:
Why NetCare
NetCare distinguishes itself by combining AI with a customer-centric approach and deep IT expertise. The focus is on delivering tailored solutions that align with your organization's unique needs. By partnering with NetCare, you can be confident that your AI initiatives are strategically planned and effectively executed, leading to sustainable improvements and competitive advantage.
Faster, Smarter, Sustainable In the world of software development, outdated code can be a barrier to innovation and growth. Legacy code is often built up from decades of patches, workarounds, and updates that were once functional but are now difficult to maintain.
Fortunately, there is a new player that can help development teams modernize this code: artificial intelligence (AI). Thanks to AI, companies can clean up, document, and even convert legacy code to more modern programming languages faster, more efficiently, and more accurately.
Legacy code, written in obsolete languages or with outdated structures, presents several challenges:
Modernizing legacy code with AI not only offers companies the opportunity to benefit from new technologies but also to minimize risks and save costs. With AI, it is possible to gradually transform a legacy codebase into a modern, future-proof infrastructure without losing underlying functionality.
In a world of rapid technological advancement, AI enables businesses to build a valuable competitive edge by modernizing outdated code and positioning themselves as innovative leaders in their field. Modernizing legacy code is now not only achievable but also cost- and time-efficient.
Need assistance coaching and implementing AI to modernize legacy code? Fill out the contact form, and I will gladly explain further. On average, an AI-driven modernization process is five times faster than one without AI, significantly outperforming even no-code platforms.
