Breaking the Rules: From Rigid Systems to Learning Algorithms

Throughout my software development career, I’ve always believed that with software, anything is possible—it’s just a function of time and resources. But I’ve learned that just because something can be built, doesn’t mean it always should be. Some solutions, while technically possible, would have taken too many resources or just weren’t practical in the real world.

This thinking influenced how I looked at technologies like artificial intelligence. Neural networks had existed since the 1950s, but even by the late 90s and early 2000s, they still felt academic to me—more like research material than something I could actually use for solving real-world engineering problems. At that time, I worked on projects like Design++, Engager, and MagicWords. In particular, MagicWords started as a rule-based system and eventually evolved into my first attempt to integrate neural networks, allowing it to learn and adapt. These projects laid the groundwork for what I'm building today.

The Early Days: Learning from Design++

In the late 1990s, I worked on Design++, an engineering tool that used rule-based logic to automate complex CAD design tasks. It was cutting-edge at the time but rigid—every scenario had to be programmed in advance, limiting flexibility.

I wasn’t an expert in rule-based systems, but I was fortunate to work with a team that really knew how to build these kinds of systems, and I learned a lot from them. They knew how to structure rules to make the software do what we needed, and I gained valuable insights about how powerful rule-based automation could be.

Design++ was impressive in how much it could automate, but it wasn’t flawless. Rule-based systems were rigid, requiring manual updates for any changes. They couldn’t learn or adapt on their own.

This experience taught me both the power and limitations of rule-based systems. They were extremely effective at automating repetitive tasks, but couldn't handle unpredictability or adapt to changing situations. They were only as smart as the rules we gave them.

Design++ still exists today, doing impressive work in its niche. But I knew AI would need to be more flexible, something that could learn as it goes.

Engager: First Attempt at Adaptability

In the early 2000s, I took the lessons from Design++ and applied them to my new project: Engager. The idea behind Engager was to create an intelligent marketing tool that adapts to users in real time, guiding them through their customer journey by adjusting messages and content based on their actions.

I envisioned Engager as a system that could personalize interactions for each user, nudging them toward specific outcomes, like making a purchase or signing up for a service. It would do this by reacting to what users did—or didn’t do—on a website or through messaging channels.

But, like Design++, Engager was built on a rule-based foundation. For it to work, we had to account for every possible scenario in advance, programming the system to respond in specific ways. For example, if a user didn’t complete a purchase, Engager would send them a follow-up message or offer them a discount to encourage conversion.

Even though we had access to user data, Engager still couldn’t adapt dynamically. Every time there was a new scenario, we had to manually update the rules, which made personalizing interactions at scale really difficult.

At the time, I knew neural networks could theoretically solve the problem—offering the ability to learn and adapt in real-time—but they weren’t yet mature enough to be easily used in a real software engineering environment at scale. They required too much complexity and weren’t straightforward to implement for practical, scalable solutions.

Still, despite these limitations, Engager was a commercial success. The technology was adopted by a large company and used in their marketing systems. But even with that success, I felt like the technology wasn’t quite there yet. It couldn’t fully realize my vision for real-time, personalized interactions. I knew there was huge potential, but we needed AI that could evolve, something that could move beyond static rules.

MagicWords: Move Toward Neural Networks

After Engager, I started developing another project called MagicWords. This was my first real attempt to move beyond rule-based systems and toward AI that could learn—or at least filter information more intelligently.

With MagicWords, I wanted to create a system that could intelligently analyze messaging content and enhance conversations in real-time by linking to relevant information—like weather updates or local news. This made the interactions feel more fluid and engaging.

The first version of MagicWords followed the same rule-based approach as Engager. We programmed it to recognize certain keywords and match them to specific content, but it didn’t understand the context. It was still a rigid system that couldn’t adapt without manual updates.

However, version 2 of MagicWords marked a turning point. This version introduced neural networks, which we used for content filtering and matching. While it wasn’t a fully adaptive system yet, it was a huge step forward. The neural network could analyze patterns in the content, filtering out irrelevant information and surfacing more meaningful data.

I didn’t code the neural networks myself—they still felt more academic than practical at that stage. But I worked closely with the team, guiding them toward my vision for what MagicWords could become. The goal was to move away from static rules and see if AI could filter and adapt based on the conversation context. We weren’t quite at the point of learning user preferences yet, but it was the first step toward creating AI that could evolve. MagicWords gave me a glimpse of what the future could hold—where AI wouldn’t just follow static rules but learn and evolve with every interaction.

A New Era of AI for Business

These early projects—Design++, Engager, and MagicWords—shaped my understanding of AI and taught me key lessons about adaptability and real-time learning. Concepts that once seemed out of reach are now possible, thanks to the rapid development of modern AI tools like Large Language Models (LLMs) and neural networks.

These technologies have moved AI beyond rule-based systems, with clear, rational use cases in areas like customer service, content creation, and HR/recruitment. These are the obvious areas where AI excels in automating tasks, streamlining processes, and enhancing scalability. For instance, businesses using AI in customer service have reported up to 30% cost savings, while improving customer satisfaction through real-time, personalized support.

However, the real potential lies in more innovative applications like omnichannel narrative management, where AI not only automates interactions but creates personalized, consistent experiences across all customer touchpoints—whether through chatbots, messaging, emails, or social media. For example, businesses leveraging omnichannel narrative management have reported higher customer retention rates, thanks to consistent and personalized interactions across platforms. By delivering a seamless, ongoing conversation with customers, businesses can foster deeper engagement, improve retention, and build long-term loyalty.

We’re at the beginning of an era where AI doesn't just react, but learns, adapts, and delivers personalized experiences at scale across platforms.

What's Next?

In my next article, I’ll explore how Engager has evolved into a narrative engine that powers more than just marketing—it drives personalized, multi-channel conversations in real time. I’ll show you how conversational AI can reshape your customer journey, adapting to user behavior and keeping interactions consistent across all channels—whether it’s chat, email, or messaging.

Whether you’re curious about how AI can revolutionize your business or you want to explore the latest advancements in conversational tech, stay tuned! Follow me for more insights, or let’s connect to discuss how AI can deliver tangible results for your business.

Oh, and fun fact—since I first built Engager, someone else snagged the name! So, while the AI has evolved, the name needs to evolve too. A new name is on the way, but the tech?Stronger than ever and ready to drive adaptive, real-time conversations.