By: Aukje Weening

I’d like to take you along on my journey as a Product Manager in the world of AI. At Fryqua, we monitor and collect large amounts of data from companies and their users, with all data fully manageable on-premises by the customer. There are no backdoors for us as the supplier—or for hackers. Our Fryqua solutions do not require an internet connection, which brings me to one of my main focus areas: can we build and deliver a stable, stand-alone AI agent that works entirely offline?

AI developments are moving incredibly fast. At times, it feels like I’m being overtaken by new techniques and methods every three months. Still, the steps in AI development remain largely the same:

Step 1 – Define the goal:
Before building an AI model, you must clearly define the problem you want to solve. This is the most critical step. Without a well-defined question, your AI solution won’t be effective.

Step 2 – Data collection:
The power of AI is directly tied to the power of your data. Everything starts with data. Fryqua has access to unique datasets that can significantly enhance our AI. This allows us to:

  • Answer questions that are currently out of reach for the customer.
  • Maintain full control over the source data, including raw data.

Step 3 – Choosing the right platform:
There are hundreds of AI algorithms available, and new platforms and techniques emerge constantly. Every problem requires a specific approach, and it’s essential to choose the right technology that fits the customer’s needs and the nature of the data.

Step 4 – Training (and optimization?):
After building the AI agent, the real work begins: training and optimization. This is a continuous process that continues even after the AI is deployed. Only through repeated optimization can the model remain relevant and effective.

Step 5 – Implementation:
Once the AI model has been sufficiently trained and optimized, it can be implemented in a real-world scenario. This is where it proves its value in practice and can be further refined based on live data and user feedback.

With this structured approach, I aim to explore how we can build a powerful, offline, and secure AI agent within Fryqua—an AI solution that is independent of external networks, yet delivers maximum intelligence and efficiency to the user.