Can you share the story behind the founding of Trinetix and your journey to becoming a strategic technology partner for Fortune 100 companies?

I’ve always been fascinated by the pace of innovation, the way new things change our lives with a click. But I’ve also noticed how fast this evolution has led to an overheated market. In 2011, the competition for being the next revolutionary provider and game-changer was extremely intense. To me, it was a time of opportunity for creating something unique and outstanding. So, I took action.

Trinetix has developed AI chatbots, digital assistants, and AI-powered data intelligence solutions. Can you elaborate on how these technologies are transforming operations for your clients?

In short, I’d say that leveraging AI enables clients to accomplish more in less time—and by more, I mean much, much more. Today, businesses have a wealth of valuable insights at their fingertips, but finding them requires organizing, categorizing, and validating data. If done by hand, the entire process can take months. Sometimes, there is too much data, and even 10 experts working 24/7 for months won’t suffice.

How do you ensure that your AI solutions are tailored to meet the specific needs of each client, particularly in diverse industries like logistics and healthcare?

Research comes first. Always.
Any AI model is as strong as the data used for training it and the knowledge of the niche it's built for. So, we always start our work with a discovery session. It helps us explore how an enterprise operates, identify its strong points, and study its key competitors.

Our top priority is to put our clients’ vision and needs into features of the future solution—so we also build from their experience, research key enterprise processes, and discuss ways of addressing constraints.

Can you discuss a recent project where Trinetix integrated generative AI to solve a critical business challenge for a client? What were the key outcomes?

There has been such a case. A Fortune 500 client operating in freight management came to us with a request to transform their request-for-proposal (RPF) management processes.

Since they were handling their RPF tasks manually, they were dealing with slow response times and calculations while accumulating heaps of unstructured data (images, screenshots, emails) — which were never converted into value. Accordingly, great opportunities were either lost among the data or in delayed tasks.

What are the main challenges you face when implementing generative AI solutions, and how do you overcome them?

The way I see it, many challenges stem from the human factor. For instance, when an enterprise adopts GenAI, employees worry that they are being replaced. As a result, AI meets organizational resistance, which defeats the entire point of technology adoption.

This is where we work together with enterprise leaders, helping them promote the change across their company. I think it's crucial to address fears from a fact-based angle, providing a realistic perspective on the strengths and weaknesses of the technology. We also include employees in the development process, establishing feedback loops and showing them how the technology works and why it benefits them.