How Tomas Franc Turned a Consulting Concept into a SaaS Powerhouse

After being laid off, Tomas Franc chose not to job hunt—he chose to build. With a background in SaaS and a long-standing passion for fixing knowledge gaps in fast-moving teams, Tomas launched NextKS: an AI-powered Slack-native platform that turns repetitive questions into lasting answers. In this edition of the Wantrepreneur to Entrepreneur Spotlight series, Tomas shares how AI transformed his idea from a consultancy model into a scalable SaaS product—and how a mathematical model, not MVP code, gave him the confidence to go all in. His journey is a masterclass in timing, execution, and truly listening to customer pain.

Hi, Tomas Franc! Thanks for joining us today. Tell us about your business. Who do you serve, how do you serve them, and what's the impact that your business and work makes?

I’m building NextKS Framework, an AI-powered knowledge-sharing platform designed for companies that live and breathe inside Slack. We primarily serve remote and fast-scaling organizations - the ones where people spend too much time chasing answers, repeating the same explanations, or losing knowledge in endless chat threads.

Here’s how we serve them:

• Slack-native AI Assistant: Team members can ask questions right in Slack and get fast, accurate answers pulled from the company’s own knowledge base.

• Expert Routing: If AI doesn’t know the answer, the question is automatically routed to the right subject matter expert - so no question gets stuck or ignored.

• Continuous Learning: Every resolved question becomes part of the knowledge base, meaning the same question never has to be answered twice.

The impact is twofold:

• Productivity - Teams reclaim hours each week that used to be wasted searching or repeating answers.

• Culture - Knowledge stops being tribal or locked in a few heads. Instead, it’s shared, searchable, and scalable, which makes growing organizations far more resilient.

In short: we turn repetitive questions into lasting answers, helping teams move faster without burning out their experts.

Tell us about the moment you finally felt like you went from wantrepreneur to entrepreneur.

For me, it wasn’t a glamorous "aha!" moment. I was laid off, and suddenly I had to decide what was next. Instead of rushing back into another job, I asked myself: What if I used this moment to finally build something of my own?

That was the shift - moving from just talking about ideas to actually acting on one. I started mapping out the concept, building the product, and putting myself in conversations where I had to pitch what I was creating. That process of committing, of showing up as the founder of something real, was the moment I stopped being a wantrepreneur and started being an entrepreneur.

Describe the moment or period in your life/career that motivated you to make the entrepreneurial leap.

The motivation came when I realized that, for the first time, I actually had everything I needed to take the leap. I had a viable idea that solved a problem I’ve been passionate about for years: how organizations share and retain knowledge. It’s a challenge most companies underestimate, but I’ve seen firsthand how much time and energy it costs when it’s left unsolved.

On top of the idea, I finally felt I had the right toolkit: years of experience building prototypes, enough sales background to speak with customers and validate needs, and familiarity with how SaaS companies operate from the inside. Put simply - the excuses were gone.

That combination of personal interest, professional experience, and the right idea at the right time gave me the confidence to step off the safe path and fully commit to entrepreneurship.

Describe a tool, service, or software that has been a game-changer for your business. How does it contribute to your success?

For me, the real game-changer has been AI itself.

At the very beginning, I built a theoretical mathematical model of knowledge sharing and communication. The goal was to identify the key factors and quantify the potential benefits of improving them. The results were clear: eliminating repetitive questions could have a huge business impact.

But at that stage, it was just a concept. Without AI, the only way to implement it was as a consultancy service - costly, time-consuming, and dependent on a team manually keeping the whole process alive. In other words, not scalable.

Reliable AI changed everything. Suddenly, what used to require heavy, ongoing human effort could be automated, seamless, and embedded directly into the way teams already work. That shift made it possible to move from a consultancy model to a scalable, cost-effective, SaaS platform that’s not only easier to deliver, but also far more user-friendly for customers.

We know that success is very often a non-linear path. Tell us about a failure, pivot point, or lesson that changed your course or direction and helped to get you where you are today.

My biggest pivot happened before I even had customers. When I first explored this idea, the only way to implement it was through a consultancy model: running workshops, building processes, and having a team in the background to keep the system alive. It could have worked, but it would have been slow, expensive, and not easily scalable.

The real turning point was when AI matured to a point where it could take over that heavy lifting. Suddenly, what was once a labor-intensive consultancy service could become a self-sustaining SaaS product. That shift completely changed the trajectory of NextKS - from a costly, manual solution to a scalable, automated platform that any team could adopt.

For me, the lesson was simple: timing and technology matter just as much as the idea itself. Without AI, this would still be a consulting concept. With AI, it’s a product.

What unconventional strategy did you employ that significantly impacted your business?

Instead of jumping straight into building, I spent about two months working with a mathematician friend to create a theoretical model of knowledge sharing. The goal wasn’t to perfectly mirror reality (there are far too many variables) but to run a sensitivity analysis that could uncover the true levers of impact.

We tested different assumptions: team size, communication channels, knowledge workflows. I originally hypothesized that some communication channels would prove far more efficient than others, and that enforcing the "right mix" (calls, meetings, wikis, emails, Slack chats) would deliver measurable benefits. But the model disproved that - the differences were negligible.

What stood out clearly, though, was one factor: eliminating repetitive questions. No matter how the variables shifted, this remained the single biggest driver of potential savings. The model showed strong business cases for teams of about 50 employees and up, especially when paired with expected pricing for NextKS.

To make the insight practical, I simplified the model into a calculator on our website so anyone can estimate potential savings for their own team. And the best part? After seven months of real-world usage at Lokalise, the actual dashboards showed a close correlation to the model’s predictions. That confirmed the concept not just in theory, but in practice.

This analytical approach might not be typical for an early-stage founder, but it gave me the confidence that the problem was real, the impact was measurable, and the business case was strong.

What’s something you wish you knew sooner that you’d give as advice for aspiring or newer entrepreneurs?

Even though every startup guide says it, I can’t stress it enough: talk to potential customers as early and as often as possible. But there’s a nuance here that I wish I had truly understood earlier.

Don’t ask your friends what they think about your idea - and don’t ask customers for their opinion on your solution. If you’re building something new, they might not even understand it, and their reactions can be more discouraging than helpful. Customers also tend to ask for something different from what they really need.

Instead, ask them about their current processes. Listen closely for weak spots. For example:

“How do you make sure everyone follows the policies in your wiki?”

“How do you ensure your subject matter experts aren’t stretched between onboarding new hires and focusing on their own work?”

Once you understand those gaps, you can estimate the cost of those inefficiencies and show them the impact. Then ask the critical question: Would you be willing to test a solution that fixes this - and eventually pay for it?

If the answer is yes, you’re on the right track. That kind of validation is worth ten times more than polite feedback or opinions.

Want to dive deeper into Tomas's work? Check out the links below!

• Visit NextKs Framework.com's website: nextksframework.com
• Connect with Tomas Franc on LinkedIn: Tomas Franc