Notes from three years of building Neople

What building an AI company taught us about timing, trust, and reality

Three years ago, Neople started in a way that now feels both recent and very far away.

There was no clear playbook. No crisp category. No shared understanding in the market of what “AI at work” was supposed to look like. We were four people with a strong conviction that something fundamental in how people worked with software was broken, and that AI could shift it.

What we underestimated was how much would change around us while we were building.

The market shifted. The technology developed. Customer expectations changed. And we had to keep deciding, sometimes weekly, whether we were early, wrong, or just impatient.

This is a reflection on what actually happened over those three years, and what we learned building an AI company from the Netherlands, in public, and in real time.

Year one felt obvious, and that gave us momentum

In the beginning, things felt strangely clear.

AI was emerging. SaaS felt clunky. People were overwhelmed by tools. The idea clicked: a digital coworker that could take work off your plate and help teams operate better.

That clarity gave us momentum. It helped us move fast, make decisions, and get something real into the hands of customers.

What we learned along the way is that early clarity often hides complexity. The technology was promising, but uneven. Customers were interested, but still figuring out how this fit their work. Internally, product decisions were shaped as much by what was feasible that month as by what the longer-term vision suggested.

Over time, that taught us something important. There was never going to be a single straight line from idea to outcome. Progress would come from constant adjustment, not from following a fixed plan.

That understanding changed how we build. It made us more attentive to constraints, more patient with sequencing, and more deliberate about what to solve next.

We learned to build for a future that arrives in pieces

One of our biggest lessons was about timing.

We raised early rounds believing that classic SaaS patterns would fade quickly, and AI-driven systems would take over large parts of operational work. Directionally, we still believe that.

What we learned in practice is that technology matures in pieces. Some capabilities moved incredibly fast. Others took longer. That uneven pace shaped how customers could realistically use what we built.

Starting with a more service-heavy approach helped us learn deeply. It showed us where automation worked, where it broke down, and where people needed visibility and control. Those insights now directly inform the product we are building.

The takeaway was constructive, not discouraging. You cannot compress maturity, but you can design for it. When the tech is still forming, you need bridges that work today and foundations that support what comes next.

That perspective has made our product clearer, more grounded, and better aligned with how teams actually grow into new systems.

The market clarified faster than we expected

Early on, around early 2023, the main challenge was adoption.

People approached AI carefully. Teams wanted to understand limits, build trust, and stay close to decisions. The focus was on assisting and aiding the employee instead of automating complete workflows, and human control played an important role in making AI usable at all.

Then expectations moved quickly.

Customers began asking different questions. Where automation could take over. Where outcomes could be faster. Where AI could operate with more independence. The change was driven by better models, broader exposure to AI tools, and growing pressure inside organizations to move faster.

That shift taught us something valuable. We had learned how to build trust first. Now the market was ready to build on top of it.

Reworking product, positioning, and sales at the same time was demanding, but it also sharpened our understanding of what customers actually wanted next.

Building in the Netherlands kept us honest

There is something grounding about building a company here.

Dutch customers are direct. They ask what works, what doesn’t, and how long it takes. There is little patience for abstract futures without practical value.

We would sometimes have a conversation with investors about a post-SaaS world in the morning, and then talk to a customer in the afternoon who just wanted to know how we were different from a chatbot or what kind of APIs we had versus custom integrations.

Both perspectives were valid. Holding them at the same time was exhausting.

It taught us that vision cannot replace usefulness. If your product does not make someone’s Monday easier, the long-term story does not matter yet.

Customers often bought “AI” before they bought a solution

One thing that surprised us consistently was how rarely customers arrived with a sharply defined problem.

In classic SaaS, people show up with pain. Too slow. Too expensive. Too manual.

With AI, many showed up with something softer: pressure to adopt AI, pressure from leadership, or a sense that they were falling behind. Customer support seemed to be the most logical place for these teams to start with their first foray into AI, with clear and quick wins in staffing, seasonality, localization, and training.

This fact alone meant we were not just delivering a tool. We were helping customers figure out what to do with it and exploring the potential gains together.

Some thrived in that openness. Others struggled. The same product could feel transformative to one team and irrelevant to another. AI maturity varied wildly, even within the same industry.

This forced us to accept a difficult truth: product success in AI depends as much on readiness as on features.

Growth moments didn’t feel big when they happened

Some milestones only felt real in hindsight.

Raising our first round changed how seriously others took us, but also how seriously we had to take ourselves. Acquiring another team and suddenly being twenty people in a room made the company feel real in a new way.

Later, realizing that Neople was considered a serious player in its niche happened quietly. There was no single moment. Just a gradual accumulation of customers, conversations, and trust.

That pattern repeated often. The biggest shifts were slow while happening, and obvious only later.

What we learned after three years

We’re very aware of how privileged we are to be building a tech company in the AI space right now. We’ve been trusted by investors to explore uncertain territory, and by a team that shows up every day to build something that did not exist before. That combination of trust and timing is rare, and we don’t take it lightly.

Sharing what we’ve learned over the last three years is our way of giving something back, and of being honest about what actually happens when you try to build in a market that keeps moving under your feet.

1) There is no stable ground in AI, only moving reference points

Your product, your customers, and the technology evolve at the same time. Planning too far ahead creates false certainty.

2) Timing matters as much as ideas

Building a product that was perhaps too early still hurts. Vision needs to be paired with something customers can use now.

3) Customers buy readiness as much as capability

AI value depends on data, trust, ownership, and internal clarity. Software alone does not solve that.

4) Hiring speed amplifies both progress and chaos

Scaling roles quickly changes culture, decision-making, and momentum. Money increases responsibility faster than it increases clarity.

5) Building in a pragmatic market forces better products

Direct feedback and low tolerance for hype are painful, but they reduce long-term delusion.

Where we are now

Bas: Looking back, I still believe the core frustration we started with is real. Technology often forces people to adapt to it, instead of the other way around. We maybe underestimated how long it would take to change that properly, but I truly believe in a future where technology is more natural.

Hans: I think the real work is learning to hold ambition and reality at the same time. The future we believe in is still coming. Our job is to build something useful on the way there, without losing ourselves in either hype or fear.

Three years in, the biggest lesson is not about AI or SaaS.

It is about staying flexible without becoming generic, and stubborn without becoming blind.

And accepting that building a company is less about executing a plan, and more about adjusting your understanding faster than the world changes around you.