Every Business Process Outsourcer (BPO) has a dirty secret, and it is one that nobody wants to voice out loud during a Quarterly Business Review: their business model depends on the exact thing their customers are trying to eliminate.

Repetition.

A BPO sells hours. Hours are billable when an agent is typing. Agents type when customers have questions. And customers have the same questions over and over again: "Where is my order?", "How do I return this?", "Why was I charged twice?". The industry has spent twenty years optimising a machine whose fuel source is the repetition its clients are actively trying to burn down.

You can bolt Artificial Intelligence onto that machine. Most of the industry is doing exactly that: a copilot here, a suggested reply there, a shiny AI-powered slide in the sales deck. It does not change the physics of the situation. The agent still types. The clock still ticks. The incentives stay broken.

At Onepilot we believe the answer is fundamentally different. We are not bolting AI onto a BPO. We are rebuilding the entire structure around a different idea: one where AI is not a feature, but the default execution layer.

This post is about the principles behind that choice.

The Repetition Economy

Look at any support queue in e-commerce, fintech, or marketplaces, and you will see roughly the same shape. The distribution of workload follows a predictable pyramid that defines how resources are currently mismanaged across the globe.

Customer Support Volume Distribution

The top 70% is the part that should never have been a job in the first place. A human reading the order status from one screen and typing it into another is not customer service, it is data movement with a friendly tone. It is a waste of human potential and a source of massive operational inefficiency.

The middle 25% is where most AI for support tools quietly fail. They can draft a reply; they cannot execute. They will tell you to issue a refund, but they will not actually issue it. They cannot read the order, check the warehouse, cross-reference the return policy, decide whether to approve, process it in the ticketing system, and notify the customer, all in one coherent action.

The bottom 5% is the genuinely hard, emotional, and novel cases where humans earn their salary. That is also where a BPO’s best agents are wasted today, because they are spending 80% of their shift drowning in the top 70%.

The whole game is moving work down the pyramid. AI eats the top. Humans keep getting better at the bottom. The Onepilot approach turns the operator into a traffic controller, deciding in real time, conversation by conversation, which work goes where. Everything else in this post is downstream of that idea.

A representative trace of a duplicate-charge ticket at Onepilot. The example is constructed for this post: the pipeline, sequence, and event types are accurate to what runs in production.

AI-Layered vs. AI-Native

The standard playbook right now is what we call AI-layered:

1. Take an existing ticket system.

2. Bolt on a Large Language Model (LLM) wrapper.

3. Add a suggested reply button.

4. Call it AI-powered.

This fails for a specific reason: the surrounding system was built to be driven by humans. The workflows, the state machines, the audit trails, the permission models: they were all designed around the concept of "agent clicks button". Drop an LLM into that environment and it can observe, but it can barely act. And when it does act, nobody trusts it, because there is no coherent layer tracking what it did, why it did it, and what happened next.

AI-native is a different thing entirely. The difference is not which model you use. It is not how clever the prompt is. It is a handful of design principles that, taken together, produce a completely different product.

Here are the five that matter most.

Principle 1: The LLM is the Engine, Not the Decision-Maker

This is the single most important idea, and it is the one most products get wrong.

Text-first is the default mode for LLM products. You give the model a big prompt, some tools, and hope for the best. The model decides when to call what, what to say, when to stop, and what to promise. This is great for demos and absolutely terrible in production, because when the same customer question leads to different actions on Tuesday and Wednesday, you do not have a product. You have a lottery.

An AI-native system inverts the relationship. The operational logic, "if the order shipped more than 14 days ago, offer a refund; otherwise, offer a reroute", lives in structured, versioned playbooks defined by humans who understand the business. The LLM’s job is not to decide what should happen. Its job is to interpret messy user input, navigate the playbook, pick the right tool, and write the reply in the brand’s specific voice.

That shift from LLM as brain to LLM as engine is what makes every other property of the system possible. Determinism, auditability, safety, cost predictability, version control, and testability. None of those are achievable when the model is freelancing.

Principle 2: Actions Over Answers

An AI that drafts a refund reply is a demo. An AI that issues the refund is a product.

Most of the industry is still living on the draft-reply side of that line. Suggested responses, macro helpers, and AI-assisted inboxes are genuinely useful, but they are also a local maximum: they cannot take you past the ceiling where a human is still in the loop for every single ticket.

The path past that ceiling runs through actions. This means:

• Reading the order data directly from the database.

• Cancelling the subscription in the billing platform.

• Issuing the refund via the payment gateway.

• Updating the customer record in the CRM.

• Scheduling the reshipment in the warehouse management system.

• Notifying the carrier and logging the resolution.

Every one of those is an API call into a tool you already own: Shopify, Zendesk, Recharge, Klaviyo, HubSpot, the ERP, or the OMS. An AI-native CX platform is, in a very real sense, a coordination layer across all of them. The intelligence is not in the replies, it is in the orchestration.

Which also means the platform has to stay open. The strongest feedback we hear from enterprise customers is: "Do not be another closed-loop vendor." They already have dedicated tools for Quality Assurance, workforce management, and knowledge bases. Our job is not to replace any of that. It is to orchestrate across it. An AI-native operator that refuses to talk to your existing stack is not AI-native. It is merely a walled garden with a new coat of paint.

Principle 3: Conversations are State Machines, Not Chat Logs

A support conversation is not just a chat. It is a state. Consider this flow:

1. Customer opens a ticket with an order number.

2. AI fetches order status.

3. Customer asks "Can I change the delivery address?"

4. AI checks whether the order has shipped.

5. It has: AI offers a reroute or a return-and-rebuy.

6. Customer picks reroute.

7. AI calls the carrier.

8. Customer says "Oh and also, my other order..."

Anyone who has tried to handle this as a growing prompt with message history knows it falls apart within three turns. The context balloons, the model forgets earlier decisions, the tool calls get redone, and the customer gets asked the same question twice.

An AI-native platform treats every conversation as a durable, replayable state, with full memory of what has been asked, what has been tried, which tools have returned what, and which decisions have already been made. If you restart the service or scale the worker pool, the conversation picks up exactly where it left off.

This is also where the uncomfortable, honest truth about LLMs hides: they are non-deterministic, and they will occasionally make the wrong call. When that happens, you want to be able to replay the exact state that led to the bad decision and see where the system went sideways. You cannot do that if your conversation is just a prompt. You can do it if your conversation is a state.

Principle 4: Measure Process, Not Intelligence

Most AI for support dashboards show metrics like % auto-resolved and call it a day. Those numbers are mostly vanity. They do not tell you why a conversation went off the rails, or which business situation is creating friction, or what to change to make it better.

The Core Metric: We do not measure AI intelligence. We measure process performance.

AI-native measurement starts from a different question: what outcome is the customer actually looking for, and how is the system doing at delivering it?

• Not: "Did the AI answer?"

• But: "Did the refund actually process?"

• Not: "Was the intent identified?"

• But: "Did the customer come back thirty minutes later with the same issue?"

We focus on outcomes, not intents. Quality scores must combine resolution, effort, trust, and experience, not a single AI resolution rate headline.

Furthermore, the platform must be honest about what it cannot resolve. Conversations that do not match any playbook are data, the raw material for the next generation of automation. Clustering them, surfacing emerging themes, and flagging friction in workflows that are technically correct but operationally failing is the real value. The AI that gets smarter over time is not the model itself: it is the system built around the model.

Principle 5: Humans are an Upgrade, Not a Patch

This is the part that differentiates an AI-native operator from a pure-play software tool.

Onepilot has thousands of real, trained agents worldwide. They are not a fallback for when the bot gives up. They are the other half of the same system. For every message, the platform decides between four paths:

1. Autonomous: The AI answers directly and resolves the issue.

2. Augmented: The AI drafts, a human reviews, and a human sends.

3. Collaborative: The AI hands over entirely with a full context briefing.

4. Human-Led: A human owns the ticket end-to-end (e.g., VIPs, crises, legal-sensitive cases).

The ratio shifts over time. A new customer or a new playbook results in a lower confidence score, leading to more human intervention. As the playbook matures and the process stabilises, the system moves towards higher autonomy. The system measures itself and tunes these thresholds conversation by conversation. Our operations team and our AI team are the same team: they ship together, they measure together, and neither can succeed without the other.

This is why our Customer Satisfaction (CSAT) scores do not drop as we automate, they climb. We are not firing humans and hoping the bot is good enough. We are using humans for the work where humans are irreplaceable, and letting the machine handle the rest.

Governance, Traceability, Trust

A lot of what separates production-grade AI from a demo isn't the AI at all. It's the governance layer around it. This is the foundation trust gets built on, and in
enterprise CX, without trust, there is no product.

The first layer is traceability. Every conversation the system handles produces a complete, queryable audit trail: which workflow was chosen and why, which tools were called with which parameters, what each tool returned, how the final reply was composed, which knowledge passages were used to ground it, and what each step cost. Not as a byproduct but as the default. If a reply goes wrong, you can replay the state that produced it. If a refund was issued, you can see the chain of decisions that led there. If a customer asks "why did your AI say X last Tuesday?", you have a real answer, not a shrug.

The second layer is governance. Every tool the AI can invoke is explicit and revocable. Every playbook is versioned, you always know what logic was live for a given conversation, even months later. Sensitive actions can be gated by authority rules ("no refunds over €500 without human approval", "no account changes for unverified customers"), restricted to specific customer segments, or run in shadow mode while they're being validated. The humans responsible for the operation can turn capabilities on and off without shipping code. Nothing the system does is invisible to the team running it.

The third layer is safety. Per-customer data isolation. Identity verification before sensitive actions. Rate limits so a logic loop can't escalate into a financial crisis.
Content filtering. Anti-injection defenses so a crafted user message can't override the system's instructions. Honest escalation when a tool call fails - no pretending a refund went through when the API returned a 500. These aren't features, they're preconditions.

The fourth layer is trust at the contract level. ISO 27001 and ISO 18295 certifications. A public trust center where the controls, sub-processors, and incident-response commitments are laid out, not buried in a sales PDF. Data residency options, so a French insurer can keep everything on European infrastructure, a US retail brand can run on a global model, and a regulated healthcare client can run on a private endpoint: all on the same platform. Customer-owned data, exportable and deletable on request.

None of this is glamorous. All of it matters. The fancy AI research papers are fun to read, but the wins in production come from the unfancy parts, and the systems that take governance seriously are the only ones that survive contact with an enterprise procurement team.

The Paradox of Choice for the Modern Buyer

If you are running Customer Experience (CX) at a fast-growing company today, you likely face one of two problems:

Option A: The Legacy BPO The service is cheap, but quality is uneven. Their AI strategy is a single slide in a pitch deck, and you have zero visibility into why a customer received a nonsensical reply last Tuesday. You are essentially paying for headcount, not results.

Option B: The Shiny AI Tool You bought a software-only solution and spent three months trying to configure it. It can answer "Where is my order?" but breaks the moment a customer asks for a complex exchange involving a promotional code and a change of address. You still have to manage the tool, which becomes a full-time job in itself.

What we offer is neither.

We provide an operator that takes full ownership of the queue, much like a traditional BPO, but built on a platform where AI is the default engine. It is a human layer where the humans are an upgrade, not a patch. It remains open at both ends, your tools remain your tools, and your data remains your data.

Where the Industry Goes Next

The old BPO business model monetised repetition. It thrived on inefficiency because inefficiency was billable. The next generation of CX operators will monetise outcomes.

That shift from billing by the hour to being paid for the resolution changes everything about how a support organisation is designed, measured, and run. Your partner's incentives finally align with your own. AI stops being a cost centre to justify and becomes a lever to pull for performance. The humans in the loop get more meaningful work, not less of it.

At Onepilot, this shift isn't a pivot. It's the model we've been running since day one. We've always billed per ticket resolved, not per hour worked, which means we've always been paid for outcomes, and we've never had a reason to protect repetition. Every minute we save on a ticket is a minute we don't bill for. Every question we can automate is one we, by design, want to automate.

That's why the AI era doesn't force us to reinvent our commercial model. It accelerates it. The incentives that made us good at running human-operated support solve it faster, solve it better, don't pad the hours are the exact same incentives that make us good at running AI-native support.

We know which side of that shift we're building on, because we've been on this side the whole time.

Onepilot runs 24/7 in 35+ languages, serving brands in commerce, fintech, and regulated industries across Europe and the North America. If you are tired of paying for the same questions to be answered for the thousandth time, it might be time to move beyond the traditional BPO model.

For me personally, this is a dream that has been nearly two decades in the making. I’ve wanted to be an entrepreneur since I was 12. While other kids were focused on school and sports, I was in my room in the French countryside, teaching myself to code, building software, and designing video games. From developing multiplayer games at 13 to building dozens of SaaS platforms to solve my own problems, my education happened in the terminal, not the classroom.

After years of deep work as a self-taught creator, seeing Onepilot reach this level of global recognition feels like a full-circle moment. It is proof that if you obsess over a problem long enough, you can eventually build the solution.

Elevating the Ancient Art of Service

Customer support is often dismissed as a back-office function, yet it is arguably the most ancient and crucial pillar of commerce. Since the first trade was ever made, the service following the sale has been the true deciding factor of brand loyalty. In an era of infinite choice, support is the only physical link left between a company and its customers. It is the frontier of brand identity. We believe that when service is executed with precision and elegance, it becomes almost sexy in its own right, not through marketing, but through the beauty of a frictionless resolution! (sexy, right?)

For decades, the legacy BPO industry operated on a flawed logic: if you have more customers, you simply hire more people. This created a linear relationship between growth and overhead, resulting in fragmented quality and black box operations. At Onepilot, we started with a different question: How do we turn the ancient art of service into a scalable science?

We didn’t set out to build another call center but to build a support engine. Today, Onepilot manages millions of monthly interactions for over 250 global brands, including Victoria’s Secret, Dash Water, Decathlon, and SNCF, by treating every ticket as a structured data point in a proprietary lifecycle. This is powered by a strict Human-AI Mix, where our dedicated AI Platform handles the complex, multi-turn workflows that typically cause traditional systems to fail. Whether a resolution requires coordinating between a warehouse, a carrier, and a customer, our platform navigates that logic without human friction.

This engine is supported by nine in-house software products that act as the digital backbone for our 2,000+ native agents. It allows our partners to scale their capacity up or down within 48 hours, providing a flexible utility for the modern retail era. But scaling with global players requires more than just speed, it requires an uncompromising stance on Security and Governance. We’ve built Onepilot to meet the most stringent institutional requirements, from full GDPR compliance and ISO 27001 to AI guardrails that prevent hallucinations and ensure brand-safe interactions. Unlike the black box models of the past, we provide full real-time auditability, giving enterprise leaders the transparency their governance structures demand.

Reaching USD35M ARR and securing USD20.5M in funding were never the ultimate goals, they are simply the metrics of our efficiency. As we expand deeper into Europe and beyond, and continue to refine our Generative AI orchestration, our focus remains unchanged: industrialising the boring parts of business. We want to ensure that high-growth companies can focus on their core mission without the fear of their own success becoming an operational bottleneck.

What's Next?

The Forbes recognition is a nod to the past five years of work at Onepilot, but for the 12-year-old kid who started this journey with a laptop and a dream, it’s a reminder that the roadmap for the next ten is where the real scale begins.

Looking back now, as 2025 ended and 2026 begins, I realise the real thread of the year wasn’t output. It was alignment; between what I build, why I build it, and who I am while building it.

What 2025 Was Really About

2025 wasn’t dramatic. It wasn’t about explosive growth or sharp pivots. It was quieter than that, but far more foundational.

• It was about choosing depth over breadth.

• Experimenting and building at my own pace.

• Getting comfortable with steady, measured progress.

• Trusting my internal compass more than external pressure.

• Being okay with myself, my needs, and my ambitions.

Less noise. More intention.

That orientation matters, because it sets the tone for what comes next. In 2026, I want to keep building from that place, not faster, but calmer. Not more reactive, but more resilient.

Where 2025 Forced Honest Reflection

That doesn’t mean everything went smoothly. The harder moments were the most revealing.

Overcommitment early in the year
Trying to do too many things, especially things misaligned with what truly mattered, drained energy unnecessarily. It taught me that saying yes too often is rarely a sign of ambition. More often, it’s a lack of clarity.

Pressure from outside timelines
There’s a subtle weight in trying to keep up with other people’s pace. This year was partly about learning to let that go. In 2026, I want to make decisions based on readiness and alignment, not urgency or comparison.

Rushing outcomes instead of grounding fundamentals
Fast results feel good. Strong foundations feel sustainable. 2025 reminded me which one actually lasts.

Neglecting health as a silent cost
This year made it very clear that health isn’t something you can defer without consequences. I was ill several times, including one serious episode that put me in hospital for a week. It forced a reset I hadn’t chosen. Energy, focus, and resilience all depend on physical and mental health, and no amount of motivation compensates when that foundation cracks. In 2026, nurturing health isn’t an optimisation goal. It’s a prerequisite.

What 2025 Looked Like for Onepilot

Alignment doesn’t mean the absence of results. It means results that come from clarity rather than chaos.

2025 was a strong year for Onepilot, one I’m proud of, not just because of the numbers, but because of how they were achieved.

We crossed €30M in ARR with a healthy margin structure and strong retention. We welcomed 70 new customers, expanded further beyond Europe, and supported clients in over 20 languages. More than 12 million interactions were handled over the year, with quality scores consistently above 89%.

We also shipped. A lot.
Six major products launched, over 3 million AI-driven interactions delivered, and continued investment in systems designed to scale without breaking.

Behind much of this shipping was a deeper shift. Over several months, I designed and built an entire AI platform for Onepilot in my role as CIO. Not a prototype anymore, but a fully operational system, onboarding clients, running in production, and forming the backbone of what comes next.

I’ll be honest about one thing: I built large parts of it alone. Fast. Deep. In a mode where I thrive. I don’t regret that. It was the right way to get it off the ground.

But it also showed me the limits of isolation. Next time, I want to build more with the team. More shared context, more feedback, more collective ownership. Some of my favourite moments this year weren’t technical at all: they were our leadership days, the moments of alignment where we slowed down, talked honestly, and made sure we were moving in the same direction. That’s something I want to intentionally protect and amplify in 2026.

Externally, the company was recognised more than ever, with seven awards, including client-driven ones, and listings among the fastest-growing startups in Europe. These aren’t goals in themselves, but they are signals. They tell us the work resonates.

Most importantly, none of this came at the cost of the team. We grew to 110 employees across 15 nationalities, with real attention paid to balance, diversity, and sustainability. That matters more to me than any single metric.

Staying Grounded in a Noisy World

2025 didn’t happen in a vacuum.

It unfolded against a backdrop of accelerating AI hype, constant announcements, shifting narratives, and a world that often felt unstable. Technologically, everything seemed possible overnight. Politically and geopolitically, the opposite was true: uncertainty, fragmentation, and short-termism.

In that environment, it became tempting to react. To chase momentum. To build louder instead of better.

I chose the opposite.

Not because I’m distant from AI. Quite the contrary. I spent part of the year working deeply on it, designing systems, shipping them into production, and living with their real-world constraints. I believe AI is genuinely transformative, and I’m excited by what it enables.

But that proximity also makes the noise harder to ignore.

There’s a growing gap between what sounds impressive and what actually works. Between demos and durable systems. Between narrative velocity and operational reality.

Transformation without grounding tends to collapse under its own weight. The more noise there is, the more discipline it takes to stay focused on fundamentals: reliability, usefulness, human impact, and time horizons that extend beyond the next announcement or funding cycle.

So I leaned into pragmatism. Into building things that made sense, solved concrete problems, and could survive contact with reality. Embracing AI fully, while remaining deeply sceptical of hype for its own sake.

Holding both at once felt uncomfortable at times. But it also felt necessary.

A Personal Parallel: Learning to Run (Properly)

On a completely different front, 2025 was also the year I started running.

Before that, I barely exercised. No routine, no structure. Running began almost accidentally and quickly became my thing. I love the solitude, the rhythm, the mental clarity. Time alone, but never empty.

Like many beginners, I overdid it.

Too much volume, too quickly, too little patience, which led to a heel stress fracture and an enforced pause of 4 months. Frustrating at the time, obvious in hindsight. Enthusiasm without balance eventually breaks.

Since then, I’ve approached it differently. I now run between 20 and 40 km per week depending on the phase. I’ve started racing. I’m learning how to train, how to recover, how to listen. Progress is slower, but it’s real. And it lasts.

What surprised me most is how closely this mirrored the rest of the year. Running taught me physically what 2025 taught me professionally:

• Consistency beats intensity.

• Rest is part of progress, not a failure.

• Structure enables freedom.

In 2026, I want to take this further: joining a club, adding a competitive dimension, committing to disciplined training and strength work. Not to chase records obsessively, but to explore potential with patience. To see what happens when effort is sustained, not rushed.

Attention, Energy, and Relationships

One of the quieter lessons of 2025 was about attention.

Between work, screens, and constant background noise, it’s easy to feel busy without feeling present. In 2026, I want to reclaim some of that space: less mindless scrolling, fewer default distractions, more intentional focus. Reading more. Being bored sometimes. Letting ideas breathe. I'll also incorporate meditation and yoga into my routine to stay grounded and support my mental health.

The same applies to relationships. This past year was demanding, and not just professionally. In 2026, I want to create space for quality social time, fewer but deeper connections, and to truly reconnect with the people who matter most, especially my partner. Not drifting. Not postponing conversations. Choosing clarity and honesty, even when it’s uncomfortable.

What I’m Carrying Into 2026

I’m not making resolutions. I’m carrying principles. Principles scale better than habits.

Clarity before speed
Every decision should have a reason. Speed without clarity is just noise.

Energy as a finite resource
Protect it. Nurture it. Spend it deliberately, whether at work, in training, or with people.

Rhythm over extremes
Work hard, rest properly, reflect honestly.

So 2026 won’t be about doing more.
It will be about doing better, with intention, steadiness, and fewer unnecessary distractions.

Closing

When I look back at 2025, the common thread is clear.

Whether it was building Onepilot, writing, learning how to run without breaking myself, or learning when to slow down and involve others, the lesson was the same: alignment creates momentum. Not overnight, but over time.

2026 doesn’t feel like a reset. It feels like a continuation, with better balance, clearer boundaries, deeper collaboration, and a stronger trust in steady progress.

And that feels like exactly the right place to be.

For years, I’ve wanted to write about my experience as an entrepreneur. I wasn’t sure how to frame it or which themes to explore. But now I realise that some things can’t really be separated.

Introduction

I’ve wanted to be an entrepreneur since I was 12. While other kids were focused on school, sports, and friendships, I spent most of my time coding websites, building software, designing video games, and figuring out how to share or even sell what I made. I didn’t feel like school was for me. I wasn’t motivated by grades, I just wanted to create things.

I often felt like an outsider, not just socially, but in how I understood the world. The structure of school and relationships didn’t make much sense to me. People followed unspoken rules, seemed content going through the motions, and I couldn’t relate to any of it. What made sense to me was building something of my own.

Being autistic in that environment was brutal. I struggled with my mental health, severe anxiety, depression, constant emotional ups and downs. Life and school became more about surviving than learning. So I escaped into the things that grounded me, often with other people who felt like outsiders too. If I didn’t fit into the system, I would find a way to exist outside of it.

Looking back, I probably came off as pretentious, maybe even a bit contemptuous. I didn’t come from wealth, my dad’s a handyman, my mum’s a nurse, but I grew up with everything I needed, in the countryside. Still, I told myself I was different, that I didn’t need to follow the same rules as everyone else. I looked at teachers and imagined they would stay stuck in their classrooms forever, while I would go on to do something bigger. Maybe that belief pushed me forward. But it also meant I spent years trying to prove to myself that I wasn’t wrong.

That’s where my journey into entrepreneurship really started.
Spoiler: it didn’t go exactly the way I expected.

Young Projects

When I was 13, I remember developing websites from scratch using HTML, CSS, and JavaScript (like many other people!). That was really fun, especially when it was related to video games. I was also exploring all kinds of tech skills and software, from Photoshop to C++ and Java, 3D modelling, and more.

I spent countless hours working on what became the main driver of my teenage years: a massive multiplayer game based on a fork of the MMORPG Flyff. It became a reference in the French-speaking online video gaming world at the time.

I also worked on so many other projects, building platforms and software, usually with a SaaS approach and most of the time to solve problems I was personally facing. I’m talking about dozens of apps and websites, from trip planning tools and dating websites to educational systems designed to help manage schools or universities.

Much later, I had to decide what to do with my life. To be honest, I didn’t want to stay in school, but my parents made me promise to at least finish and get my A-levels, which I eventually did.

Then I almost got pulled into the system and considered doing undergraduate studies in computer science. I actually started and stuck with it for 3 months before realising I was still surrounded by the same bunch of teenagers, except now, they were paying expensive tuition fees to be there. I had already paid for the year out of my own pocket, so I had to make the most of it. That’s when I decided to study marketing on my own (in a computer science school, yes), iterate on project ideas, do some freelancing to repay my student loan, and try to build a network and understand how things actually work in the real world.

One of my first attempts to pitch a project: a trip planning app

Entering the Matrice

Fast forward to the end of 2020. I’m 23 years old. COVID has exploded, and the world shut down. I’ve spent the past few months feeling more vulnerable than ever, struggling with my mental health, watching my routines collapse, and feeling my mood slip away. I’ve been drifting from hospital to hospital, unsure of what comes next.

But something came. A new project, a new venture, something exciting. Something that would require days and nights of imagining, coding, and doing my best in the shortest time possible. After meeting with two ex-colleagues, who would soon become two of my co-founders, I was convinced to jump into this new adventure, and I would be the CTO.

Our beginnings: the original team of co-founders and first hires (in Ghibli-style, because why not)

The first few months were, and still are, incredibly special to me. I can’t describe how much I value that bootstrapping phase, where everything feels possible simply because everything still needs to be built. I started coding the platform’s modules, pushing a thousand lines of code every 24 hours, creating new features, and rapidly expanding the platform.

I worked closely with my company’s CEO, that ex-colleague I had known for years. It was a delight to imagine the future while building the present. I was developing features almost in real time as we brainstormed them in our meetings, turning ideas into reality as fast as we could think them up. I would wake up in the middle of the night to push lines of code, unable to sleep because new ideas kept racing through my mind.

I had to learn to understand the other founders, which wasn’t easy since we had different personalities, backgrounds, and visions. It was particularly challenging for me, as it always is when I have to meet new people, especially in a context where social harmony is expected, not just casual small talk. Navigating unspoken rules, reading between the lines, and figuring out the right way to communicate without overanalysing every interaction felt exhausting. I remember feeling anxious for a long time, constantly questioning if I was saying the right things or if I was coming across the way I intended. To be honest, it’s still pretty much the same today, it just feels a bit safer.

€2.5M

Hey guys, they loved the demo. Let’s grab some pizzas and talk about something

Only four months after writing the first line of code and signing our first clients, we already had several solid offers from VCs. A month later, after accepting the best ones, we decided to officially launch our company to the public. In April 2021, it was born, and we announced our €2.5M fundraising at the same time. We were officially building something very exciting.

Recruiting

Here comes the unknown! I couldn’t keep developing and maintaining the main platform and new products on my own forever. After all, I couldn’t work 24/7, and at some point, we had to build a tech team to prove we could scale. That meant hiring people, conducting interviews, and, eventually, managing them.

I had my first interview. He was a nice guy, and I thought he could code and understand things easily, like me. I decided to hire him, but it turned out to be a mistake. Instead of easing my workload, it added more weight on my shoulders than if I had just worked alone. I remember not understanding how this could happen: why this nice person just wasn’t good enough? It made me sick, draining all my mental bandwidth. So we hired someone new, then another, and another. With each hire, we gained a better sense of skill levels and what we could realistically expect from a developer. Eventually, we had to terminate some contracts. It was really tough.

Conducting interviews, onboarding people, managing them, it was exhausting. I no longer had the energy to code efficiently.

I remember when we moved into our first office, with sales and operations people joining the team. It was exciting, but I had to face a difficult question: how could I be a CTO if I couldn’t manage my team without burning out and becoming useless to the company? The answer seems obvious now: I simply didn’t have to be the CTO anymore!

Product, Innovation and 🇬🇧

We decided that we would find the new CTO for the company and gradually transition myself into the role of CPO. The goal was to build a proper product organisation to support both the tech team and the broader company vision. It was an interesting period. We were still regularly hiring new engineers, and I was working closely with the future CTO before eventually handing over the team to him.

But what is product, anyway? I had no theoretical understanding of it. It’s not something that was taught in school or online, well, it is now, but even then, there’s no single way of doing “it”. What I did know was how to code and how to make things efficient, so I started from there. I iterated on methods, organisation, roadmaps, communication, boards, design, sprints—everything. Time passed and eventually, we hired a product manager to work with me. Then a product designer.

And… wow, hold on a second. I was building a team again. A team I would have to manage. Hmm. I was definitely having a bit of déjà vu.

I wasn’t just doing “product stuff” (whatever that means). I was still coding a lot, helping the engineers, and working on projects on my own to keep things moving quickly. I’ve always valued flexibility and being hands-on where it matters. And once again, I found myself either being slowed down or getting bored by how my role was evolving. Hey, let’s hire a CPO, no? It worked pretty well the first time.

Oh, I almost forgot: I also moved to the UK in the meantime. That meant I wasn’t physically with my team anymore, at least not as often as before. It didn’t exactly make things easier, but I’m genuinely happy with that decision. I got to discover a new place and be part of the daily life in what became our London office, full of lovely people too.

So here we were again, hiring a CPO (well, a Head of Product at the time) and transitioning slowly, giving me the space to focus on what really matters to me: building, shipping, solving problems. Doing things that actually move the company forward.

At that point, we were kind of running out of C-level titles. What could I be next? Can’t I just be Robin?

All right: Chief Innovation Officer it is.

What I once thought was a BS title actually turned out to be a pretty accurate summary of what I do every day: exploring new ideas, testing things quickly, challenging how we work, and building high-value features on my own, while still staying closely connected with the different teams. We also raised a €15M Series A around this time, investors were confident, we felt the same.

Having a lovely time with the UK team! 🎄

Scaling

If I take a moment to reflect on what we’ve achieved so far, it’s kind of nice, at least, I think it is.

In less than four years, we acquired 2 companies (one outsourcer competitor and one AI startup), grew our ARR (annual recurring revenue) from €0 to €25M, built a team of over 100 permanent employees across three countries, and now counting on more than 2,000 contractors throughout Europe and beyond. Today, we are a profitable business.

It’s easy not to reflect, not to take the time to feel proud or grateful. I’m usually just focused on the work, always thinking about what’s next. It always feels like the beginning to me. Like there’s more to build, more to fix, more to figure out, like it was still day one.

Part of the team at the headquarters in Paris (with our awesome CTO Sacha taking the selfie)

A Different Way of Thinking

I haven't talked about autism as much as you might have expected so far in this article, but it’s everywhere between the lines. It shaped how I saw school and childhood as a system I had to escape. It’s in the anxiety I felt when building new teams, in the exhaustion from trying to read people, in the moments I disappeared into code because that’s where things are a bit calmer.

Being autistic doesn’t mean I have superpowers. It means I experience the world in a way that often clashes with how things are structured — socially, professionally, emotionally. It means I need clarity, consistency, and space to think. It means my habits and routines are essential anchors in a world that often feels unpredictable. It means I can hyperfocus for days, then burn out because I’ve gone too far without noticing. It means new situations, unexpected changes, and social interactions, even the ones that seem basic , can be deeply destabilising. It means I struggle with unspoken expectations, new situations, sensory issues, anxiety and group dynamics, but I thrive in being quiet and building things that solve problems.

What’s hard isn’t always visible. The way I process things, the importance I place on routine, structure, and calm doesn’t always translate. What seems small or trivial to someone else might be something I depend on to feel safe and steady. Masking, overthinking every conversation, trying to function in ways that don’t come naturally: those are quiet battles I fight almost every day. I’ve learned to work around them, sometimes through delegation, sometimes through brute force, and sometimes just by accepting that I’ll never be the most socially comfortable person in the room. But I’ve also learned to own the things that make me different; the obsession with doing things well, the need to question everything, the craving for clarity, efficiency, and usefulness.

It also means I need more rest than most people. Not because I’m lazy, or unmotivated, but because navigating the world, especially the social and chaotic parts of it, takes an enormous amount of energy. After a day of meetings, commutes, conversations, context-switching, or just being around people, I often feel completely drained. Sleep isn’t just rest for me, it’s recovery. I’ve learned to listen to that need rather than fight it. I sleep more than others, and I don’t feel too guilty about it anymore (still a little bit). It’s part of what allows me to function, to focus, to create. Without that space to recharge, I just can’t show up as myself.

Being autistic and a founder can be especially challenging. You're expected to do things you’re not naturally wired to do like talking to a lot of people, going to events, handling constant uncertainty, and showing up with a confident smile even when you’re falling apart inside. And when your sense of balance depends on having a predictable rhythm, that kind of chaos can be overwhelming. And people don’t always understand. You keep your head up, you keep adapting, and most of the time, it works. But it can also be lonely, even with your own associates.

That said, I’m fully aware you don’t have to be a founder to feel this way. Most jobs require you to act “normal,” to communicate smoothly, to perform socially, and that’s exhausting. Many workplaces (and society in general, even more so than the workplace itself) aren't designed for autistic people, and functioning in them every day takes an unseen toll.

I’ve been lucky. I’m mostly surrounded by people who understand me. They give me the space I need, they trust me, they know how I work. For that, I’m genuinely grateful.

I don’t know what comes next, but I know I’ll keep building. That’s the one thing that’s never changed.

And maybe that’s the most autistic part of me.