Introduction

Behind every data-driven solution is a deep understanding of both technology and the challenges that businesses face. At Agilytic, co-founder Christophe Robins draws on a rich background in applied mathematics and data integration. After completing his studies in applied math at UCL, he started at Deloitte, where he focused on CRM (Customer relationship management) implementations and discovered the power of linking a variety of data sources to enhance productivity and customer insights. Later, he joined a major telecom provider, digging into data mining to understand what drives retention, loyalty, and churn.

Seeing how lower barriers to entry could benefit companies beyond just large enterprises, Christophe and his colleague Julien founded Agilytic with a single guiding principle: solve real problems with pragmatic data solutions. They soon discovered that many business issues are best addressed by straightforward fixes—sometimes no advanced algorithm required—while others need the careful application of analytics or AI.

In our podcast, Christophe shares how Agilytic stays pragmatic in a rapidly evolving AI landscape, why focusing on adoption is just as important as building the right model, and what kinds of projects truly move the needle.

What sets Agilytic apart from other data consultancies?

Christophe: Our first step is always, “Okay, what problem are you trying to solve?” I see so many consultancies say, “You need a data platform—let’s start there.” We disagree. If the solution is simply automating two Excel files and joining the data, let’s do it. We’re pragmatic. Sometimes that means no overly complex algorithms—just the right fix for the problem.

We distance ourselves from the ‘hype’ that AI must be applied everywhere. You want to be sure it’s actually needed and adds value. Pragmatism and genuine problem-solving define Agilytic. If the simplest fix solves your headache, we’ll use it. Overcomplicated solutions can cause headaches down the road.

How do you keep up with rapid AI developments?

Things like ChatGPT, Deepseek, Llama, or other open-source models evolve frequently. We watch them closely but wait before diving in. We look for proven stability and real return on investment. The big trend I see is increasingly specialized, accessible tools for specific tasks—coding assistance, document classification, customer service, and so on.

There’s also a major shift in open-source. Models like Deepseek and Llama began a wave of open-source innovation. Now, organizations weigh using a paid service like ChatGPT or hosting their own model internally to avoid licensing fees. Yes, there’s a cost in infrastructure, but you can predict it better and keep your data more secure.

Agilytic’s “smart follower” mindset ensures the team adopts new tools once they prove their worth in real client scenarios.

Which major categories of projects does Agilytic handle?

We see three main pillars:

1. Commercial performance: Retention, loyalty, and marketing. We help businesses optimize how they keep and grow customers, combining internal data with public information for richer profiles.

2. Operational efficiency: Automating repetitive tasks, reducing human error, or speeding up processes. That might be checking invoices, handling complaints, or even optimizing logistics—like ordering tires under complex constraints.

3. Financial performance: Consolidating data for clearer reporting, reconciling different systems, or detecting fraud. We’ve built accelerators to spot document falsifications and flag suspicious transactions.

How do you ensure solutions are truly adopted?

A solution that’s never used is a waste of time and money. We involve the client’s teams from day one—IT, business stakeholders, end users. If it’s a reporting solution, we want to see how people will utilize it. If it’s a predictive model, we want to know who will act on its predictions.

Involving actual users early means they’re more likely to trust the final product. We also organize the deployment so that it meets the company’s IT standards. You can have a brilliant model on a data scientist’s laptop, but if you can’t integrate it into the daily workflow, it’s meaningless.

Agilytic follows its own ADOPT mindset to curb scope creep, nail down deployment, and keep stakeholders engaged from start to finish.

Any favorite success stories to share?

One that stands out is a tire-order optimization for a leasing company. Twice a year, they place huge tire orders involving multiple brand groups, different rebates, and constraints—way too complicated for anyone to solve manually. We built a model in three weeks, and it saved them €100,000 on the first run. It has since run twice a year for four or five years, consistently generating savings.

Another example: we assigned daily tasks for a back office of 600+ employees. In a few weeks, we had a proof of concept. Users gave feedback, we iterated, and it ended up saving the equivalent of two full-time positions in scheduling time alone. Plus, employees appreciated that we factored in their preferences. That’s a double win: cost savings and happier teams.

Both illustrate Agilytic’s focus on measurable improvements through targeted analytics and iterative development.

Any final takeaways for leaders exploring data projects?

First, don’t fall for the hype. AI doesn’t magically fix every issue. Start by listing the challenges you face. Which ones can you really solve using data? Is it customer complaints, a production bottleneck, or a recurring invoice headache?

Second, don’t go overboard on massive new systems. A small pilot can quickly show real value and return on investment. You learn what technology and people you need, then scale from there.

Third, get your project stakeholders involved. Data projects need buy-in and clarity from day one, or you risk building solutions no one adopts.

Listen or schedule a call

• Want to hear more? Listen to the full podcast episode and learn how Agilytic guides businesses from data to real impact.

• Ready to start your own data project? Schedule a call with us. We’ll talk about your challenges, figure out how data can help, and build something that truly delivers.