Every year, a handful of AI startups manage to cut through a market that’s already crowded with well-funded competitors. Leadbay is one of them.
Founded in 2023 by Ludovic Granger and Milan Stankovic, Leadbay builds AI systems that help B2B sales teams find and qualify small and mid-sized business leads that traditional prospecting tools tend to miss entirely — the plumbers, hairdressers, hotels, restaurants, and independent shops that don’t show up cleanly on LinkedIn, in job postings, or in the structured firmographic databases that platforms like Sales Navigator, Clay, or ZoomInfo rely on. Rather than aggregating more of the same data, Leadbay’s AI infers what a business does, who it serves, and how fast it’s growing from fragmented, incomplete signals — an approach the founders describe as thinking more like an experienced human salesperson than a database lookup.
That bet paid off. This year, Leadbay closed a $4.3M seed round. The company is putting the fresh capital toward growing its U.S. go-to-market team, deepening its inference research through a new partnership with Sorbonne University, and expanding engineering under co-founder and CTO Milan Stankovic.
Ludovic’s own path to Leadbay runs through both worlds the company is trying to connect: a family background in traditional business, and several years working as a tech investor before turning founder. That combination shows up throughout our conversation, in how he talks about product-market fit, about the discipline of charging customers early, and about the work of building a company that doesn’t depend on its founders to close every deal.
Below, Ludovic walks through what it took to get from an idea about “AI that works with limited data” to a funded, revenue-generating company, and what he’d tell other founders trying to do the same in the next 12 months.
The Origin Story
Every founder starts with a frustration they can’t ignore. What problem led you to build Leadbay, and what convinced you this was a problem worth dedicating years to solving?
I come from both the traditional business world, where my parents built their careers, and the tech world, where I spent three years working as an investor. I saw a clear gap between technology companies leveraging data and signals to make their sales teams more efficient, and traditional businesses that were still searching for and qualifying leads manually.
When AI emerged, I saw the opportunity to bridge that gap: making AI work with limited data and delivering it through an intuitive, easy-to-use interface so the rest of the market could benefit from the same capabilities as leading tech companies.
The Market Gap Traditional GTM Tools Miss
Some of your customers, including L’Oréal and Nespresso B2B, have said Leadbay helped them triple their addressable market and increase qualified leads by 10x. That’s a remarkable shift. What were these companies missing before, and why do traditional GTM strategies leave so much of the market untapped?
People targeting small and medium-sized businesses face a serious lack of data and buying signals to qualify a broad market of companies into high-quality leads. Today, they still rely on traditional, manual methods to collect, enrich, and qualify data before they can identify the companies most likely to buy.
Until now, no one has been able to solve this data problem. Milan and I came to the market with years of research led by Milan on how to make AI work with limited data. By combining reasoning, extrapolation, and the ability to infer missing data, patterns, and connections, we’ve built a system that thinks more like a human — identifying, qualifying, and enriching leads even when the underlying data is incomplete.
A Blue Ocean, Not a Red One
AI prospecting has become one of the most competitive categories in SaaS. What did you believe about the market that others either underestimated or overlooked?
Some people see AI prospecting as a highly competitive market. We—and our partner Garry Tan—see it very differently. We believe it’s a blue ocean opportunity.
Today, most companies are still solving the same problems using the same types of data signals and the same SaaS interfaces. That approach doesn’t reflect the reality that every company’s go-to-market strategy is different.
The opportunity we’re building is a system that can adapt to a wide range of workflows and go-to-market motions. Instead of forcing companies into a standardized process, our AI fits the way they already work, helping them scale more effectively and unlock better sales outcomes.
The Road to Product-Market Fit
Before raising your seed round, what were the most important milestones you deliberately focused on? If you were starting over today, would you prioritize the same metrics?
I think that in any company, you end up spending an enormous amount of time on operational, financial, and administrative tasks. Early on, I realized that you need to outsource or automate as much of that work as possible so you can focus on what truly matters: spending time with customers, understanding their needs, and building products they’re willing to pay for.
Every startup has moments when the original assumptions prove wrong. What’s one decision or hypothesis you had to abandon that ultimately made the company stronger?
We initially wanted to build software for people who didn’t have any software. But at one point, we understood that the customers most able to pay and grow with our product were already the most promising and fastest-growing companies in the market.
We realized we could offer them something valuable: helping companies targeting SMBs grow even faster by giving them better data, better signals, and a stronger way to identify the companies most likely to buy.
Founders often hear they need product-market fit before raising institutional capital. In reality, what signals convinced investors that you had reached that point?
Enterprise companies were already paying for the product and growing with the product over time. We achieved both the pilot and land phases with enterprise customers, as well as the expansion phase.
This is a strong signal of product-market fit because it shows that companies don’t just see initial value—they want to continue using the product and expand its adoption inside their organization.
Building for Today, Not for Some Future Market
As AI evolves at an incredible pace, how do you balance shipping quickly with building a product that will still be relevant several years from now?
We don’t build a product assuming it will still be relevant several years from now. Product-market fit is something you have today, but you cannot assume it will stay forever in a constantly changing market.
That’s why we continuously develop and evolve our platform to always make it better, stronger, and faster. The goal is to keep creating value as customer needs and markets evolve.
Congratulations on closing your $4.3M seed round. Looking back, what do you think investors were really betting on: the product, the market, your traction, or your team? Were there any assumptions they challenged during the process?
I think it’s always a mix. On our side, there’s obviously a strong emphasis on the team and the traction, while the market itself is very large and crowded. The people who bet on us are the ones who believe a new world is being built — and that this new world is fundamentally different from everything that’s currently on the market.
The Hardest Part of Scaling
Looking beyond fundraising, what has been the hardest challenge in scaling the company so far? Was it hiring, product direction, customer acquisition, or something less obvious?
The biggest challenge has been learning how to replicate successes and replicate them without the CEO.
In the beginning, founders are involved in everything: customers, sales, decisions, and execution. The challenge is building systems and teams that can reproduce those successes without depending on the founders.
Advice for the Next Wave of Founders
If you could leave founders building AI companies with one piece of advice (especially those hoping to raise their first institutional round in the next 12 months), what would it be?
Spend time with customers and charge them as much as possible. Spending time with customers is the best way to understand the real problems you need to solve, and charging them forces you to build something that creates real value. At the end of the day, the strongest validation is when customers are willing to pay for what you are building.
Key Takeaways For Founders
- “Competitive” doesn’t mean “solved.” Ludovic’s read on the crowded AI-prospecting space is that most players are still using the same data signals and the same interface conventions. Real differentiation came from rethinking the underlying approach (inference over aggregation), not from out-executing rivals on a shared playbook.
- Product-market fit is a moving target. Rather than treating PMF as a box to check before scaling, Ludovic treats it as a temporary state that has to be re-earned continuously as the market and the technology shift underneath the product.
- Revenue is the real filter. The willingness to pay is the clearest signal a company is solving something real. Time with customers and pricing discipline did more to prove the business than any pitch deck metric.
- Founder-dependence is the next problem after fundraising. Closing a round doesn’t solve the hardest organizational challenge, which is building systems and teams that can reproduce what the founders did instinctively, without the founders in the room.
