For a certain kind of European founder, the dream is a clean exit to a pharmaceutical giant. For Ana Maiques, the dream was to get one to run on her software. At her neurotech startup Neuroelectrics, she spent years convincing clinicians that brain stimulation could be therapeutic. Now at Aily Labs, she is convincing executives that the answer to a billion-dollar supply chain question might be waiting on an employee's phone.
Aily Labs sells an AI-powered decision intelligence platform. The pitch is simple, if ambitious: connect every data silo in a sprawling multinational, from ERP systems to factory sensors, and push personalized, prescriptive insights to the people who need them, in real time. The company calls it a mobile-first app, which is a polite way of saying it wants to kill the executive dashboard. Instead of a manager refreshing a BI tool, Aily's system, built on AWS, surfaces recommendations directly to a supply chain planner or a finance analyst.
The bet on the factory floor
The company's foundational case study is Sanofi. The French pharmaceutical giant uses Aily's platform, branded internally as 'Plai', to aggregate over one billion data points across its R&D and supply chain operations [sanofi.com, 2026]. The goal is to predict everything from clinical trial enrollment timelines to the probability of success for a given drug program. For a company that moves physical goods and biological materials across the globe, shaving weeks off a timeline or identifying a bottleneck before it happens translates directly to millions in value.
Aily's approach is modular. Instead of a monolithic platform, it offers specialized apps,.fin for finance,.r&d for research,.supply for supply chain, and so on [Perplexity Sonar Pro Brief]. This lets functional leaders buy in piecemeal, a classic wedge strategy for enterprise sales. The promise is a 99% predictive accuracy from its library of over 300 models [ailylabs.com].
A founder built for regulated scale
CEO Ana Maiques cut her teeth in one of the most challenging arenas for a startup: regulated medical devices. As the former CEO of Neuroelectrics, she navigated clinical trials and FDA pathways for non-invasive brain stimulation technology [Perplexity Sonar Pro Brief]. That experience is a relevant credential for selling to the Sanofis of the world, where sales cycles are long, compliance is non-negotiable, and the cost of being wrong is catastrophic. Her profile extends beyond the company; she is president of EsTech, a lobby group for Spanish scale-ups, and a permanent member of the European Innovation Council Advisory Board [techcrunch.com, 2022], [imaginationinaction.co].
The leadership team has been bolstered by heavyweight appointments to its advisory board, including former Novartis CEO Joseph Jimenez and former Eli Lilly CFO Derica Rice [PR Newswire, 2026]. The company has also expanded its footprint with a New York office and key commercial hires, indicating a push into the crucial North American market [PR Newswire, April 2024].
The $80 million question
In May 2025, Aily Labs raised an $80 million growth round led by Insight Partners, with participation from Eurazeo and others [PR Newswire, May 2025]. The round was later characterized as a Series B led by FPV Ventures [techfundingnews.com, 2026].
| Round | Amount | Lead Investor | Year |
|---|---|---|---|
| Series B / Growth | $80 million | Insight Partners / FPV Ventures | 2025 |
The competitive moat is not the AI models themselves, but the deployment. Anyone can train a model on historical supply chain data. The hard part is integrating it with a legacy SAP instance, getting clean real-time data from a hundred global factories, serving a secure, actionable insight to a manager's phone, and having that manager trust it enough to act. Aily's bet is that its mobile-first, role-based packaging and its deep AWS integration create a deployment advantage that pure-play AI model shops or legacy business intelligence vendors cannot easily match.
Where the wheels could come off
For all its ambition, Aily Labs faces a landscape littered with the wreckage of enterprise 'AI platforms' that promised to be the single pane of glass. The risks are not hypothetical.
- Implementation gravity. The value proposition hinges on connecting disparate data sources. In a global enterprise, that can be a multi-year, eight-figure systems integration project.
- The dashboard is a habit. Convincing employees to change daily workflows from a familiar dashboard to a push-notification-driven app is a change management challenge as much as a technical one.
- The incumbent response. Players like SAP, Oracle, and Salesforce have entire divisions dedicated to embedding AI into their existing platforms.
The company's answer, implied in its Sanofi case study, is to start with a high-value, contained use case, like predicting R&D costs, and demonstrate such clear ROI that expansion becomes inevitable.
The next twelve months
The fresh capital suggests a phase of aggressive expansion. The hiring of a senior customer success manager for pharma and finance in New York is a concrete signal [jobs.insightpartners.com]. The watchpoint is whether Aily can replicate the Sanofi blueprint in another Fortune 500 vertical, like automotive or chemicals, and do so within a typical enterprise sales cycle. Another milestone will be moving beyond predictive insights to demonstrating closed-loop automation, where the system doesn't just recommend an action but executes it, with human oversight.