Sifflet, the Paris-and NYC-based data observability startup founded by Salma Bakouk, has raised $18 million in fresh funding to scale its platform globally. The round was led by EQT Ventures and Mangrove Capital, with participation from Capmont Technology. This capital will fuel Sifflet’s expansion into North America and accelerate the development of its AI-native observability agents and contextual data monitoring tools.

In the past year, the company has tripled its customer base and revenue, serving enterprise customers like Saint-Gobain, Penguin Random House, and leading European tech companies. Sifflet’s platform gives technical and business teams shared visibility into the health of their data - not just detecting anomalies, but diagnosing them, mapping their impact, and suggesting resolutions through intelligent agents.

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From Alerting to Understanding: Redefining Observability

Today’s AI-driven enterprises depend on complex data pipelines. When those pipelines fail silently - through schema drift, data gaps, or misconfigurations - the downstream consequences compound quickly. Sifflet was built to prevent those failures from becoming business risks.

Instead of focusing purely on detection, the team designed the platform to deliver contextual, explainable observability - combining metadata, lineage, monitoring coverage, and machine reasoning. Their AI agents - Sentinel, Sage, and Forge - proactively surface risks and recommend fixes, transforming alerts into actionable intelligence.

At Penguin Random House, data leader Pete Williams noted that Sifflet has become indispensable for their operations. He emphasized that Sifflet "has become an essential part of how we ensure data reliability across the business, not just within engineering,” and praised the platform’s usability, adding, “their AI‑native approach doesn’t just detect issues, it gives our team the context to act faster and smarter.”

But Sifflet’s biggest breakthrough isn’t in its alerting logic - it’s in its decision to build a product that sits at the intersection of technical depth and cross-functional clarity. Rather than building another engineering tool that teams have to interpret, Sifflet built something broader: an intelligent interface layer that turns complexity into clarity across the entire organization.

This is where founders in frontier markets should pay close attention. Sifflet didn’t win by optimizing for resolution time. They won by owning the interface between complexity and clarity. In fast-moving, fragmented environments, the product that succeeds isn’t the one that catches every error - it’s the one that helps users move confidently through uncertainty.

By building trust into the architecture of observability, Sifflet shifted from being just another monitoring solution to becoming a strategic layer. It positioned itself not as a gatekeeper of signals, but as the lens teams use to understand system behavior in real-time.

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Investor Confidence Rooted in Execution

The clarity of Sifflet’s strategy has earned it strong repeat backing. Carl Svantesson of EQT Ventures remarked that he’s seen Sifflet evolve from a promising vision into “a platform that’s setting a new standard in data observability.” He attributed that evolution not just to market need, but to execution and a clear understanding of the user.

Yannick Oswald of Mangrove Capital, who has now invested in the company across three rounds, pointed to a deep sense of belief in the founding team. “Backing a company once is a bet,” he said, “backing it three times is conviction.”

That conviction comes from Sifflet’s consistency - not only in growth, but in the way it has defined and led a category, rather than trying to fit into an existing one.

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Built for AI, Designed for the Enterprise

Sifflet’s UI and workflows are designed for real-world adoption. The platform auto-generates data lineage, maps monitor coverage, recommends fixes based on historical incidents, and surfaces the root causes of data failures. What makes it especially powerful is that it delivers these insights in a way that’s consumable by business analysts, product teams, and compliance leads - not just engineers.

This cross-functionality is what turns observability from a technical backend function into a business enabler. It makes trust in data something everyone in the org can align around.

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What’s Ahead: AI Agents, US Expansion, and Product Deepening

With this latest round, Sifflet will continue hiring across engineering, go-to-market, and customer success. The AI agent suite will move toward full release, with Sentinel enhancing monitor recommendations, Sage helping teams diagnose recurring issues based on memory of past incidents, and Forge offering automated or semi-automated resolutions.

CEO Salma Bakouk described the company’s next chapter as one focused on “embedding AI into every step of observability, so teams can move faster without sacrificing trust.”

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Why Sifflet’s Rise Signals a Larger Shift

As data becomes the foundation of every AI and analytics initiative, the silent risk of broken pipelines becomes existential. The companies moving fastest won’t be those who build the biggest LLMs or train the most models - they’ll be the ones who can see, understand, and trust their data in motion.

Sifflet is quietly becoming the visibility layer for that future. And in that shift, it’s showing founders everywhere that infrastructure doesn’t have to be loud to be transformative. It just has to be the thing that gives everyone else the confidence to move.