OpsMill raises $14m Series A to make IT infrastructure data trustworthy



IRIS led the Paris- and London-based startup’s round, with BGV joining alongside existing investors Serena and Partech. The company’s Infrahub platform is in production at TikTok and at one European cloud provider that says it has cut deployment times from five days to fifteen minutes.


OpsMill, the Paris-headquartered infrastructure data management company, has raised $14m in a Series A round led by IRIS, with participation from BGV and existing investors Serena and Partech.

The company says the funding will go to growing its engineering and product teams and continuing to develop its Infrahub platform, which is designed to give AI agents and engineering teams a single trusted view of an enterprise’s IT infrastructure.

The pitch addresses a problem most enterprises have lived with quietly for years. While automation has spread through applications and workflows, the data describing the underlying infrastructure, physical hardware, virtual machines, cloud resources, and the connections between them, has remained scattered across spreadsheets, configuration management databases, and ad-hoc scripts.

None of those sources was designed to feed AI agents reliable information. When agents act on incomplete or inaccurate infrastructure data, the company says, the result is errors that can cascade through production systems quickly.

OpsMill cites two figures to size the problem. Gartner forecasts that 30 per cent of enterprises will automate more than half of their network activities by 2026, up from under 10 per cent in mid-2023. Per the ITIC 2024 Hourly Cost of Downtime Report, the average enterprise loses around $300,000 for every hour of downtime, before reputational costs are counted.

Infrahub, OpsMill’s flagship product, takes a different approach to representing infrastructure data than the table-based asset registers most enterprises use. The platform is built on a graph database that maps the connections between hundreds of thousands of infrastructure elements, including the metadata describing how each element should be configured.

Every proposed change is validated and approved through a DevOps-style review process before deployment. Co-founder and CEO Damien Garros, who built and scaled Infrahub alongside co-founder and COO Karen Gallantry, spent two decades on the operator side of the same problem at Juniper, Roblox, and Network to Code before starting OpsMill.

Garros put the case for the company in his own framing. “Automation is ultimately a data problem and if you only have a partial view of your network, you’re flying blind,” he said in the announcement.

“Writing the code for automating infrastructure was never the problem; the challenge has always been maintaining it and being able to trust it in production. We built Infrahub so that infrastructure teams, and the AI agents working alongside them, always have a complete, trusted record of what exists, what’s supposed to exist, and a way to safely change and evolve at scale.”

Infrahub is available in two editions, free open-source Community and licensed Enterprise. OpsMill compares the model to GitLab’s, with users able to develop on the open-source version and graduate to the Enterprise edition when they need governance and compliance features at scale.

The open-source community already includes hyperscalers including TikTok, alongside global retailers, fintechs, insurers, and manufacturers using the Enterprise edition in Europe and North America.

Eurofiber, a European cloud-services provider named in the announcement as an Enterprise customer, has cut its service deployment times from five days to fifteen minutes since deploying Infrahub. That figure is the most concrete customer-side data point in the company’s release.

Julien-David Nitlech, managing partner at IRIS, framed his fund’s investment in similar terms in the announcement.

“The race to adopt AI in enterprise infrastructure is real, but most organisations are trying to build on foundations that were never designed for it,” he said.

“OpsMill is solving the problem that everyone else is working around: without clean, structured, trustworthy infrastructure data, AI-driven operations simply cannot function at scale.”

OpsMill, headquartered in Paris with a London office, says the Series A will fund engineering and product expansion alongside continued development of data-centric AIOps capabilities.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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