The Missing Layer: Why Agentic AI for Engineering Is the Next Great Enterprise Software Opportunity
There is a quiet irony at the heart of modern industry where we find the companies that design the world's most advanced products.
They might be conjuring up next-generation electric vehicles, hydrogen-powered aircraft, or wearable consumer electronics. However, they still run their engineering processes on a patchwork of disconnected tools, manual handoffs, and institutional knowledge.
Over fifty percent of R&D engineering time is consumed not by design or innovation, but by coordination: moving files between software systems, reformatting data, chasing approvals, and re-running calculations that someone else already completed in a different department.
This is a structural problem worth an estimated €500 billion globally. And it is the last major enterprise function that software has failed to automate. Finance has its ERPs. Sales has its CRMs. Marketing has its automation stacks. But engineering still runs on siloed tools, sometimes legacy software solutions, brittle scripts, and spreadsheets passed between specialists who guard their methodologies like trade secrets.
With our investment in Synera, Revaia’s first in Germany, we are betting this is about to change.
Synera has developed the first agentic AI orchestration platform purpose-built for engineering. We believe that the company best positioned to lead that change is not a Silicon Valley AI startup, but a team of engineers in Bremen, a historic port city in Northern Germany known as one of the leading locations in the international aerospace industry, who have spent seven years solving the hardest integration problems in industrial software.
Why Engineering Has Resisted Automation
To understand why Synera matters, you first have to understand why the engineering software stack is so stubbornly resistant to the kind of automation that has transformed every other enterprise function.
The computer-aided engineering (CAE) market is estimated at roughly $8–10 billion and is growing at approximately 10% annually (source: Dedale Intelligence). But it is dominated by a handful of large, well-known international players: Ansys, Dassault Systèmes, Siemens, and a long tail of specialized point solutions. These tools are powerful, mission-critical, and deeply embedded in the workflows of every major manufacturer. They are also, for the most part, twenty to thirty years old, file-based, lacking modern APIs, and locked inside vendor-specific ecosystems that prevent interoperability.
The result is an extraordinary landscape of fragmentation. A typical automotive original equipment manufacturer (OEM) might use Dassault's CATIA for computer-aided design matters (CAD), Ansys for structural simulation, a different solver for crash analysis, yet another tool for computational fluid dynamics (CFD), and a proprietary spreadsheet for cost engineering. None of these tools talk to each other natively.
When a design changes in one system, an engineer has to manually export, reformat, and reimport data into the next system. Multiply that by dozens of departments and thousands of design iterations, all within large international industrial groups with numerous subsidiaries, and you begin to see the scale of the coordination problem.
This fragmentation is a structural feature of the market. The major CAE vendors compete on depth and accuracy within their specific physics domains, and their primary growth strategy is mergers and acquisitions (M&A). They acquire niche players to expand their customer base and cross-sell.
But this consolidation has not produced integration. Each acquired tool retains its own architecture, data formats, and interface. The result is that the bigger the platform vendor gets, the more heterogeneous its internal stack becomes. No single vendor has been able or willing to build a true cross-vendor orchestration layer.
Finally, add to this the growing demand for speed and velocity in the design, engineering, and development of new products, in a world where intense competitive dynamics are emerging from any corner of the globe, and where today's industrial leaders find themselves challenged by more agile newcomers, and in some industries ever-more complex products.
Enter the Orchestration Layer
This is the gap Synera was built to fill. Founded in 2018, the company started not with AI but with a deceptively simple insight: if you could connect all of these disconnected engineering tools into a single workflow, then you could fundamentally change how engineering teams work. This is the kind of vision that keeps the focus on solving a problem in a way that could create real value for a customer, rather than just playing with a buzzy new tool and hoping to find a good use for it.
The platform's core is a visual, easy-to-use workflow editor that allows engineers to digitally model and automate complex processes by connecting functional building blocks — "nodes" — that represent operations like geometry preparation, meshing, simulation runs, optimization, or result documentation. Each workflow is deterministic and transparent: every input, processing step, and output is visible, inspectable, and reproducible. This matters enormously in industries like aerospace and automotive, where traceability and auditability are regulatory requirements.
On top of this workflow layer, Synera has built an agentic AI orchestration system designed specifically for engineering tasks. Unlike generic AI copilots that suggest text or generate code, offering up probabilistic answers, Synera's agents are designed from the ground up to execute real engineering work, turning expert workflows into scalable, autonomous “digital engineering colleagues.”
By digitizing the “how” behind engineering, Synera enables enterprises to build, orchestrate and scale multiple specialized agents that collaborate like engineering teams actually work in practice, while maintaining precision and security. In other words, a simulation agent, a costing agent, and a design agent can be deployed, and Synera can orchestrate how they work together, including the workflows and the tools that they call.
The results are not theoretical. At BMW, Synera has compressed scenario planning from three weeks to two minutes. Airbus has reduced the time required to create a request-for-quote from 50 hours to 7 minutes. Other customers report similar time savings. These are not benchmarks from a controlled demo.
Why Incumbents Cannot Build This
The natural question is: why can't Siemens, Dassault, or Ansys simply add an orchestration layer to their own platforms?
The answer lies in the market's structural dynamics. Enterprise engineering customers do not want a single-vendor stack. They want best-in-class tools for each domain. They want the best crash solver, the best CFD engine, and the best topology optimizer. And they want those tools to work together.
A vendor-neutral orchestration layer that sits above the existing toolchain, connecting everything without requiring customers to replace anything, is precisely what incumbents cannot offer because each has a commercial interest in keeping customers within its own ecosystem.
As a result, the company rarely competes directly with another orchestration platform. The real competition is the status quo: the manual processes, brittle scripts, and undocumented institutional knowledge that constitute the "hidden factory" within every engineering organization.
The Agentic Pivot
The generative and agentic AI explosion has created so much advancement in such a compressed timeline that it can be hard to forget just how recently certain needs, such as orchestration, have emerged.
For the first several years of its existence, Synera focused on workflow automation for engineering teams. During that time, the company built a robust platform, developed deep integrations across the engineering software stack, and established relationships with leading industrial customers.
Then, in late 2024, something shifted. Anthropic, the company behind Claude, released the Model Context Protocol (MCP), an open-source tool that would enable increasingly smart, somewhat autonomous AIs (agents) to connect with one another. About a year ago, C-level executives at major industrial companies began issuing board-level mandates to explore and adopt agentic AI to understand how to push the boundaries of automation across their companies.
The buying motion flipped from bottom-up evangelization to top-down urgency. Suddenly, the same sales team with the same product began closing significantly larger deals.
Synera's annual recurring revenue doubled in 2025, with 60% of new business coming from agentic deals. The company now serves over fifty enterprise clients across fifteen countries, including BMW, Airbus, Hyundai, Volkswagen, NASA, and L'Oréal. That’s a customer roster that would be remarkable for a company ten times its size.
What makes this momentum particularly compelling is that Synera is not surfing a hype cycle with a hastily assembled AI wrapper. The agentic layer sits atop seven years of deep integration work and a proven workflow automation engine, while delivering tangible results for clients in terms of industrial efficiency and return on investment.
The AI agents are not generating suggestions in a chat window. They are executing deterministic, auditable engineering computations across real production systems. For industries where a simulation error can mean a failed crash test or a grounded aircraft, this distinction between "AI that suggests" and "AI that executes with traceability" is existential for extending autonomous features with greater confidence. Deployed on-premises, it ensures sensitive engineering IP remains secure and never exposed to external AI models.
A European Platform for a Global Opportunity
Our investment in Synera represents a conviction that engineering R&D automation is one of the most significant enterprise software opportunities of the next decade, and that the company best positioned to capture it is one that combines deep domain expertise, a vendor-neutral architecture, and a European DNA that is increasingly a commercial asset.
Defense and aerospace are two of Synera's fastest-growing verticals. In these sectors, European ownership and on-premise deployment are procurement requirements. Synera's architecture, in which customer data never leaves the customer's infrastructure and LLM selection is controlled by the customer's IT department, is precisely the deployment model favored by European regulators and defense procurement. This is a structural advantage that no cloud-based competitor can easily replicate. But automotive also remains a significant growth lever, in an environment where European OEMs are being disrupted by international competitors and must gain in speed and efficiency.
At the same time, the opportunity is global. The engineering coordination problem is identical whether you are building cars in Munich, aircraft in Toulouse, telescopes in Greenbelt, Maryland, or shoes in Seoul. Synera's expansion into the United States and Asia Pacific, led by a newly hired Chief Revenue Officer with twenty-five years of experience at the four largest players in the CAE industry, represents the next phase of a company that has proven its value proposition in the most demanding engineering environments in Europe and is now ready to scale internationally.
Beyond capital, our role as an investor is to help Synera accelerate the commercial expansion that its technology has earned. As a European growth equity firm with deep networks across Germany, France, and the UK, we bring direct connections to the large industrial companies that are Synera's core customers, particularly in France.
Equally important is the operational support that a high-growth company at this stage needs: helping to recruit and structure the sales organization as the team scales rapidly across new geographies, and sharing the hard-won lessons of helping European software companies build a credible presence in the United States.
For us, the investment thesis is clear. In a market drowning in AI companies that promise to transform everything, Synera is one of the rare platforms where autonomous agents are already running in production at blue-chip accounts. These are not demos, not proofs of concept, but real workflows replacing weeks of engineering work with minutes of automated execution.
That is the kind of durable, measurable value creation that defines a category leader. It is exactly the kind of opportunity we built Revaia to pursue.