Synera raises $40M to embed agentic AI into NASA and BMW engineering processes

German startup Synera raised $40 million in a Series B round to embed agentic AI not in flashy demos, but in real engineering processes of large industrial companies — where they calculate loads, run simulations, coordinate designs, and respond to technical requests. The Bremen-based company builds a platform for industrial engineering that doesn't attempt to replace existing software but rather connects it into a single orchestration layer. Synera claims its system already works with more than 75 engineering tools, including solutions from Altair, Autodesk, Hexagon, PTC, and Siemens.

On top of this integration, teams of AI agents are deployed, capable of autonomously executing entire task chains: from design and simulation to optimization, cost calculation, and report generation. This is a key moment for the industry: companies aren't being asked to scrap their current infrastructure for AI, they're being offered a way to embed AI directly into already existing processes. Against this backdrop, it's particularly notable that Synera's active implementations include NASA, BMW, Airbus, Volvo Trucks, and Hyundai.

The round was led by Revaia fund, with Capgemini entering through its ISAI Cap Venture. All Series A investors also returned: UVC Partners, BMW iVentures, Cherry Ventures, Venture Stars, and Spark Capital. For Synera, this is not just capital but also a signal of confidence from funds that have already seen the company in action.

The new capital will go toward accelerating growth in the US and international markets. The company already has a presence in Boston, and Capgemini's participation looks strategically important also because it is one of the world's largest providers of IT and engineering services to the automotive and aerospace sectors. After this round, Synera's total raised capital has grown to approximately $58 million.

Synera bets on agentic AI as an autonomous execution layer within engineering workflows. According to the company's description, such agents can run iterative simulations, gather reports, respond to RFQs, and conduct tasks through approval stages without constant manual involvement at each step. The deployment model is also important: the platform runs on-premises, so engineering intellectual property and sensitive data remain within the client's infrastructure.

For companies working with CAD models, simulation results, component requirements, and manufacturing constraints, this is often a mandatory requirement. In practice, Synera is trying to solve not the problem of a convenient chat for an engineer, but the problem of AI access to those systems where real engineering work is already happening. So far, the story looks stronger than a typical AI pitch due to concrete results.

According to company data and validation by Frost & Sullivan, in the engineering group EDAG, using the platform reduced time spent on finite element modeling by 95%. At BMW's Additive Manufacturing Campus, with Synera's help, the mass of a 3D-printed robotic gripper structure was reduced by 30%. Another illustrative example is NASA, where several agents translate requirements into verified component designs and perform hundreds of design iterations in a single hour.

All this unfolds against a broader market backdrop: according to Gartner, manufacturing companies plan to increase investments in generative AI, but only a portion of prototypes reach production. Synera builds its thesis on the idea that the main problem in the industry is not the quality of model responses, but weak integration of AI with existing engineering infrastructure. For the market, this is an important marker: the next stage of agentic AI deployment in industry will depend not on grand promises, but on the ability of companies to embed agents into complex, error-sensitive processes with strict data security requirements.

If Synera can scale its experience with clients at the level of NASA, BMW, and Airbus, it has a chance to become one of the notable players that turn agentic AI from an experimental overlay into a normal working layer of engineering infrastructure.