Neural Concept Raises $100M to Scale AI-Native Engineering for Product Teams
Neural Concept closed a $100 million Series C led by Growth Equity at Goldman Sachs Alternatives, with participation from Forestay Capital, Alven, HTGF, D.E. Shaw Ventures, and Aster Capital. If you run product development, this signals a clear shift: AI for design and simulation is moving from proof-of-concept to standard practice.
The company builds CAD-native enterprise AI that understands geometry, constraints, and design intent. Teams use it to deploy physics-aware design copilots, explore millions of options early, and cut late-stage changes that burn timelines and budgets.
Why this round matters
- Proof of demand: engineering orgs are scaling beyond pilots across automotive, aerospace and defense, energy, semiconductors, and consumer electronics.
- Clear use of funds: faster product development, global GTM expansion, and a deeper role as the intelligence layer tied into existing engineering systems.
- Roadmap: a generative CAD capability targeted for early 2026, pointing to AI that proposes manufacturable concepts with constraints baked in.
What the platform changes in your workflow
- Front-load exploration: screen a massive design space during early phases instead of firefighting during DV/PV or at release gates.
- Physics-aware guidance: copilots suggest geometry changes with direct links to performance targets and constraints.
- Shorter cycles: compress what used to take months into days, with fewer loops between CAD, CAE, and test.
- Better tradeoffs: optimize for efficiency, safety, and sustainability from the start, not retrofitted at the end.
Partnerships and stack fit
Neural Concept is integrating across the tools you already use, including Nvidia, Siemens, Ansys, Microsoft, and AWS. That matters for adoption: less custom plumbing, better model deployment, and smoother alignment with PLM, CAD, and simulation environments.
What leaders in product development should do now
- Pick 2-3 focused use cases with clear metrics (e.g., aero drag reduction, thermal performance, weight, cost). Define baselines and target deltas.
- Audit data readiness: CAD formats, CAE meshes, historical test data, and metadata quality. Close gaps in naming, versioning, and units.
- Plan the integration: where the copilot lives (CAD plug-in vs. web), connections to PLM, simulation queues, and approval workflows.
- Establish governance: model validation criteria, change control, and traceability from AI-suggested geometry to final drawings and BOM.
- Run a 90-day pilot: limited scope, weekly reviews, and a decision gate on go/no-go with quantified time and performance impact.
- Upskill your team: teach prompt patterns for geometry, constraint encoding, and CAE handoffs; create playbooks for repeatable studies.
What investors and the team are saying
"Neural Concept's technology represents a rare leap forward in enterprise engineering AI," said Lambert Diacono, Executive Director Growth Equity at Goldman Sachs Alternatives.
"As demand accelerates for AI that drives real impact in complex industrial workflows, Neural Concept is emerging as one of the leading companies in the market," added Christian Resch, Partner, Head of EMEA Growth Equity at Goldman Sachs Alternatives.
Dr. Pierre Baqué, CEO and founder of Neural Concept, said: "Advances in AI are transforming engineering from a process of trial and error into a data-driven workflow where tradeoffs and constraints can be understood and optimized from the start."
Company snapshot
- Founded in 2019 as an EPFL spin-out; platform embeds AI directly into design and simulation workflows.
- Serves global OEMs and tier suppliers across automotive, aerospace, energy, consumer electronics, semiconductors, and defense.
- Previous round: $27 million Series B in 2024.
If you're evaluating AI-native design tools
- Start where physics is well-characterized and test data is abundant.
- Prioritize interfaces that fit your CAD/CAE stack to reduce change management overhead.
- Measure impact on rework, ECO count, and test iteration time, not just top-line performance metrics.
For the full announcement and multimedia, see the source on Business Wire: Neural Concept Series C.
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