Key Takeaways
- SPREAD AI, a Berlin based engineering intelligence startup, has raised USD 30 million in Series B funding to expand its industrial AI platform globally.
- The round was led by OTB Ventures, with participation from DTCP Growth, IQT, Salesforce, Thesiger Capital, angel investor Christian Schulz, and existing backers HV Capital and Nauta Capital.
- The company will invest the new capital in international expansion, advanced AI agents, and deeper product data capabilities for sectors such as automotive, aerospace and defense, and industrial equipment.
- SPREAD AI’s Product Twin technology already supports over 100 enterprise customers and is credited with up to 30% faster development cycles and 75% faster troubleshooting in complex engineering environments.
Quick Recap
SPREAD AI, the Berlin based AI powered engineering intelligence platform, has closed a USD 30 million Series B round to accelerate its global growth and deepen its industrial AI capabilities. The raise was first highlighted publicly via social channels, including The SaaS News, which cited the participation of both new and existing investors in the round. The funding builds on SPREAD AI’s earlier Series A and positions the company to scale its Product Twin platform across major manufacturing verticals worldwide.
Industrial AI Spine for Complex Engineering
SPREAD AI positions itself as an engineering intelligence layer that connects fragmented product data across PLM, CAD, ERP, ALM and other enterprise systems, turning them into a unified ontology called EIN which underpins its Product Twin models. By integrating structured and unstructured data across the full lifecycle, from early design decisions through production and field operations, the platform gives engineering and operations teams a single, queryable representation of complex products.
The USD 30 million Series B is led by OTB Ventures, joined by DTCP Growth, IQT, Salesforce, Thesiger Capital, and angel investor Christian Schulz alongside existing backers HV Capital and Nauta Capital, who also led the company’s USD 16 million Series A in 2023. Management plans to channel the capital into global go to market, broader coverage of industrial systems, and AI agents that can perform root cause analysis, requirements management and error inspection on top of the Product Twin, where customer deployments have reported up to 75% faster issue resolution.
Why Industrial AI Is Having a Moment?
The raise comes as Europe steps up its push for AI sovereignty and targeted investment into industrial and manufacturing AI, with similar rounds across the region’s industrial AI segment surpassing EUR 250 million in 2026 alone. SPREAD AI’s focus on regulated, capital intensive sectors such as automotive, aerospace and defense, and heavy industrial equipment aligns with the need for trusted, explainable AI systems that can work with decades of engineering data and long product lifecycles.
With reference customers that include manufacturers like BMW, Mercedes and Rheinmetall, SPREAD AI is using its Series B to cement a category it calls Engineering Intelligence, aiming to standardize how global manufacturers build Product Twins for their most complex platforms. At the same time, the company is deepening its partnership with Salesforce, combining its Product Twin and engineering context with Salesforce’s Customer 360 stack to better connect customer feedback with engineering and operational execution.
Competitive positioning in engineering intelligence
| Feature/Metric | SPREAD AI (News Subject) | Competitor A: Cognite Data Fusion* | Competitor B: Tulip Interfaces* |
| Core focus | Engineering intelligence and Product Twins for complex mechatronic products. | Industrial data operations and asset centric data fusion for heavy industry. | No code manufacturing apps for shop floor operations and data collection. |
| Context window (data scope) | Integrates 40+ PLM, CAD, ERP, ALM and other systems into a unified engineering model (EIN). | Aggregates time series, event and asset data across OT/IT systems into a unified industrial knowledge graph. | Captures real time production, quality and operator data from cells and lines, with lighter integration depth into legacy PLM. |
| Pricing per 1M “events” (indicative) | Enterprise contracts, pricing tied to number of models, systems integrated and engineering seats, not disclosed per token or event. | Tiered enterprise pricing based on data volumes, sites and assets managed, typically premium for multi site deployments. | SaaS pricing per site or user with lower entry point for mid market manufacturers. |
| Multimodal support | Handles structured BOMs, CAD metadata and unstructured documents such as requirements and field reports across the Product Twin. | Strong for time series, logs and asset hierarchies, with growing support for documents and images in industrial contexts. | Focused on forms, dashboards, images and operator inputs tied to shop floor workflows. |
| Agentic capabilities | Developing AI agents for requirements management, error inspection and root cause analysis on top of the Product Twin. | Provides AI powered recommendations and anomaly detection for maintenance and optimization, less focused on engineering change workflows. | Workflow bots that trigger alerts and guide operators, but limited deep engineering reasoning compared with Product Twin agents. |
| Target customers | Global OEMs and tier 1 suppliers in automotive, aerospace and defense, and industrial machinery. | Large energy, process and heavy industry operators (oil and gas, utilities, metals). | Mid market and large manufacturers seeking digital work instructions and MES light capabilities. |
From a strategic perspective, SPREAD AI appears strongest where deep engineering context and Product Twin driven reasoning are required, particularly in complex mechatronic products. Cognite style platforms remain attractive for large scale industrial data operations, while Tulip like systems look more cost effective for factories prioritizing quick deployment of operator workflows over rich engineering intelligence.
TechnoTrenz’s Takeaway
In my experience, a USD 30 million Series B at this stage is a clear signal that industrial AI is shifting from pilots to core infrastructure, and SPREAD AI sits in a sweet spot where engineering, data and operations converge. I think this is a big deal because manufacturers have spent decades building complex platforms and now urgently need AI native tooling that respects that legacy data while enabling faster, safer iteration on new products.
I generally prefer companies that define a clear category over those that chase generic AI narratives, and SPREAD AI’s explicit push around Engineering Intelligence and Product Twins fits that pattern. For TechnoTrenz readers, I would frame this as a bullish signal for industrial AI adoption and a reminder that some of the most durable AI value may come not from headline grabbing chatbots, but from quietly rebuilding the data backbone of how real world products are designed, built and maintained.
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