Today, CoLab revealed its vision for generative engineering, announcing the release of a new capability called generative artifacts and setting a date for the launch of a generative CAD product in H1 2027.

“There is a lot of excitement about generative CAD accelerating design iterations and allowing engineering teams to explore more concepts,” said Adam Keating, Co-founder and CEO of CoLab. “It’s an important piece of the puzzle, but if all you do is speed up CAD operations, you’re not really solving for program speed. The question engineering leaders should be asking is not ‘how can I adopt generative CAD?’ but ‘how can our organization shift toward generative engineering?’”

Generative artifacts, a new capability from CoLab, provides one example of what generative engineering could look like in practice. Beyond CAD, there are dozens of other artifacts that engineering teams must mature and approve for release. Using CoLab, engineering teams will be able to generate and refine these artifacts – for example, a Design Failure Mode and Effects Analysis (DFMEA) or a root cause analysis – retaining critical program context, alongside the history of each design.

CoLab also revealed plans to release a generative CAD product in H1 2027. With model capability advancing rapidly, CoLab is focused on providing guardrails that will help AI produce outputs engineers can trust.

“Our customers are designing complex products that must be manufactured at scale,” said Keating. “AI is getting better at generating geometry, so we’re focused on the last mile: fine-tuning the outputs using an organization’s historical designs, standards and guidelines, and expert knowhow.”

CoLab’s generative CAD offering will address two different needs. First, CoLab will offer the ability to generate new concepts, leveraging historical program data and integrations with leading CAD and topology optimization tools, including nTop. Second, CoLab is developing the ability to “AutoFix” a design, based on a specific annotation or requirement. What’s unique about CoLab’s approach is the emphasis on using engineering knowledge and documentation to justify each design decision.

“Now that AI can use CAD, engineering teams have to consider what that means for how they make design decisions,” said Jeremy Andrews, CTO and Co-founder of CoLab. “Your competitive advantage is based on the knowledge only you have. So AI should leverage internal data and standards, there should be a human in the loop, and design changes should be paired with rationale as part of an auditable design history.”

CoLab is uniquely suited to offer exactly this kind of AI context. Hundreds of engineering organizations already use CoLab’s Design Engagement System to run virtual design reviews, capturing expert feedback as they go. This design rationale, which is rarely documented otherwise, can help AI make better decisions, forming the backbone for reliable generative CAD.

Beyond that implicit knowledge, CoLab is investing in providing explicit guidelines for AI as well. Companies can upload their internal standards and guidelines to the platform, and CoLab will license trusted third party standards. Earlier this year, CoLab announced a licensing agreement for ISO standards, which allows AutoReview, CoLab’s AI Peer Checker, to review designs for compliance, providing citation-backed annotations.

The announcement comes as several major players, including PTC and Autodesk, recently announced Model Context Protocol (MCP) offerings, which allow AI agents and large language models to use some functionality within OnShape and Fusion. MCP gives agents a standard way to access CAD. According to CoLab, this is not only a sign that AI for CAD is accelerating, but also a building block for comprehensive AI for engineering:

“Soon, AI agents will be able to use any CAD tool,” said Andrews. “But what will ultimately accelerate program speed is when you can combine agentic CAD with the engineering knowledge, reviews, and validation data to mature designs. That’s where CoLab is going long term.”

About CoLab

CoLab builds AI-powered software for mechanical engineering and hardware development teams. Its EngineeringOS platform helps engineers make better, faster design decisions by connecting people, data, and AI in one collaborative workspace—capturing expert knowledge as a natural part of day-to-day work.

With AI agents built into the platform, CoLab enables teams to apply that knowledge automatically to improve design quality and accelerate product development. Founded in 2017, CoLab is trusted by leading global manufacturers to drive decision velocity and bring better products to market, faster.

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