In the fast-evolving manufacturing and product development landscape, engineering teams face mounting pressure to accelerate design cycles, reduce errors, and innovate under tight deadlines. CoLab, founded in 2017, is at the forefront of meeting these demands with its AI-powered Design Engagement System (DES). Fortune 500 companies already use the platform to streamline design feedback, speed up processes, and minimise costly delays. Yet, what is behind this transformation is more than the platform itself; it is the application of AI and machine learning to reframe the design process, creating value where traditional methods often fall short.
CoLab’s co-founder and CEO Adam Keating says that his team started with one clear mission: “We wanted to make collaboration in engineering as seamless as possible. We saw so much valuable feedback buried in emails, meetings, or across disparate systems, and the inefficiency was enormous. By centralising design feedback, CoLab’s DES has created a more streamlined process, and with the addition of AI, the system is redefining how design engineering operates across industries.”
Streamlining design review
Design feedback in any large manufacturing organisation often involves numerous stakeholders, leading to fragmented and time-consuming review processes. Traditional methods rely on emails, presentation decks, and in-person meetings, frequently lacking the continuity to ensure feedback is actionable and accessible.
“About 43 per cent of feedback is lost or ignored, and nearly 90 per cent of design decisions take over an hour to trace back to their sources,” explains Keating. “This isn’t just inconvenient; it directly impacts product timelines and quality.” CoLab’s DES tackles this by consolidating feedback into one accessible system, linking it to the specific part of the design model it relates to. This shift has been pivotal for companies like Johnson Controls, which has adopted CoLab’s DES. Keating notes, “In the past, Value Analysis/Value Engineering (VA/VE) sessions required engineers to fly across the world, which was costly and time-consuming. Now, they can collaborate virtually, with feedback that’s in context, ready to be actioned.”
The platform’s AI capabilities automate much of the repetitive work, allowing engineers to focus on complex problems rather than routine checks. “Routine design checks can eat up 50 per cent of an engineer’s time,” says Keating. With AI, we’ve automated these checks so that engineers can skip over routine verifications and focus on refining the design itself.”
Efficiency gains with applied AI
Beyond streamlining feedback, CoLab’s DES provides applied AI tools that allow engineering teams to work faster. One example Keating highlights is Mainspring Energy, which designs linear generators involving thousands of parts. Previously, Mainspring’s design reviews were slowed down by communicating feedback in multiple formats, from slides to weekly meetings. “By moving to CoLab, they cut review times by 27 per cent, halved the design cycle for one product enclosure, and saved 50 per cent in bill of materials (BOM) costs,” Keating states. “It’s proof that centralising feedback and automating standard checks allows companies to innovate faster.”
CoLab is exploring how to use AI to introduce valuable downstream data, such as warranty claims or scrap rates, to inform engineers during design. This AI-enabled feedback loop will give engineers insights from previous projects and allow them to avoid common mistakes. “Imagine an engineer working on a new component who can see feedback from a similar part that faced issues months earlier,” says Keating. By having this data available in real-time, engineers can take proactive steps to avoid costly mistakes down the line.”
Speeding up design with generative AI
As AI advances, CoLab is also investing in generative AI tools designed to provide engineers with immediate alternatives. This new development, set to roll out in 2025-26, uses historical project data to offer design variations that engineers can review and refine. “Generative AI is incredibly valuable for engineering teams,” Keating explains. “It’s about acceleration. Imagine entering a design review and seeing three or four configurations that the AI has generated based on what worked in the past. It’s not doing the job for you but giving you a foundation to build on.”
One of CoLab’s upcoming generative AI tools will automatically highlight design similarities, surfacing insights from past reviews. This is especially useful in industries like automotive manufacturing, where components often share similarities across different models or product lines. “When engineers work on similar parts across projects, they risk repeating the same mistakes without realising it. AI will help them see those patterns and avoid that repetition,” says Keating.
Keating emphasises that these tools do not replace the human expertise required in design engineering but provide engineers with intelligent recommendations. “Our goal is to give engineers a head start on their projects so they can apply their skills to the high-impact parts of the design,” he adds.
Achieving accuracy with Review AI
One of CoLab’s AI tools, set to be a cornerstone of the solution, is Review AI due to launch next year. It will surface past quality and product performance data to engineering teams as they design the next iteration of the product. “In manufacturing, there is often a disconnect between the people who design the product and those who handle warranty claims or assess failure rates,” says Keating. “Review AI closes that gap by putting that performance data in front of the designers.”
This feature allows engineers to make informed decisions with a broader view of the design’s impact across the product lifecycle. Keating believes this capability is especially valuable as it shifts the design process from reactive to proactive. “An engineer in Europe can avoid a mistake that a team in the U.S. encountered six months ago because Review AI flags it immediately. That’s how we’re reducing risk and, ultimately, cutting costs,” he explains.
The platform’s ability to integrate feedback and downstream data means teams can reduce issues that would otherwise appear later in production. “A single piece of early feedback can prevent a multimillion-dollar problem,” Keating notes. “These aren’t small tweaks; these are strategic changes that save companies time and money on a massive scale.”
Supporting diverse systems
Compatibility is essential for CoLab, especially as many of its clients operate in multi-platform environments where different departments use various CAD or PLM systems. “Our clients often work with multiple CAD platforms, leading to compatibility issues that create delays,” says Keating. “CoLab’s platform is designed to integrate with a broad range of CAD and PLM systems, meaning engineers can merge models from different systems without the usual friction. This cross-platform functionality enables organisations to build a centralised design workflow, regardless of the tools their teams use.”
Security is another priority, especially in industries with strict regulations around intellectual property. CoLab offers an encrypted, secure space for collaboration, which is crucial for clients like Ford Pro, where designs must remain confidential across multiple suppliers and regional teams. “Security and confidentiality are built into the platform because we know how critical IP is for our clients,” Keating notes. “By reducing risks associated with sharing files across systems, CoLab has helped clients reduce project timelines by as much as 30%, a benefit that speaks directly to operational efficiency and trust.”
Creating a collaborative culture
CoLab’s DES does not just optimise workflows; it encourages a culture of collaboration within engineering teams. “Traditionally, design reviews are isolated events, and often, the feedback from one stage doesn’t effectively carry over to the next,” Keating explains. We’re creating an environment where sharing feedback isn’t just an option; it’s an expectation. Engineers want to collaborate openly, knowing their insights will be accessible to anyone who might benefit.”
This collaborative approach has gained traction as more experienced engineers retire and companies face a knowledge gap. “We’re seeing valuable expertise leave the workforce,” says Keating. “By integrating GenAI, we can retain that knowledge and make it accessible for the next generation. It’s not just about saving time; it’s about preserving the expertise built over decades.”
CoLab’s system also encourages knowledge retention, transforming insights from experienced engineers into a lasting resource. “GenAI helps us retain institutional knowledge in a way that’s easy to access,” says Keating. “That knowledge becomes an asset for the company, rather than leaving with the individual.”
The future of AI in design engineering
Keating is optimistic about AI’s role in future design engineering, particularly in areas where routine tasks can be automated and data can provide actionable insights. “AI isn’t replacing engineers; it’s enhancing their work, letting them focus on the elements of design that require creativity and problem-solving,” he says. “CoLab’s ongoing work to incorporate more downstream data into the design process, including data from testing, warranty claims, and production, signals a shift towards a more holistic, data-driven approach.”
CoLab’s DES offers a compelling solution for companies grappling with digital transformation that combines the best of AI and human expertise. “The future of design is about data, connectivity, and collaboration,” Keating concludes. “By integrating generative AI, we’re helping teams leverage their data fully, enabling new levels of innovation.”
As manufacturing continues to evolve, platforms like CoLab’s DES illustrate how AI can provide a significant edge, offering executives the tools to increase productivity, reduce costs, and foster a collaborative culture of knowledge-sharing that will drive innovation for years.
