Launchpad Build AI launches language model for manufacturing and aims to accelerate industrial automation
Artificial intelligence is making a serious push onto the factory floor, and one company has decided to go all in on this transformation.
Launchpad Build AI just announced a wave of news all at once: the launch of its own language model built for manufacturing, a full rebrand, the opening of a U.S. headquarters, and the addition of new technical leaders to the team.
At the center of it all is the Manufacturing Language Model, or MLM — a technology developed specifically to speed up industrial automation projects without requiring massive investments or teams of specialists.
The idea is pretty straightforward: any factory, regardless of size, could use a photo, a video, or a CAD file to kick off an automation process.
Sounds ambitious? It is, but the numbers the company is putting forward back up the concept, and the broader market context makes it clear why this kind of solution makes sense right now.
What the Manufacturing Language Model is and why it matters
The MLM is not just another generic language model hastily tweaked to talk about factories. According to Launchpad Build AI, it was built with an exclusive focus on manufacturing, which means the technical vocabulary, industrial workflows, and the specific quirks of different types of production lines are baked into the core of the system. That changes the conversation significantly when you compare it to solutions that try to retrofit general-purpose models for highly specialized contexts, because the depth of understanding around physical processes, equipment, and operational constraints is on a completely different level.
Jon Quick, CEO of Launchpad Build AI, explained the reasoning behind the approach to The Robot Report. Instead of trying to build everything from scratch or scraping the internet for generic data about millions of screwdrivers, robotic grippers, and other components, the MLM organizes information that already exists and has been validated — tested tolerances, ideal operating conditions, data from real production environments — and packages it in a way that delivers the biggest possible impact when designing an automation solution.
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In practice, what the company is proposing is that an operator or engineer could, for example, take a photo of an existing workstation and use that image as a starting point for the MLM to suggest or even structure an automation project for that specific environment. CAD files, production line videos, and other visual or technical inputs also serve as data for the system to process and return actionable recommendations. This drastically cuts the time normally spent on manual assessments, lengthy consulting engagements, and prototyping cycles that can drag on for months in a traditional deployment.
The most direct impact of all this is on industrial automation accessibility. Historically, automating a production line required a significant investment not just in equipment, but also in highly skilled professionals to plan, program, and implement every step. With a specialized language model acting as an intermediate intelligence layer, the trend is that mid-size and smaller companies can get into the game in a much more realistic way, without having to build out a full technical team just to get started.
The company stated that the MLM is the first model of its kind built specifically for industrial automation design, trained on data from real production environments. This democratization of access to automation is arguably the most significant takeaway from the entire proposition.
A track record that predates the AI hype
Launchpad Build AI did not pop up overnight. Founded in 2020 originally under the name Launchpad, in Edinburgh, Scotland, the company has been developing artificial intelligence systems to accelerate the design and delivery of robotics and automation solutions by up to 50%. The recent rebrand to Launchpad Build AI reflects the evolution of the company’s focus, which now wants to make it crystal clear that using AI to transform manufacturing is the heart of the business — making systems faster to design, more flexible to deploy, and more resilient in operation.
That positioning got a financial boost last year when the company raised $11 million in a Series A round. Investors include heavyweights like Lavrock Ventures, Squadra Ventures, Lockheed Martin Ventures, Scottish National Investment Bank, PXN Group, CX2, and Ericsson Ventures. When you see the investment arms of Lockheed Martin and Ericsson at the table, it is pretty clear the company’s pitch caught the attention of people who understand manufacturing, defense, and cutting-edge infrastructure.
The company already has active deployments in the United States and Europe, serving clients in the manufacturing and defense sectors. Its gantry-based system — an overhead robotic structure that moves along multiple axes — was designed to handle a wide variety of assembly tasks. On top of that, the company has Digitool, a robotic self-programming system that uses real-time computer vision to handle part and process variations without needing manual reprogramming every time something changes.
Robotics and AI: a combination that is reshaping industry 🤖
Robotics was already evolving at a rapid pace before artificial intelligence took on the spotlight it has today, but the combination of the two technologies has created a qualitative leap that goes far beyond the sum of their parts. Traditional industrial robots are programmed to execute repetitive tasks with precision, but any variation in the environment or the task requires manual reprogramming, which seriously limits flexibility. When you put artificial intelligence in the mix, those systems start learning from their environment, adapting behaviors, and making decisions in real time, turning a static mechanical arm into a dynamic collaborator on the production line.
This integration of robotics and AI is being driven by exactly the kind of technology Launchpad Build AI is developing. A language model specialized in manufacturing can function as the brain that translates intentions into instructions for robotic systems, reducing the reliance on low-level programming and opening the door for professionals without a software engineering background to interact with this equipment in a more natural and intuitive way.
Quick highlighted that the company is already building capabilities that allow clients to achieve a 99.8% effectiveness rate. One of the differentiators, according to him, is the ability to operate an environment with 50 digital systems collecting data simultaneously, while also running simulations and real deployments for clients. This kind of parallel operation between the digital world and the physical world is one of the pillars of so-called physical AI, a concept Quick does not consider futuristic but rather something that already exists and works in the present.
The market is responding to this movement quickly. Major global industry players are increasing their automation budgets, and the growth of the collaborative robotics sector — commonly known as cobots — is a clear signal that factories are looking for solutions that work alongside humans, not just in place of them. The arrival of language models built specifically for this segment is likely to accelerate that curve even further, because it addresses one of the biggest bottlenecks in the process: the difficulty of communication between human intent and mechanical execution.
New executives to sustain the growth
To keep pace with all the developments, Launchpad Build AI brought in two notable names for its technical leadership team.
Ken Moynihan stepped into the CTO role, bringing more than two decades of experience in computer vision, robotics, and artificial intelligence. Before joining Launchpad, Moynihan held senior R&D and leadership positions at TOMRA, a global leader in AI-based sorting systems. His expertise in deploying vision systems in real-world environments — not just in controlled labs — is exactly the kind of experience a company at this stage of growth needs to scale its solutions with consistency.
Yannis Georgas came on board as the head of the MLM, overseeing the development and deployment of the Manufacturing Language Model. Georgas comes from Capgemini Invent, where he led industrial data and AI initiatives, delivering projects involving agentic AI, large language models, and digital twins for global clients in the manufacturing and defense sectors. Having someone with that profile dedicated specifically to the MLM shows the company is treating the model as a core product, not a secondary feature.
U.S. headquarters strategically positioned
Launchpad Build AI’s new American headquarters is located in El Segundo, California, in a region that is home to some of the biggest aerospace, defense, and advanced manufacturing companies in the United States. The choice was no accident.
Quick pointed out a figure that puts the opportunity into perspective: in the U.S., 95% of the 64,000 factories are small and mid-size businesses. In the UK, that number rises to about 98%. And among high-mix, low-volume manufacturers, automation penetration sits below 3%. These are exactly the clients the company wants to reach with the MLM, using artificial intelligence to tackle the initial diagnostic phase and eliminating the need to have a specialist on-site or to absorb steep upfront costs.
Chris Pimentel, Mayor of El Segundo, noted that the city’s tech community has an interdisciplinary profile focused on hardware and deep tech that attracts companies like Launchpad Build AI. According to him, the company is not just building solutions in AI and advanced manufacturing but also creating local partnership networks that strengthen the region’s ecosystem.
Digital transformation in manufacturing: the time is now ⚡
Digital transformation in industry is nothing new as a concept, but the speed at which it has been happening in recent years surprises even those who follow the sector closely. The need to operate more efficiently, with fewer people physically present, and with a greater capacity to adapt has shone a spotlight on solutions that were once considered futuristic. Today, talking about automation, machine connectivity, real-time data analysis, and artificial intelligence applied to production is about competitive survival, not a luxury differentiator.
Launchpad Build AI’s move fits into this context with a proposition that goes beyond the product itself. Opening a headquarters in the United States signals a clear geographic ambition, targeting one of the most important industrial markets in the world. The arrival of new technical leaders on the team reinforces that the company is building the structure to grow with consistency, not just riding the momentary artificial intelligence hype wave. Together, these moves tell the story of a company positioning itself for the long haul.
The landscape for manufacturers that have not yet jumped on the automation bandwagon also deserves attention. With the shortage of skilled labor being a growing global problem, technologies that reduce reliance on human specialists during the automation design and deployment process gain immediate practical relevance. The MLM was designed precisely to tackle that point, offering a more accessible and less intimidating entry point for factories that, until now, thought robotizing their processes was out of reach.
For anyone working in or following the industrial sector, the message is pretty straightforward: the convergence of artificial intelligence, specialized language models, and robotics is creating a new layer of possibilities for manufacturing that simply did not exist five years ago. Companies that manage to absorb these technologies into their processes strategically will operate at an entirely different level of efficiency, flexibility, and market responsiveness.
And the most interesting part is that the barriers to entry for this world are going down, not up — which opens space for digital transformation to stop being a privilege of large conglomerates and truly reach factory floors of all sizes. 🏭
