IntelliAM AI – executive interview

In this interview, IntelliAM AI CEO Tom Clayton and CFO Daud Khan discuss how the company’s industrial AI platform helps manufacturers improve reliability, productivity and operational efficiency. Clayton explains how machine learning, large language models and closed-loop applications turn factory data into actionable insights, drawing on examples involving Müller and SKF. He also discusses the platform’s differentiation, its ability to work across different equipment and data sources and recent commercial momentum involving Mars, an agricultural customer and US integration partner SkyIO. Khan outlines IntelliAM’s land-and-expand model, in which engineering relationships develop into recurring software subscriptions that can extend across manufacturing lines, sites and geographies. He discusses the target of at least 70% gross margins on software subscriptions and the potential for revenue to grow faster than costs. The interview also covers how the c £6.3m conditional fundraising and proposed move to AIM support US expansion, with annual recurring revenue (ARR) growth, customer penetration and operating leverage identified as key measures of progress. A £0.5m retail offer via the BookBuild platform closes at 4:35pm GMT on 27 October 2026.

What does IntelliAM do and what problems does it solve for manufacturers?

Tom Clayton: Ultimately, the IntelliAM AI platform is about getting more for less, which is what industrial AI should be about. It’s the true productivity conundrum. We connect to the machines and assets of factories and provide intelligence, but it goes right through to the organisation’s supply chain.

We’re taking huge amounts of data. Over 16bn data points a week are now being ingested. From that, we’re creating insights that provide reliability intelligence, safeguarding processes and machinery and helping mitigate risks.

We’re also providing productivity and operational insights, getting greater throughput efficiencies and reducing waste. The decision logic, right through to the planning and organisation of these businesses, means they can lower their cost base at the same time as increasing their throughput. That’s essentially what we provide.

Which manufacturers use IntelliAM, and how quickly can customers benefit from the technology?

Tom Clayton: We’ve got a strong presence in the food and beverage industry. We’re serving roughly 20% of the world’s top 100 food and beverage organisations and beginning to expand across their estates. That’s probably where we’re most predominant. However, we’ve also moved into building materials. Pharma and chemical processing are quite prevalent too, so the verticals we can reach are quite wide. We’re arguably solving the more complicated problem in the intricacies of packing and wrapping environments, which is easier to apply to process environments. Many of the fast-moving consumer goods people buy on supermarket shelves are products whose applications and processes we support. A typical example is Müller, which has been cited as quite a big customer of ours. They provide a significant share of milk to the UK market across numerous factories and production lines. The typical journey would be through various layers of our organisation.

The first layer is about providing reliability intelligence. The platform is extremely agnostic, so it doesn’t really matter about the age, the OEM profile, the machine profile or the sensor profile. We’re able to connect to those in an intelligent, unified way that allows us to add context at speed. That enables us, first and foremost, to provide a more advanced predictive maintenance solution from an engineering perspective. That can be done quite quickly. Within two or three weeks from the point of order, we can have machine learning algorithms and guard-railed large language models established in their factories to provide data. The reason I say that is we don’t have an AI platform that hallucinates and tries to please people. It’s dead accurate. It has to be. This is health and safety. This is where millions of pounds and decisions are being made, and we need to get that right.

The second layer is where we’re taking vastly more data from things like line controls and enriching it with criticality assessments, plant maintenance instructions, operational instructions, shift patterns and SKU patterns. This allows them to have copilots to create transformational change. They can see right through to buying: where to get ingredients from and the effects on the plant, differences between shifts and how to understand the optimum run speeds or tolerance settings to maximise throughput. That’s generally a two to three-month onboarding process. At that point, we can connect more into what we call agentic AI, in the sense that we have closed-loop systems.

I think we announced roughly 12 months ago a partnership with SKF, the world’s largest bearing and lubrication system supplier. Essentially, you’ve got IntelliAM inside the hardware they provide. Our intelligence sits inside it and enables the hardware to interact with the machine and the environment to maximise dosing efficiencies in a closed-loop manner. There are essentially three areas of AI: machine learning, which is the primary point, large language models and agentic functions.

How is IntelliAM’s machine learning technology differentiated, and what do agentic AI capabilities add?

Tom Clayton: The difference ultimately is that we’re not a current incumbent. We’ve come to this market with a huge amount of domain expertise. We’ve come from the manufacturing sector with automation capability, asset management capability, reliability and applied science within our organisation.

One of the major steps forward we have over other software companies is that we understand the marketplace implicitly. We understand exactly what data needs to be ingested, what the correlation is, what the requirements and insights need to be and how people need to receive it and interact with it within their own workflows. We have all that information within the organisation and that gives us a massive leg up over other software businesses.

The other area is that, as I mentioned previously, we’re totally agnostic. We most often now speak to C-suite individuals about buying some of the transformational intelligence we can provide. What they don’t want is 50 intelligent islands with different commercial agendas across their platform.

Our platform is holistic. It sits over the line controls and whoever’s sensor data is out there, providing a single source of truth that people can interact with.

We did a big launch at the stock market about three months ago and talked about cracking the code of industrial performance. It’s the way we unify and land that data through our IntelliAM Namespace organisation that allows us to provide intelligent solutions. I think we generally have a first-mover advantage, and the listing and raise right now give us a great deal of wind beneath our sails. It’s a super exciting time to start to scale out because we can do that at speed now, and that’s the real key.

How do recent wins with Mars and a large agricultural customer, and the SkyIO partnership, support growth?

Tom Clayton: In the first two years of being on the market, leading up to the move taking place now, there’s been an awful lot of productisation around the platform. Some of those logos you mentioned, and many others, have been on that journey. Some were early adopters. What we’ve just mentioned, and the way we unify and move now, means we can scale at pace across these estates. That’s been a major breakthrough. If you take an organisation like Marshalls, we were connected to five to six sites quite quickly, within weeks. You’re connecting and releasing value to organisations, and that’s important.

That moves us to SkyIO, because we’ve set up an office in the US. If you look at our portfolio of customers, a large percentage are American-owned businesses. You mentioned the agricultural business, Mars and Post Holdings, which is Weetabix in the UK. If we’re providing insights and value, there’s a natural evolution that could take place into that market, with case study examples within those organisations. It’s a huge addressable market.

We decided quite early that we don’t want to set up another software business in the US. We want a commercial entity there, so we want the right integration partners. SkyIO is a wonderful company, with up to 200 automation engineers deployed across the US. We’ve productised our platform, which largely takes out a lot of the upfront engineering work to make sure we can scale. There are always going to be occasions where it’s about pulling data from different silos and making sure businesses are AI-ready. The piping between the factory and the cloud is essential. That’s the sort of work SkyIO can provide for us and what we’re looking for from an integration partner in that space. It allows us to scale at the rate we need to.

How do customer relationships develop into recurring software revenue, and what does this mean for operating leverage?

Daud Khan: Fundamentally, our model is a land-and-expand model. We often begin our customer relationship through established engineering and asset management services. That gives us a deep understanding of their operations and, importantly, builds trust. The next step is to connect the manufacturing data into the IntelliAM platform. That creates the digital foundation from which we can deliver our software and AI capabilities.

From that, the opportunity is to expand. We may initially connect a number of manufacturing lines at one site. As a customer sees the value, we can add further lines, additional sites and potentially new geographies. A lot of our customers are international. Each expansion increases subscription ARR and makes IntelliAM increasingly embedded in the customer’s operations, strengthening retention and the long-term value of the relationship.

Financially, that changes the mix of the business over time towards higher levels of recurring software revenue. We are targeting a minimum 70% gross margin on software subscriptions.

As Tom mentioned, the platform has been architected for scale using our IntelliAM Namespace and unified namespace approach. That means revenue can grow significantly faster than the underlying cost base. We expect to use AI extensively within our organisation, improving productivity, so we should not need to increase headcount proportionally with revenue. That operating leverage is an important part of our pathway to profitability over the next few years.

How will the fundraising and proposed AIM move support growth, and which milestones should investors monitor?

Daud Khan: The proposed AIM move and fundraising are about giving us the capital and platform to accelerate the model, which we believe is scalable. AIM gives IntelliAM access to a broad institutional and growth investment audience, while the fundraising allows us to invest selectively behind some very large addressable markets. The US is the obvious example.

We don’t believe we need to replicate a large infrastructure base in the US as we have done in the UK. The product is designed to scale internationally and across estates. What we need initially is a relatively small number of the right people on the ground, supported by targeted marketing and business development investment to build the pipeline and convert opportunities.

For investors, there are a few clear milestones over the next few years. The most important is growth in ARR. Alongside that, investors should look for evidence of the land-and-expand model working: increasing across sites within our existing customers and adding new logos initially from a single site before expanding across regional and international locations. Ultimately, the strongest validation of the model will be a combination of ARR growth and increasing penetration within existing customers, alongside new customer wins and improving operating leverage in the financials. If we continue to grow recurring revenue while costs increase at a materially slower rate, that will progressively move us towards profitability.