AI is everywhere. In the news, in the tools we use, and in the strategy decks of every ambitious startup.&nbsp;
But while adoption is rising, so is confusion.
We asked founders from across the Faces of AI series to weigh in on the biggest myths they&rsquo;re still encountering in conversations with clients, customers, and teams. Here&rsquo;s what they told us.
<hr>
<h2 dir="ltr">1) &ldquo;You can bolt AI onto existing processes and expect transformational results&rdquo;</h2>
AI isn&rsquo;t a magical solution to all business problems.&nbsp;
If you&rsquo;re already in the space, you&rsquo;ll know that. But as George Parry, CTO at Emma, points out, the idea that AI is a guaranteed golden ticket to perfect workflows still crops up time and time again:
<h5 dir="ltr">People often think it&rsquo;s about adding a chatbot or giving staff access to ChatGPT. And while that might help a little, the real gains only come when you rethink how a business operates with AI at its core.</h5>
<h5 dir="ltr">You could hand someone the best AI tools in the world&hellip; but without people management, change management, and a clear explanation of why workflows need to evolve, it won&rsquo;t have the impact you&rsquo;re hoping for.</h5>
In other words, transformation doesn&rsquo;t come from tooling alone. It comes from redesigning the way people and processes interact with those tools.
<hr>
This challenge becomes even more complex when you look under the hood.&nbsp;
Eduardo Mandela, co-founder of Maihem, sees the same disconnect but from a deeply technical perspective.
<h5 dir="ltr">We see it across the board &ndash; people are very aware of AI&rsquo;s power, but there&rsquo;s still a lot of scepticism around it.&nbsp;</h5>
<h5 dir="ltr">One of the key things many people don&rsquo;t realise is that these new models are probabilistic. You can give them the same input twice and get two completely different outputs.</h5>
That unpredictability doesn&rsquo;t just surprise users, it undermines their confidence in the tools entirely. Hallucinations (or confidently wrong outputs) are consistently flagged by founders as to be one of the biggest hurdles to meaningful adoption right now.&nbsp;
When trust is lost, engagement drops fast. Eduardo continues:&nbsp;
<h5>There&rsquo;s a real gap between what people think AI should be capable of and how these technologies actually work. They&rsquo;re still not perfect.</h5>
<hr>
<h2 dir="ltr">2) &ldquo;AI = ChatGPT&rdquo;</h2>
Generative AI might have put artificial intelligence firmly into the public consciousness, but the field is far broader and older than many people realise. As Ivan Scattergood, CTO of OctaiPipe, puts it:&nbsp;
<h5 dir="ltr">To people outside the space, AI often just means &lsquo;ChatGPT&rsquo; now. But I studied neural networks back in the &rsquo;90s. The maths hasn&rsquo;t changed &ndash; what&rsquo;s changed is data and computation.</h5>
The synonymous use of "AI" and "ChatGPT" reflects just how narrow public understanding still is. The overwhelming focus on chatbot functions has obscured AI&rsquo;s wider applications &ndash; in optimisation, automation, forecasting, recommendation systems, and more.&nbsp;
As adoption accelerates, education needs to catch up. The hype can obscure the real work &ndash; and it&rsquo;s changed how startups present themselves, even when the underlying tech hasn&rsquo;t.
<h5 dir="ltr">We used to avoid saying &lsquo;AI&rsquo; at all. We&rsquo;d say ML, because that&rsquo;s what we were actually doing. We even had a joke: if you&rsquo;re talking to investors, it&rsquo;s AI. If you&rsquo;re hiring, it&rsquo;s ML. And when you&rsquo;re building the thing, it&rsquo;s logistic regression.</h5>
In reality, most startups don&rsquo;t need the flashiest, most complex models. They need ones that are accurate, stable, and fit for purpose.
That&rsquo;s something Katya Lait, founder of nettle, sees regularly in conversations with customers:
<h5>Customers get really excited and ask,&nbsp; &ldquo;Can it do this? Can it do that?&rdquo;&nbsp; We&rsquo;ll get a list of 10&ndash;20 things they want nettle to do. It&rsquo;s great and exciting, but building the kind of features they want takes time, and we have to temper expectations a little bit in the short term.</h5>
For founders, managing expectations is now part of the job &ndash; especially when the hype outpaces the reality of what AI can (and should) do.
<hr>
<h2>3) &ldquo;It&rsquo;s taking over the world&hellip; and our jobs&rdquo;</h2>
Fear is still one of the biggest blockers to adoption &ndash; and not just among technophobes. For many users, it&rsquo;s not the capabilities of AI that concern them most, but the uncertainty around how it works (and what it might be doing behind the scenes.)
Katya Lait regularly sees scepticism surface in client conversations:
<h5>They may misunderstand what's happening, so they'll think that we&rsquo;re reading all of their data, all of their personal information. They don&rsquo;t trust the system and want us to list every single source we use.</h5>
That underlying mistrust isn&rsquo;t always rooted in the tech itself. Often, it&rsquo;s the result of a lack of transparency, or simply a fear of being left behind. George Hancock, CEO at OctaPipe, notes a familiar sentiment:
<h5 dir="ltr">We often hear hesitancy to the tune of "I don&rsquo;t know it, I don&rsquo;t trust it, I don&rsquo;t want to use it."</h5>
And in some cases, those fears are generational. Roshan Tamil Sellvan, co-founder at AdvisoryAI, says this is particularly pronounced among more experienced professionals:
<h4 dir="ltr"></h4>
<h5 dir="ltr">We&rsquo;ve found that older professionals have more barriers than younger ones. They think: I&rsquo;ve been doing this job for 20 years and AI is going to come in and take over my job.</h5>
But founders across the board agree: AI isn&rsquo;t here to replace people. It&rsquo;s here to support them. The real value lies in helping individuals do their jobs faster, better, and more creatively.
<h5 dir="ltr">If you&rsquo;re spending eight hours writing a report, AI can draft the bulk of it in ten minutes. That frees you up to focus on the parts that really matter. The real challenge is shifting perspectives away from the fear that AI is taking over the world.</h5>
That shift in perspective relies on one thing above all: trust. And trust is fragile &ndash; especially when AI systems behave unpredictably or rely on complex, opaque processes. Eduardo Candela puts it plainly:&nbsp;
<h5 dir="ltr">If an AI gives a wrong answer, people often lose trust in the whole system.</h5>
<hr>
<h2>Closing the Gap</h2>
For founders, one of the biggest ongoing challenges is bridging that gap. Helping users, clients, and even investors understand the reality through transparency and education is what will ultimately build trust in systems that often feel opaque.
Eduardo Candela, co-founder at Maihem, believes closing this gap starts with honesty and clarity about what AI can and can&rsquo;t do:
<h5 dir="ltr">We want people to confidently harness the power of AI, while also being aware of the limitations. Bridging that gap between expectations and reality is key.</h5>
That process of building trust is especially tricky in more complex AI systems. Even a well-meaning effort to explain how it all works can end up overwhelming users. As Katya Lait explains, that means walking a fine line between transparency and information overload:
<h5 dir="ltr">Obviously, we want to create trust within the product, but multi-agent systems are very complicated and dense in their workflows.&nbsp;</h5>
<h5 dir="ltr">We pull data from many sources and process them in complex ways, so we have to make tradeoffs between creating trust and not completely overwhelming them with what's going on in the background.</h5>
Ultimately, education, design, and communication all play a role. The AI systems that succeed won&rsquo;t just be the ones that work &ndash; they&rsquo;ll be the ones people understand and believe in.
<hr>
<h3 dir="ltr">Curious to learn more?</h3>
Our <a href="https://www.generativegroup.ai/insights/ai-equal-opportunities-resources/">Opportunities Hub</a> is packed full of resources to upskill and find community in the AI space.
<h3>Building with AI?</h3>
Generative partners with startups at every stage to help them scale smarter, move faster, and deliver real results. <a href="https://www.generativegroup.ai/contact-us/">Find out how we work with AI startups.</a>

We asked founders from across the Faces of AI series to weigh in on the biggest myths they’re still encountering in conversations with clients, customers, and teams. Here’s what they told us.

The 3 BIGGEST Misconceptions About AI (Debunked by Founders)

<h4 dir="ltr">Data centres are under immense pressure, from power constraints to sustainability mandates. Cooling systems are one of their biggest energy drains &ndash; and OctaiPipe is using AI to make them significantly more efficient.</h4>
But this isn&rsquo;t your typical AI startup story. Born from a spur-of-the-moment conversation on a train and shaped by years of deep R&amp;D, OctaiPipe has evolved from consultancy roots into a product-first company focused on federated learning &mdash; an approach that keeps data local and private, while still enabling powerful AI training at the edge.
We caught up with co-founders George Hancock, Ivan Scattergood and Eric Topham to talk about privacy-preserving AI, federated learning, and building for the market before the market&rsquo;s ready.
<h2 dir="ltr">What specific problem is OctaiPipe solving, and why does it matter now?</h2>
George: As AI demand grows, so does the pressure on the infrastructure that supports it. Data centres are facing increasing constraints around power, energy use, and sustainability &ndash; and cooling systems are one of their biggest energy drains. That&rsquo;s where we come in.&nbsp;
Our focus is to optimise energy efficiency in data centre cooling systems using AI, aiming to deliver up to 30% savings.
What makes our approach different is the use of federated learning. Our platform allows AI to be trained and deployed securely at the edge, maintaining privacy. This design has attracted attention from both customers and investors, which has naturally drawn us deeper into the data centre space. We offer a timely solution to a growing challenge.
<h2 dir="ltr">Tell us about the idea behind your startup. What&rsquo;s the story there?</h2>
Ivan: It all started on a train, really. Eric met Paul Carver while geeking out over weather maps &ndash; Eric was racing professionally at the time, and Paul was about to set sail. One thing led to another, and Paul, excited by Eric&rsquo;s background in data science, suggested they start a company.
That company turned into a successful data science consultancy. George came on board to lead commercial efforts, and I joined to help deliver projects. As part of our work, we ran an &ldquo;innovation sandbox&rdquo; where MSc students could complete their theses by working on real R&amp;D challenges for our clients.
It was during one of these projects that we began exploring federated learning &ndash; driven by a use case involving factory-floor devices producing a terabyte of data per week each. Uploading that volume to the cloud was tough, so instead of taking the data to the code, we brought the code to the data. That&rsquo;s when the concept behind OctaiPipe, our federated learning platform, was born.
The market wasn&rsquo;t quite ready for a federated learning platform on its own. What it was ready for was a compelling application of that tech. Our work with edge devices in manufacturing pointed us toward data centres.&nbsp;
Several clients nudged us in that direction, and we realised we could simulate data centre energy use and train reinforcement learning agents to optimise cooling. That&rsquo;s how OctaiPipe became what it is today: a platform quietly powered by federated learning, driving real-world energy efficiency.
<h2 dir="ltr"><img src="https://cdn.sourceflow.co.uk/__gallery/1ad4899e-4079-46fd-b027-5312fccfc353" alt="" width="600" height="398"></h2>
<h2 dir="ltr">There&rsquo;s a lot of uncertainty around AI in the wider population. What are some of the common myths or misconceptions you hear?</h2>
George: We often hear hesitancy to the tune of "I don&rsquo;t know it, I don&rsquo;t trust it, I don&rsquo;t want to use it"&nbsp; &ndash; especially in manufacturing, where people have real concerns about how it fits into their work.&nbsp;
There&rsquo;s also this idea that AI is something you just plug in and it works. But without upskilling and buy-in from the people using it, even the best solutions won&rsquo;t stick. Our approach with clients is always: do you actually need an AI solution? If we can&rsquo;t see a clear business case or ROI, we won&rsquo;t build it. We&rsquo;ve walked away from projects that didn&rsquo;t stack up, and some of those clients have come back later with a more focused brief.
And honestly, AI still exists in a bit of a bubble. To people outside the space, it often just means "ChatGPT" now. But there&rsquo;s a whole world beyond that. Ivan, for example, was working on ML way before it was cool.&nbsp;
Ivan: I studied cybernetics and control engineering back in 1994 and built a neural network to play the Prisoner&rsquo;s Dilemma. The maths has been around for decades. What changed was the arrival of big data and greater computing power about ten years ago. That's why there's been this massive explosion.
Back when we were a consultancy, we used to avoid saying "AI" at all. We&rsquo;d say ML, because that&rsquo;s what we were actually doing. We even had a running joke: if you&rsquo;re talking to investors, it&rsquo;s AI. If you&rsquo;re hiring, it&rsquo;s ML. And when you&rsquo;re actually building the thing, it&rsquo;s logistic regression.&nbsp;
There's a lot of hype about it but, in most situations, you don't need anything particularly complicated to help you. You just need something that works.
<h2 dir="ltr">&lsquo;AI safety&rsquo; can mean very different things depending on who you talk to. What does AI safety mean to you, and how does it show up in your day-to-day?</h2>
George: AI safety is a broad term, and its meaning really depends on who you&rsquo;re talking to &ndash; practitioners, users, commercial teams, or policymakers will all define it differently. For us, it&rsquo;s about building AI systems that are explainable and trustworthy.
Last year, we worked on an Innovate UK initiative to unpack this idea. We looked at how terms like &ldquo;AI safety&rdquo; and &ldquo;AI ethics&rdquo; are often used interchangeably, and we built a comprehensive framework around the principles that matter most to us &ndash; things like transparency, interpretability, and user confidence.
That thinking carried through into a project with P&amp;G, where we focused on practical ways to make AI outputs understandable and reliable. Not just technically sound, but usable and trusted by the people interacting with them.&nbsp;
<h2 dir="ltr">What&rsquo;s been the biggest challenge in bringing an AI product like yours to market at scale?</h2>
George: Market maturity. We&rsquo;ve always been slightly ahead, working on solutions 18 months before the market&rsquo;s really ready for them. That edge has come from staying close to research and applying data science deeply, but it&rsquo;s also meant we&rsquo;ve had to shift focus to more immediate, tangible applications.&nbsp;
The challenge isn&rsquo;t whether AI can help. It&rsquo;s whether the market recognises the problem it&rsquo;s solving, right now.
Eric: I&rsquo;ll put it another way: 99% of people talking about AI don&rsquo;t really know what they&rsquo;re talking about. That makes scaling difficult, because most organisations don&rsquo;t know what they&rsquo;re buying or how to be ready for it.
It&rsquo;s not just about technical integration. It&rsquo;s about embedding AI into business processes. You can build a brilliant churn prediction model, but if the business doesn&rsquo;t know what to do with that prediction, it&rsquo;s useless. And too often, rather than saying &ldquo;we weren&rsquo;t ready,&rdquo; people blame the application.
So, yes, it&rsquo;s a maturity problem. Even now, as awareness grows, most people still struggle to frame the use case, or measure its impact in commercial terms.
<img src="https://cdn.sourceflow.co.uk/__gallery/7edcc2f1-801c-4b66-bcaa-0f4846528d41" alt="" width="600" height="312">
<h2 dir="ltr">Is it possible to have the traditional 3&ndash;5 year business plan in a sector like AI?</h2>
Eric: I&rsquo;d say the idea that &ldquo;everything changes too quickly for long-term planning&rdquo; mainly applies to the world of LLMs, where there&rsquo;s a well-funded arms race driving rapid iteration.
But outside of that bubble, there are slower-moving, longer-term trends &ndash; like edge computing and the ability to run machine learning on small, distributed devices. Those shifts take time because they rely on complex systems maturing and integrating together.
When George says we&rsquo;ve been operating 18 months ahead of the market, I&rsquo;d argue it&rsquo;s often further. We&rsquo;ve been building toward a distributed machine learning paradigm for years: models that don&rsquo;t rely on massive, centralised datasets in the cloud, but instead train locally on devices like mobile phones. It&rsquo;s a direction that supports data privacy, sovereignty, and trust. These are all issues that matter increasingly to individuals, businesses, and governments alike.
So yes, some areas of AI are evolving quickly.&nbsp; LLMs release every few weeks, but even those models are borrowing ideas like quantisation from edge AI, where constraints like limited memory have been long-standing design challenges.
What we&rsquo;re seeing is the growing viability of scalable, distributed AI &ndash; something we&rsquo;ve been ahead of the curve on for some time. And in that space, yes, you can plan for the long term because you're building on maturing trends, not chasing hype.
<h2>You&rsquo;re now a team of 15. What has the talent attraction process been like, and how has it evolved?</h2>
Eric: We&rsquo;ve shifted a lot as a company. In the early days, we were more service-based &ndash; building bespoke solutions for others &ndash; so we hired a lot of sharp, junior talent straight out of top universities. The recruitment focus then was all about raw technical ability.
Now, as we&rsquo;ve transitioned into a deep-tech, product-led company, we look for experienced specialists with domain expertise. The process has become more about understanding how someone approaches design and problem-solving. Can they navigate complexity, not just code?
If someone shows up and can&rsquo;t code, we&rsquo;ll figure that out pretty quickly and they won&rsquo;t be around for long. So it&rsquo;s less about testing fundamentals and more about bringing in people who already know how to apply them at a high level.
<h2 dir="ltr">Finally, you're obviously very busy people. What can we find you doing in your downtime?</h2>
Eric: Thinking about work.&nbsp;
George: Yeah, ringing each other to chat about what we need to do next!&nbsp;
Eric: But when I do switch off, I like exploring Paris with my wife or going climbing.
Ivan: I train in kung fu two or three times a week &ndash; I&rsquo;ve been training with my current Shifu for eight years. Before that, it was Taekwondo, Wing Chun, boxing&hellip; martial arts have been a big part of my life since my twenties. I do a fair bit of mountain biking as well.
George: I used to be big on gym, swim, and exercise. But since my daughter arrived last July, most of my time off is spent on daddy daycare duty to give my wife a well-earned break!
<hr>
<img src="https://cdn.sourceflow.co.uk/__gallery/1993f6aa-ebed-4306-a563-2ddf177443ce" alt="" width="600" height="322">
OctaiPipe is one of the 54 innovative AI organisations featured in our 'Faces of AI 2025' report, which is available to <a class="article-editor-link zSvKgyodXUoGbhUhxSWpxUXaFuhWtTKexrM " href="https://www.generativegroup.ai/reports/faces-of-ai-2025/?utm_source=linkedin&amp;utm_medium=social" rel="noopener noreferrer">download for free now</a>.
At Generative, we work with AI-driven startups to scale innovation, refine strategy, and accelerate growth. If you're building in AI and looking for the right talent to take your company to the next level, <a class="article-editor-link zSvKgyodXUoGbhUhxSWpxUXaFuhWtTKexrM " href="https://www.generativegroup.ai/contact-us/" rel="noopener noreferrer">let's talk.</a>

Cooling systems for data centres consume huge amounts of energy. OctaiPipe is taking on the challenge with AI-powered federated learning

Cool Heads, Cooler Systems: The Startup Making AI Work for Energy Efficiency

<h3 id="ember219" class="ember-view reader-text-block__paragraph"><a id="ember220" class="ember-view" href="https://www.linkedin.com/in/jonathanrhyslow/">Jonathan Low</a> and <a id="ember221" class="ember-view" href="https://www.linkedin.com/in/jakobcassiman/">Jakob Cassiman</a>, co-founders of <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.linkedin.com/company/scopeinspection/" data-test-app-aware-link="">Scope</a>, were outsiders to the Testing, Inspection, and Certification (TIC) industry &ndash; and that&rsquo;s exactly what gives them their edge. </h3>
They&rsquo;re redesigning the inspection process for industries still reliant on pen, paper, and outdated tech. Scope builds AI-powered tools that cut cycle times by up to 45%, freeing inspectors to focus on decision-making over admin.
They&rsquo;ve only just begun, but the potential to disrupt the sector is huge. We caught up with Jonathan to hear how Scope went from an idea to a fast-growing company working with some of the biggest names in TIC.
<h3 id="ember224" class="ember-view reader-text-block__heading-3">Could you be a key player in Scope&rsquo;s founding team?</h3>
Generative has partnered exclusively with Scope to hire for key London-based roles in AI and engineering. If you're ready to help reshape the $250bn inspection industry, get in touch with <a id="ember226" class="ember-view" href="https://www.linkedin.com/in/dan-clouting/">Dan Clouting</a>.
<h2 id="ember227" class="ember-view reader-text-block__heading-2">Let&rsquo;s talk about the origins of Scope. How did you and Jakob come together?</h2>
Both of us are really ambitious people from technical backgrounds. I used to work at Conjecture, focusing on document processing and building tools to make AI systems more controllable. And Jakob was a machine learning engineer working with physical data.
We both knew we wanted to start a company, so we left our jobs and joined a start-up program called Entrepreneur First in London; they introduce you to other aspiring founders and you work on exploring problems and developing products together.
We started building AI tools to search documents like PDFs, Excels and other structured data formats, before leaning into the TIC market.
<img src="https://cdn.sourceflow.co.uk/__gallery/dbd68f25-0a63-497f-b551-34e08b787274" alt="" width="600" height="568">
<h2 id="ember232" class="ember-view reader-text-block__heading-2">How did Scope end up getting into the TIC industry?</h2>
One of our early customers was a large inspection company and the positive reaction we got after working with them was incredible.
We spoke to an inspector on their team who carried a hefty manual of standards she had to reference during every inspection. After trying the tool for the first time, she said, &ldquo;Wow, I don't need this anymore&rdquo; and threw the book away.
It was a small moment that spoke volumes.
After this experience, we started talking to more people in the TIC space, and realised it's an industry that's been stuck with dated systems for far too long. We saw an opportunity to create value and solve real problems &ndash; so we decided to double down on TIC.
<h2 id="ember238" class="ember-view reader-text-block__heading-2">What&rsquo;s the biggest problem that Scope helps inspectors solve?</h2>
Inspectors spend most of their time on planning, creating reports, and data retrieval for their next inspection.
We build tools that automate the time-intensive parts of the process, enabling inspectors to spend more time on decision making. This pays off for our customers: we reduce their cycle time by 45% and get average reporting times down to 12 minutes. Without compromising on the quality of the work.
We talk to companies with 100,000 people, who'll admit, &ldquo;Yeah, we still do everything on pen and paper&rdquo;. The reality is, the software that inspectors are using is built for other people. It's built for people in factories, it's built for maintenance experts. Inspectors have been incredibly underserved by tech solutions.
<h2 id="ember243" class="ember-view reader-text-block__heading-2">What's the most exciting technical challenge that you're dealing with at the moment?</h2>
Something that&rsquo;s difficult in the inspection space is dealing with a lot of different workflows across customers. Inspecting a t-shirt on a production line is very different from inspecting a power plant. So, we build software that can work for different types of customers. That's a challenge that's been pretty interesting for us to adapt to.
We make bespoke systems for each customer. We've got machine learning engineers who ingest everything from PDF data to Excel data; we use that to automate their workflows. That&rsquo;s why the companies we work with love our solution. We're embedded in their systems.
<img src="https://cdn.sourceflow.co.uk/__gallery/8eec0f8d-22d8-4480-807f-f71d0ffbcfec" alt="" width="600" height="400">
<h2 id="ember248" class="ember-view reader-text-block__heading-2">As a startup what are you finding the most difficult at the moment?</h2>
Good question. At the moment it&rsquo;s hiring&hellip; we have a really high bar. We've hired people from DeepMind, Microsoft and Amazon and want every new hire to make the whole team better.
We're ambitious, doing big things, and striving for excellence. The people that join Scope are motivated and intelligent, so it&rsquo;s a challenge to find people with the right skills and mindset.
The people we interview are always really excited about working with us. Obviously there&rsquo;s the technical challenge of building these tools, which is attractive to lots of people. But it's about more than that: our users say that it saves them their weekends and gives them more time back with family. Making that difference is something we all find incredibly fulfilling.
<h2 id="ember253" class="ember-view reader-text-block__heading-2">In terms of scaling the product that you've built, what's the main challenge at the moment?</h2>
We've taken a very modular approach to development to solve different issues. It can be difficult because most inspection workflows are unique. So, we've been through many iterations.
Now, as we scale, the challenge is to make sure that the data layer stays consistent so we're able to onboard customers faster and make it easier for them to make adjustments to their reports and workflows.
As a small team, we've been able to stay very customer focused. As we scale, we want to keep that mindset &ndash; ensuring that everyone on the team gets the same level of customer involvement as we do now. It&rsquo;s something that we're going to keep at the core of our business as we grow.
<h2 id="ember257" class="ember-view reader-text-block__heading-2">How do you see your technology evolving over the next few years? And how will it change what you've built so far?</h2>
We know that language models and AI systems will continue to get better. We've seen a steady increase in how these models perform, so I expect that more of our work can be automated in the future. That&rsquo;ll create opportunities for the businesses we work with.
I see Scope evolving into a platform where businesses can take the data that we gather like all the instructions for a product, and then build their own custom analysis and calculations on top of the data.
<img src="https://cdn.sourceflow.co.uk/__gallery/704dc4fe-23e0-4ea3-a73b-60b851973353" alt="" width="600" height="400">
<h2 id="ember260" class="ember-view reader-text-block__heading-2">You&rsquo;ve got a clear vision for how the product will evolve as you scale - but how do you see the company changing?</h2>
At the moment, we&rsquo;re looking to make a few key engineering hires to scale the team. We&rsquo;re on the lookout for people with the right AI skill set, but mindset is just as important for us.
We know nothing worth doing is easy, so everyone on the team needs to be passionate about solving real world problems.
While we&rsquo;re 10x oversubscribed on our Pre-Seed funding, we predict we'll be raising a Series A in the next 9-to-12 months. We're aiming to have more than a million dollars in revenue in 2025, and &ndash; if we hit our growth aims by mid-2026 &ndash; we&rsquo;ll be on track to be doing 2-to-3 million dollars in recurring revenue, which is so exciting.
As a part of the Series A fundraising, we'll be expanding to the US. We have a couple of customers out there who are eager to get involved in what we're doing. One day, we'll have deployment engineers in the Middle East and Asia too. Our ultimate aim is to be working with inspectors around the world.
<h3 id="ember265" class="ember-view reader-text-block__heading-3">Scope is hiring!</h3>
Work on real-world AI with a pre-seed startup backed by tier-one investors. Contact <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.linkedin.com/in/dan-clouting/?lipi=urn%3Ali%3Apage%3Ad_flagship3_company_posts%3BXL%2Fa1Xp%2FSt2h2foMBgxJ6w%3D%3D" target="_self" data-test-app-aware-link="">Dan Clouting</a> to learn more.
<h2 id="ember267" class="ember-view reader-text-block__heading-2">When it comes to AI trends, what are you most excited about?</h2>
What excites me? This is really niche, but I'm quite excited by OCR (Optical Character Recognition).
We&rsquo;re getting really good at being able to interpret really ugly hand-written documents, which means we can take a lot of data that's just written down and locked away in a filing cabinet somewhere and turn it into really useful data.
<img src="https://cdn.sourceflow.co.uk/__gallery/9567ea09-3068-4140-969b-fd62acf49b2e" alt="" width="600" height="400">
<h2 id="ember271" class="ember-view reader-text-block__heading-2">And what do you feel is overhyped?</h2>
I think agent frameworks are overhyped. A lot of people talk about AI agents and making them work, but so far, we haven't really seen enough reliability to make it work in an enterprise setting.
While it may work for simple tasks, there's a lot of steps in more complex tasks where things can go wrong.
<h2 id="ember275" class="ember-view reader-text-block__heading-2">What's the best advice you've ever received about leading a tech company and a startup?</h2>
The best advice I&rsquo;ve had while building Scope is to build a product that people actually need. Something that genuinely fixes a problem for people.
It&rsquo;s easy to think about solutions and then try to find people that need that solution. But you should speak to people, find real problems, and then think about how you can build a solution to fix that problem.
<h2 id="ember278" class="ember-view reader-text-block__heading-2">You&rsquo;re obviously incredibly busy. How do you switch off and relax outside of work?</h2>
Jakob and I do a lot of running. He recently did a marathon in Europe and I&rsquo;d like to have a go at the London Marathon one day, but it&rsquo;s just so hard to get into.
I also love swimming. I do a lot of in-pool swimming, but when I can, I&rsquo;ll go open-water swimming. I&rsquo;ve also tried my hand at some long distance swimming when I&rsquo;ve had the opportunity. A few months ago, I went for a swim across a huge lake in Wales in the middle of winter, which was incredible, but also a big challenge and pretty scary at times!
<h2 id="ember281" class="ember-view reader-text-block__heading-2">Finally, if you weren't working at Scope, what do you think you would be doing now?</h2>
Before founding Scope, I knew that I wanted to start a business. That&rsquo;s why I left my job and joined the Entrepreneur First program&hellip; but it&rsquo;s interesting to think where I&rsquo;d be if things turned out differently.
If I wasn&rsquo;t building Scope and solving problems for the TIC industry, I'd probably be building another company doing something with AI. There's a lot of interesting problems out there that can be solved with technology and AI, so I&rsquo;d like to think if I wasn&rsquo;t fixing problems in the inspections process, I&rsquo;d still want to be fixing problems elsewhere.
<h2 id="ember284" class="ember-view reader-text-block__heading-2">Want to be part of the team revolutionising TIC?</h2>
<div class="reader-image-block reader-image-block--full-width">
<img src="https://cdn.sourceflow.co.uk/__gallery/be8d77f4-5c44-4503-b297-585b45ea0490" alt="" width="600" height="600">
Are you a driven AI Engineer, looking to make an impact and solve problems with technology? <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.linkedin.com/company/generativegroups/" target="_self" data-test-app-aware-link="">Generative</a> has partnered exclusively with <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.linkedin.com/company/scopeinspection/" target="_self" data-test-app-aware-link="">Scope</a> to find top talent for three critical roles:
👉 Applied AI Engineer (&pound;90K&ndash;&pound;130K + equity)
👉 Founding Full Stack Engineer (&pound;80K&ndash;&pound;120K + equity)
👉 Forward Deployed Engineer (&pound;80K&ndash;&pound;120K + equity)
These are permanent, London-based opportunities (Covent Garden) and offer a rare chance to get in early at a 10x oversubscribed pre-seed success story, backed by tier-one investors.
📩 Get in touch with <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.linkedin.com/in/dan-clouting/?lipi=urn%3Ali%3Apage%3Ad_flagship3_company_posts%3BXL%2Fa1Xp%2FSt2h2foMBgxJ6w%3D%3D" target="_self" data-test-app-aware-link="">Dan Clouting</a> / <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="mailto:dan.clouting@generativegroup.ai" target="_self" data-test-app-aware-link="">dan.clouting@generativegroup.ai</a> to find out more.
<img src="https://cdn.sourceflow.co.uk/__gallery/1993f6aa-ebed-4306-a563-2ddf177443ce" alt="" width="600" height="322">
Scope is one of the 54 innovative AI organisations featured in our 'Faces of AI 2025' report, which is available to <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.generativegroup.ai/reports/faces-of-ai-2025/?utm_source=linkedin&amp;utm_medium=social" target="_self" data-test-app-aware-link="">download for free now</a>.
At Generative, we work with AI-driven startups to scale innovation, refine strategy, and accelerate growth. If you're building in AI and looking for the right talent to take your company to the next level, <a class="UCfvfNsyTpsPQidhUnqqzZLAaQtxAVUA " tabindex="0" href="https://www.generativegroup.ai/contact-us/" target="_self" data-test-app-aware-link="">let's talk.</a>
<figure class="reader-image-block__figure"><a href="https://www.linkedin.com/posts/generativegroups_scope-x-generative-activity-7323270478075232256-AP7q?utm_source=share&amp;utm_medium=member_desktop&amp;rcm=ACoAABgfqgoBCfoqMBKUwKk7zyapkunRbJbrdKY" target="_blank" rel="noopener noreferrer">
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 ">&nbsp;</div>
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</a></figure>
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Jonathan Low and Jakob Cassiman, co-founders of Scope, were outsiders to the Testing, Inspection, and Certification (TIC) industry – and that’s exactly what gives them their edge. 

Disrupting a $250bn Industry: How Scope is Helping Firms Ditch Pen and Paper for AI

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Specialists In&nbsp; Industry 4.0 Recruitment&nbsp;
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At Generative, we don&rsquo;t just fill roles &ndash; we help businesses build the teams they need to thrive in the era of Industry 4.0. By focusing on Automation, Data, and AI/ML.&nbsp;
</div>
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Far beyond just filling open roles &ndash; We leverage or team&rsquo;s in-depth automation and AI expertise to help streamline your hiring process and achieve your goals.&nbsp;
</div>
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If you&rsquo;re ready to build an agile &lsquo;Industry 4.0 ready&rsquo; team that understand how AI/ML, data, and automation can propel your business forward, we&rsquo;ve got the connections to secure the right talent.&nbsp;
</div>
<div class="OutlineElement Ltr SCXW148639536 BCX0">
<h5 class="Paragraph SCXW148639536 BCX0" lang="EN-GB" xml:lang="EN-GB">The Challenges We Solve For You&nbsp;</h5>
</div>
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<li class="OutlineElement Ltr SCXW148639536 BCX0" role="listitem" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1">
We address the scarcity of specialised automation talent with our in-depth industry expertise&nbsp;
</li>
</ul>
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<div class="ListContainerWrapper SCXW148639536 BCX0">
<ul class="BulletListStyle1 SCXW148639536 BCX0" role="list">
<li class="OutlineElement Ltr SCXW148639536 BCX0" role="listitem" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1">
Slow recruitment processes disrupt timelines, but we deliver fast solutions to keep projects on track&nbsp;
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<ul class="BulletListStyle1 SCXW148639536 BCX0" role="list">
<li class="OutlineElement Ltr SCXW148639536 BCX0" role="listitem" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props="{&quot;335552541&quot;:1,&quot;335559683&quot;:0,&quot;335559684&quot;:-2,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1">
Traditional hiring can be expensive, so we focus on tailored, cost-effective approaches&nbsp;
</li>
</ul>
</div>

Flexible Industry 4.0 Talent

The Challenge&nbsp;
A major investment led to a significant expansion of their manufacturing capabilities, including new automation infrastructure. &nbsp;
The company needed urgent support to hire across Automation and MES roles to meet project deadlines. Existing suppliers struggled to deliver sufficient talent within budget&nbsp;
Our Solution&nbsp;
We secured 15 engineers across both disciplines, strategically located in the US and EU, and provided end-to-end support with onboarding and logistics. &nbsp;
This allowed the company to meet project timelines and successfully complete their expansion.&nbsp;

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<h2 class="Paragraph SCXW235710044 BCX0" lang="EN-GB" xml:lang="EN-GB">Proven Results&nbsp;</h2>
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<h5 class="Paragraph SCXW235710044 BCX0" lang="EN-GB" xml:lang="EN-GB">Leading Biotechnology Manufacturing Company &ndash; East Coast, US&nbsp;</h5>
</div>

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