Global connectivity provider Roamless has closed a $12 million Series A funding round led by Rasmal Ventures, with participation from Shorooq, Revo Capital, Finberg, and JIMCO. The new capital brings Roamless' total funding to $18 million, following a $6 million seed round in 2024. Founded in Türkiye and headquartered in the US, Roamless was established in 2023 by Emre Demirel, Ali Gazioglu, Asim Alp, Selim Aykut, Cagdas Yalti, and Cengiz Oztelcan. It offers a borderless mobile connectivity platform built around a single eSIM intended to work across multiple countries. Roamless provides a “Single Global eSIM” with pay-as-you-go and trip-based plans across 200+ countries, designed to keep travellers connected without changing SIM cards. The company says it operates on its own telecom infrastructure and plans to add features such as local numbers, voice, SMS, and partner APIs. Roamless reports serving more than one million travelers and working with partners in travel, aviation, and financial services. Roamless telecom stack is a cloud-based, carrier-grade infrastructure layer that manages connectivity across hundreds of networks through a single service. This supports cross-border coverage and is intended to enable services beyond data-only offerings, while giving it more control over the customer experience than a resale model. The eSIM market has been growing quickly, supported by wider adoption from smartphone manufacturers. While fewer than 20 per cent of global smartphone connections used eSIM in 2024, the GSMA projects penetration could reach up to 88 per cent by 2030. Roamless also expects adoption to continue rising as more devices move away from physical SIM trays. Emre Demirel, co-founder and CEO of Roamless, said the company expects the market to grow significantly and believes long-term winners will combine a strong product with reliable infrastructure and carrier-grade technology. We are rolling our local market, b2b and AI-enabled features in our next phase - all to ensure we continue to lead on innovation, and give global mobile users what they need to maximise their experience. In the next phase, the company will expand network coverage, scale its global go-to-market and customer support efforts, and deepen supplier and corporate partnerships. It plans to launch Roamless Numbers, which would allow users to obtain local numbers in more than 20 countries and make calls or send and receive SMS within the app. The company will also invest in APIs and B2B solutions so airlines, airports, online travel agencies, financial institutions, and superapps can embed connectivity into their customer journeys. In addition, it expects to roll out AI-driven features aimed at improving network quality, lowering costs, and surfacing relevant partner offers, while growing its team across regional offices in major travel hubs.

A David vs. Goliath shift is emerging in AI as a small Nordic team is challenging Big Tech’s cloud-LLM dominance by bringing Small Language Models (SLM) directly to users’ devices. Copenhagen-based open-source startup NobodyWho has raised €2 million in pre-seed funding. The company aims to strengthen Europe’s position in global AI by championing Small Language Models as a cost-efficient, data-secure, and climate-aligned alternative to today’s massive cloud-based LLMs. I spoke to founder to CEO, Cecilie Waagner Falkenstrøm, to learn all about it. NobodyWho is founded by award-winning entrepreneur and artist Cecilie Waagner Falkenstrøm, whose pioneering work with interactive AI dates back to 2016. Together with co-founder and CTO, Asbjørn Olling, and a team of software engineers, she has spent nearly a decade advancing local-AI technologies — from UN-commissioned projects to a 2021 edge-AI experiment aboard NASA’s International Space Station.  Today’s cloud-based LLMs are controlled by a handful of non-European tech giants and require massive computational resources, constant internet access, and the transfer of vast amounts of data to third-party servers. This creates high costs, lock-in, and a structural loss of European data security.  NobodyWho takes a fundamentally different approach. Its engine enables Small Language Models (SLMs) to run locally on laptops and mobile phones, so organisations and individuals keep full control over their data. With device-first architecture, no data needs to leave the device enabling true data sovereignty and privacy by design.  Waagner Falkenstrøm explains: “These models are still large by most standards, but they’re much smaller than systems like ChatGPT. They’re comparable to earlier generations of large modelsn— and they’re more than capable for many real-world use cases.” Local inference as a security and privacy advantage Running models locally has immediate privacy and security implications. From a security standpoint, this also creates a more resilient architecture. Rather than relying on a single, centralised cloud server that can be targeted, computation is distributed across thousands—or even millions—of devices. Further, local inference shifts the cost burden away from cloud infrastructure entirely. Users bring their own hardware, meaning the system can scale without increasing inference costs. Whether an application serves ten users or ten million, there is no escalating cloud bill.  This makes advanced AI accessible to organisations that would otherwise be priced out, including NGOs, public-sector bodies, and early-stage startups. A 500x reduction in AI’s carbon footprint NobodyWho’s local-first SLM architecture dramatically reduces this footprint by shrinking the models and moving them close to where they are used. Early benchmarks show up to 100x lower training footprint and up to 500x lower inference footprint. So this approach isn’t just cheaper and faster — it’s also dramatically more sustainable. Open source by default Everything core to NobodyWho is open source — from its inference engine and inference libraries to its developer integrations — and that will remain the case. Rather than building proprietary language models, NobodyWho focuses on the infrastructure layer that makes existing open-source models usable in real-world products. Its engine enables more than 10,000 open-source language models to run efficiently across devices and operating systems. “Our belief is simple: the models already exist,” says founder and CEO Cecilie Waagner Falkenstrøm. “The real bottleneck is making them practical to deploy — especially for developers who don’t have machine-learning expertise.” Most developers, she explains, aren’t ML specialists. NobodyWho’s goal is to make running a local language model as straightforward as integrating any other software dependency. “An app developer should be able to run a local model with two lines of code,” she says. “You shouldn’t need a PhD in machine learning to ship AI.” To achieve this, NobodyWho integrates directly with major developer frameworks. The company recently launched Python support and is expanding into additional ecosystems, allowing developers to drop NobodyWho into existing projects without deep ML knowledge or custom infrastructure work. The company operates on an open-core business model. While all core components remain open source, NobodyWho monetises fine-tuning services — an area where compute requirements quickly become expensive and operationally complex for teams to manage alone. “Companies could fine-tune models on-prem,” Waagner Falkenstrøm explains. “But that means servers, engineering time, and ongoing maintenance. Instead, they can fine-tune models using our engine, pay for the compute, and we take a small cut. It’s still significantly cheaper and simpler than doing it themselves.” Once a model is fine-tuned, it can be deployed to millions of end users with no additional inference cost, a key advantage of running models locally rather than in the cloud. Small language models, running where the data lives I was curious what the trade-off is from using SLMs instead of LLMs. “Historically, making an API call to a cloud-based model was easy, and running models locally was hard. "We've solved that problem,” shared Waagner Falkenstrøm. “With NobodyWho, the traditional complexity gap of local inference is effectively removed.” There are still some use cases where very large models are necessary — extremely broad or complex reasoning tasks. Those models won’t disappear. But for most real-world business applications, such as chatbots, HR assistants, customer support, and domain-specific tools,  Small Language Models are more than sufficient, especially when fine-tuned. “Fine-tuning smaller models is also easier, explained Waagner Falkenstrøm., “You need less data and less compute, and you get more controllable behaviour. Most companies operate within specific contexts, and small models excel there.” NobodyWho uses the European Public License (EUPL) 1.2, which explicitly allows both individuals and companies to build commercial products on top of its code — a deliberate choice aimed at driving real-world adoption with cross-platform support across mainstream operating systems and development frameworks included. “If you want genuine uptake, commercial use has to be allowed,” says Waagner Falkenstrøm. “Otherwise you don’t get an ecosystem — you get a demo.” Over 5,000 devs building The ecosystem is already forming. NobodyWho now has more than 5,000 developers building with the platform via GitHub, alongside an active Discord community where contributors discuss use cases, share feedback, and help shape the roadmap. “The open-source aspect is critical,” Waagner Falkenstrøm adds. “It’s what allows a real community to emerge — not just users, but contributors.” With the platform well beyond MVP, the company’s focus has shifted firmly to scale. “We’re past the experimentation phase,” she says. “Now it’s about expanding framework support and enabling more developers to build production-grade applications.” She believes the next leap forward in AI will come from making models smaller, more local, and more human-centric.  “The future of AI won’t be won by size, but by decentralised models that anyone can run on their own devices.” Investors view the rise of local, energy-efficient AI as a major strategic opportunity for Europe, especially as demand grows for privacy-compliant and cost-effective alternatives to cloud models. The round is backed by PSV Tech and The Footprint Firm, and Norrsken Evolve. “I’ve known Cecilie for nearly a decade and have seen first-hand how she consistently turns bold ideas into real, working technology,” says Christel Piron, co-founder and General Partner at PSV Tech: “Backing NobodyWho was a no-brainer for us: this is an exceptional team building critical European AI infrastructure that is privacy-protecting, energy-efficient, and accessible to developers and companies everywhere”. Sofie Käll, CIO at The Footprint Firm, shared: “NobodyWho is pioneering the infrastructure that makes these ultra-efficient models truly plug-and-play for developers.  This is a transformative climate-tech opportunity in one of the fastest-growing emissions categories, and we’re excited to support a team capable of moving the industry toward more responsible AI."  Waagner Falkenstrøm contends that no matter what we do, Europe will not outcompete the US or China in the “bigger is better” game.  “The compute, capital, and hyperscale infrastructure simply aren’t comparable. “But from our experience, we knew that smaller models are genuinely powerful in many fields. That creates an opportunity for Europe to compete differently.” At the same time, there’s a strong values-based dimension to what NobodyWho is doing.  She asserts: “Coming from the EU and the Nordics, we care deeply about data security, GDPR compliance, sustainability, and data sovereignty. NobodyWho is designed to reflect those values.” “Technology is power” Waagner Falkenstrøm asserts that Europe needs to believe in itself. We have some of the best education systems, software engineers, and research institutions in the world. “We don’t need to copy the US — we need to build AI that reflects European strengths and values. Further, technology is power. If we care about privacy, sovereignty, sustainability, and democratic control, those values must be embedded in the technology itself. That’s what we’re trying to do with NobodyWho: decentralised, open, privacy-preserving AI that anyone can build on.” Waagner Falkenstrøm sees NobodyWho as part of the first real wave of companies building infrastructure specifically for Small Language Models. “A year ago, SLMs weren’t widely discussed. Today, developers and investors understand the category. The models are improving rapidly, and the tooling has matured. Big tech companies will enter this space — but they’ll optimise for their own ecosystems. Apple will build for Apple. Microsoft will build for Microsoft. We’re platform-agnostic. That creates a meaningful opportunity.”

Oxford-based materials science startup A&B Smart Materials has closed a £1.5 million pre-seed funding round. The round was led by existing investor Sake Bosch, with new strategic investors Caesar and Living Hope VC, alongside participation from Archipelago Ventures, Triple Impact Ventures, Cranfield University Seed Fund, Oxford Seed Fund, and several business angels from Cambridge Capital Group and Oxford Innovation Finance. The superabsorbent polymers (SAP) market is sizeable and growing, driven mainly by demand from absorbent hygiene products such as nappies and menstrual pads, with additional use in agriculture and smaller applications in areas like medical products, construction, consumer goods, and water treatment. Most SAPs in use today are synthetic and fossil-based, and because they are not designed to biodegrade, they can persist in the environment and contribute to microplastic pollution, including through high-volume single-use products such as disposable nappies. A&B Smart Materials is developing fully biodegradable alternatives to fossil-based superabsorbent polymers, using polymer science and natural feedstocks to create absorbent materials intended to match existing performance requirements and fit within established manufacturing processes. Its approach is based on modified biopolymers derived from widely available, lower-cost natural materials. A&B Smart Materials co-founder and CTO Dr. Benjamin White said the persistence of synthetic superabsorbent polymers contributes to long-lasting pollution, including microplastics in the environment. We intend to completely replace these products with biocompatible and biodegradable materials, without compromising on product performance or affordability. The funding will primarily be used to accelerate R&D to refine A&B’s sustainable superabsorbent polymer formulations and target a combination of strong performance, competitive cost, and industrial-scale validation for hygiene and agricultural applications. The company’s longer-term objective is to replace synthetic SAPs in a market projected to reach about $17 billion by 2035.

Resale platform Bought, launched in March 2025, has announced its third acquisition of the year, Robes Rental, to strengthen its position in Europe and explore peer-to-peer renting. Earlier this year, Bought acquired the Finnish secondhand platform Zadaa as well as the bankruptcy estate of Vähänkäytetty.fi. Bought was the first to launch peer-to-peer local logistics in partnership with Wolt earlier this year. Check out our earlier interview with Erik Kymäläinen, co-founder and CEO of Bought. Robes Rental is a Finnish peer-to-peer rental platform focused on luxury fashion. Featured in Vogue and loved by celebrities and politicians alike, Robes’ technology, industry knowledge, and dedicated community allow for Bought to strengthen its position as the up-and-coming circular platform arising from the Nordics. “Robes and Bought have been aligned in many ways from the start. Both were founded in Helsinki by young and ambitious teams with the goal of changing the way we buy, sell, and own. Both believe that technology plays a key role in enabling this shift in circularity. This collaboration gives us the tools to realise our vision even more efficiently,” says Erik Kymäläinen, co-founder and CEO of Bought. “Two years ago, we set out to build a new kind of wardrobe experience, one that made luxury fashion accessible and circular. By combining Bought’s automated resale and digital closet technology with Robes’ expertise in rental and circular wardrobes, we can offer a more complete circular fashion experience and create greater impact from one place. This is an exciting new chapter for our community and for the entire circular fashion ecosystem in Europe,” says Anna Sillanpää, co-founder and CEO of Robes Rental. Moving forward, Robes’ technology will be transferred to Bought to support the development of advanced circular wardrobe capabilities on Bought’s platform. This means that the standalone Robes app will sunset at the end of the year, making way for something even more impactful through Bought.

Monzo is making its first-ever acquisition, buying UK digital mortgage broker Habito. The acquisition of Habito, which employs around 100 people, comes as the UK challenger bank looks to beef up its mortgage offering and diversify beyond its core digital banking products. Kunal Malani, Monzo chief banking officer, posted on LinkedIn: “Buying a home should be exciting - not overwhelming. Together, Monzo and Habito will make it easier for customers to find, compare and secure the right mortgage, all from the Monzo app." Monzo said the deal, for an undisclosed amount, means it’s the first UK bank to offer an end-to-end mortgage broking experience within its app. Monzo, which has more than 14m customers, currently offers users a Homeownership feature, which it says more than 450,000 users use to track their mortgage, home value and mortgage deals. The UK digital bank pointed to data showing 87 per cent of UK mortgage seekers used a mortgage broker. London-based Habito, founded in 2016, is a digital mortgage broker which provides tools, including mortgage switching and home-buying services. It is backed by investors including Augmentum Fintech, SBI Investment and Volution. Ying Tan, CEO of Habito, said: “At Habito, we’ve always believed mortgages should be easier, fairer, and simpler for everyone. "I’m incredibly proud of what our team has built, and I couldn’t be more excited for this next chapter with Monzo. Together, we’ll transform what the mortgage experience feels like - effortless, empowering, and truly built around people." Financial terms of the transaction are not disclosed, with the deal expected to close in the spring of 2026.

OpenAI for Startups is OpenAI’s programme designed to help early-stage companies build and scale products using AI. Rather than just offering model access, it focuses on removing practical barriers for founders by combining technical support, resources, and credits to accelerate product development. In practice, that means pairing access to OpenAI’s models with people, infrastructure, and hands-on support designed to help teams move faster from prototype to production. Startups in the programme can receive OpenAI API credits, higher rate limits, and access to hands-on guidance from OpenAI’s technical teams. Those backed by participating VC partners can unlock additional benefits, including enhanced support and invitations to founder-focused events. I spoke with Mark Manara, Head of Startups at OpenAI, and Romain Huet, Head of Developer Experience, at Slush in Helsinki, to learn more about their support for startups and the biggest trends. Mark Manara , who leads startups at OpenAI, heads a global team working closely with companies building on top of the OpenAI platform. VC partnerships are an extension of startup support. The team also collaborates extensively with venture capital funds through a dedicated VC partnerships function, focused on providing the right resources and hands-on support to the startups they back OpenAI also hosts events and VC summits in places like London, San Francisco, where it brings together its leadership, product teams, and startup teams to share what it's building, what patterns it's seeing, and to hear feedback directly. The focus, Manara says, is on giving founders practical leverage — from infrastructure and credits to direct time with OpenAI’s solutions and engineering teams.  "We focus on resourcing portfolio companies — credits, technical support, and access to our solutions and engineering teams.”  According to M, VCs are understandably most interested in roadmaps but also assurance that OpenAI is closely partnered with their scaling companies. “There’s a lot of interest in understanding what we’re seeing, what we’re building, and we try to share those insights with VCs so they can better support their companies,” shared Manara. “Increasingly, they’re interested in ChatGPT as a distribution channel. With hundreds of millions of weekly users, there’s real interest in embedding startup experiences directly into that ecosystem. Commerce is another emerging area of interest.” Overall, the company’s work with VCs and startups shapes how OpenAI evaluates startups. OpenAI’s litmus test for startups Despite OpenAI’s scale and resources, Manara is quick to stress that the startup-facing team itself is still small. He admits that while the company is big in terms of funding,“We still feel like babies.” “My team is about 45 people globally. I’m based in San Francisco. We started there, but now we’re in Europe and Asia as well.” For Manara, a great startup, from our perspective, is pushing the frontier of how they’re using OpenAI’s models to build product.  “We work with what we call AI-native companies — where there’s an LLM at the core of the product. If you took it out, the product wouldn’t work anymore. That’s the litmus test. Within that, we want companies that are really operating at the bleeding edge. We work across many verticals — coding, customer support, legal tech, healthcare — and also newer companies taking entirely different approaches.” Being at that edge provides a feedback loop. Startups help the team understand how models need to improve to support specific tasks such as legal workflows, live conversation, or sales automation.  “That feedback accelerates our own pace of development.” In return, model improvements are usually very closely tied to product-market fit for those companies.  “It becomes self-reinforcing,” shared Manara.  As more startups build on the same underlying models, Huet argues that defensibility — not just technical capability — has become the defining challenge for founders. The real moat in AI startups: deep problem understanding When it comes to defensibility and category leadership, Huet argues that access to powerful models is no longer enough. He contends that in this wave of startups, there are a lot of competitors emerging in the same categories. “ Legal tech is a good example — I could name a dozen companies off the top of my head.” So what differentiates them? Part of it is product.  “There’s real skill in designing a great product, even without AI. User experience matters enormously," explained Huet. “With AI specifically, how you use the models matters a lot. Some teams have a much deeper understanding of how models are built — how to prompt better, how to provide context, what’s in distribution and what isn’t. That AI engineering sophistication really shows.” Speed still matters. As Manara puts it: “There’s a joke that speed is the only moat in the application layer right now — and there’s some truth to it. Teams that ship fast, get in front of customers quickly, and react in real time have a genuine edge.” Romain Huet agrees, but argues that speed alone is no longer a differentiator. “Speed has almost become table stakes. Builders can now go from an idea to a working feature incredibly fast,” he says. "What really matters is an obsession with the problem you’re solving. Unless you’ve spent dozens — even hundreds — of hours deeply understanding a customer's pain point, it’s extremely hard to solve it well, even with AI.” The strongest founders, Huet adds, are those who combine sharp AI intuition with deep customer obsession — using speed not as a shortcut, but as a force multiplier. The importance of understanding LLMs In terms of the teams that break through, according to Manara, most of the teams OpenAI sees doing really well have very strong engineering backgrounds that sometimes border on research.  “They’re not doing foundation model research, but they understand how models work. There’s a new skill set here that’s different from building a web app. Some teams experiment with fine-tuning — not as a first step, but when it makes sense. That requires understanding data composition, overfitting, and evaluation. That’s a different discipline than hooking up a database to compute.” How startups shape OpenAI’s roadmap Startups play a critical role in OpenAI’s feedback loop. “Startups often provide reproducible examples that our research teams can investigate and build evaluations around,” says Manara. “There’s an old adage in programming languages: the ones people complain about are the ones people use. Models are similar. Even very successful companies can point to many things they want improved.” That feedback feeds directly into OpenAI’s research priorities, which span everything from highly technical issues — such as improving tool-calling accuracy for AI agents — to more visible product capabilities. One area of sustained investment is coding. “Coding isn’t one thing,” Manara explains. “It includes code review, generation, schema adherence, language specificity, and more. We’re constantly iterating across all of those dimensions.” Romain Huet notes how quickly the role of AI in software development has evolved.“Coding has changed dramatically in the last few months. Where models once helped with snippets or light tasks, they now function more like teammates — taking on large, complex work for hours and returning complete outputs,” he says. “That’s why we’re continuing to release models optimised specifically for coding.” According to Manara, “This cycle is different from previous tech waves. When we release something new, it can materially change a startup’s roadmap or an investor’s thesis. So those conversations are critical.” Why pivots have become easier  I’ve seen more startup pivots in the last 18 months than ever before. Why now? According to Huet, pivoting used to be extremely costly — six to twelve months of runway. Now, with AI tools, teams can test new directions in days or weeks: “Founders can explore new customer segments or problems very quickly. Accelerator teams are pivoting multiple times in a short period, which would have been unthinkable a few years ago.” Part of pivoting is open expanding to new markets. In terms of emerging areas, Huet asserts that multimodality and speech-to-speech are still underused.  “The quality is now there, and pricing has dropped enough for startups to build viable products. As AI moves into the physical world — robots, devices, hardware — voice will likely become the primary interface.” OpenAI is learning from Europe According to Manara, some of the most exciting startups we work with are coming out of Europe.  “The ecosystem is vibrant, funding is increasing, and many European companies are category leaders. Several European startups have taught us the most about how our models are used in production.” Huet revealed: “I built my first company in Paris 17 years ago, when the startup ecosystem barely existed. Today, Europe has talent, capital, and experience. What excites me isn’t comparing valuations to the US — it’s the trajectory. Five years ago, many of these companies couldn’t have been built here. Now they can.” Ultimately, Manara wants startups to know “We’re here to work with startups, and we want feedback. Benchmarks matter, but what matters more is how models perform in real products. That’s how we learn and improve.” Huet stresses, "The pace of change isn’t slowing down — it’s accelerating. Founders who stay curious and master the tools will have a real edge. And we’re happy to help.”

Document automation company PandaDoc has raised €5 million from Índico Capital Partners. Founded in 2013 by Mikita Mikado (CEO) and Serge Barysiuk (CTO), PandaDoc began in response to inefficiencies in document processes, particularly for smaller businesses. It later shifted its focus to document automation software, expanded its operations, and grew internationally. In September 2021, PandaDoc reached unicorn status with a valuation of more than $1 billion. Today, PandaDoc provides tools for creating, sending, signing, and managing business documents digitally. Its platform supports workflows such as proposals, contracts, quotes, invoicing, and payments, and is used by teams including sales, operations, HR, legal, and revenue to streamline document-related processes. PandaDoc integrates with a range of CRM, payment, and productivity tools and is designed to replace manual, paper-based document handling with digital workflows. PandaDoc has an office in Lisbon that serves as a European hub for the company, making Portugal an important base for its operations. We're thrilled to partner with Índico Capital Partners as we enter our next chapter of growth. Their deep expertise in AI and track record of scaling technology companies makes them the ideal partner to help us accelerate our AI innovation, said Mikita Mikado, Co-founder and CEO of PandaDoc. The company said the investment will support its next phase of AI-focused product development and team growth in Lisbon, drawing on Índico Capital Partners’ experience in artificial intelligence and scaling companies.

German neobank N26 has appointed a British executive as its new CEO, on the same day it was hit with new sanctions by the German financial regulator relating to compliance issues. Banking executive Mike Dargan will take over as CEO of N26, one of Europe’s most valuable fintechs, in April next year, replacing co-founder and co-CEO Maximilian Tayenthal and interim co-CEO Marcus Mosen. The appointment of UBS executive Dargan effectively draws to a close the leadership of Tayenthal and Valentin Stalf, who founded the challenger bank in 2013. The pair served as its co-CEOs until Stalf stood down as co-CEO earlier this year, following a reported dispute with some of N26’s investors over the handling of regulatory issues by the founders. Dargan, who has held senior roles at Merrill Lynch and Standard Chartered, will stand down from his current role as group chief operations and technology officer at UBS at the end of this year. Dargan said: “This marks the beginning of something new for me – a new opportunity, a new bank and a refreshed business model. N26 has been a pioneer in digital banking with a strong foundation and a forward-looking strategy.” Tayenthal said: “I am confident to step back and put N26 in the very capable hands of Mike. Beyond his extensive experience combining banking, technology and digital transformation, he is also fully committed to the N26 vision.” The appointment of Dargan came on the same day that N26, which has over five million customers across Europe, was hit by new sanctions by BaFin. These included N26 being banned from lending new mortgages in the Netherlands and BaFin appointing a special monitor to oversee N26's compliance activities, after the regulator found compliance shortcomings. The intervention followed previous sanctions imposed by the German regulator on N26, including a €9.2m fine in 2024 relating to late filings of suspected money laundering. Tayenthal will leave the management board at the end of the year and Stalf has moved to be a member of the N26 supervisory board.

The butterfly effect is a well-known pillar in chaos theory and its premise that the simple flapping of a butterfly’s wings in China can cause hurricanes in Texas has had huge cultural influence. It is cited in any number of films including Back To The Future and, more poignantly for this article, AI’s favourite (and lazy) touchpoint Terminator. One of my lowlights this year was attending a UK House of Lords session where the presentation continually cited Arnold Schwarzenegger's film as why the UK was leading the way in AI. More like AI for Idiots. However, there are other places outside the UK Parliament where more enlightened people are setting the standards for the future of AI (and humanity for that matter) with frameworks that will resonate far from where they were announced… not least in Europe. Europe has spent the past decade wrestling with the contradictions of its tech ambitions: yearning to lead in innovation while regulating with an iron fist, chasing Silicon Valley while side-eyeing it and insisting on “strategic autonomy” while relying on US cloud providers and Asian hardware. So when a new global declaration on AI emerges, this time via Seoul in South Korea, it’s worth asking not just what it means for global governance, but what it means for Europe that desperately wants a seat at the top table of AI rule-making. The Seoul Statement, announced last week at the inaugural International (AI standards) Summit in South Korea, is the latest in a string of international overtures attempting to corral AI into something safer, fairer and more widely beneficial. In front of a personally invited audience of 300 people, it framed AI as ‘an opportunity to advance the well-being of humanity’ and emphasised an ‘inclusive, open, sustainable, fair, safe, and secure digital future for all’. Vimal Mahendru is the IEC Vice-President and Chair of the Standardization Management Board and was in Seoul to witness the announcement. “It is always about people, about making technology work for all humanity, and not the other way around. Amidst rapid technological developments, the Seoul Statement aims to safeguard our shared future by placing the aspirations of all humans at the centre of AI governance and standards development,” he said. Europe, meet the world (finally) For once, the global AI conversation sounded very… European. The Statement stressed socio-technical contexts, how AI behaves not in the lab, but out in the wild, interacting with people, institutions and societies. It’s as if the world has been reading EU white papers on long-haul flights and decided they make sense after all. But this isn’t just a pat on the back for Europe’s regulatory evangelists. The Seoul Statement also lands at a moment when the EU is staring down the reality that rules alone won’t build AI champions. Europe is still over-indexed on governance, under-indexed on compute, and chronically under-funded compared to the US and China. Aligning with global standards matters, but only if Europe can engage from a position of technological strength, not moral superiority alone. Standards: the Brussels effect meets the Seoul effect? The Statement gives international standards pride of place, arguing that they ‘build trust, facilitate digital cooperation, enable interoperability across borders’ and strengthen regulatory collaboration. What’s interesting is that Seoul now positions standards as a tool not just for safety or accountability, but for development and inclusion. As Mahendru continues: Building on the work of the Council of Europe's framework on Artificial Intelligence, Human Rights, Democracy and the Rule of Law, the statement highlights the role that international standards play as part of a holistic approach to AI governance. “By making the connection between technical standards and human rights in this way, we have taken an important and necessary decisive step towards ensuring that transformative technologies like AI work for the good of society. Europe should pay attention to Mahendru’s words. Because while it has historically exported rules outward, AI is exposing its own internal divides: between data-rich and data-poor industries. Between countries with powerful research ecosystems such as France and Germany and those still digitising basic services in parts of Southern and Eastern Europe, between startups leveraging frontier models and SMEs still figuring out cloud migration. If the EU wants to remain relevant, it must consider standards not as a cudgel, but as connective tissue, something that keeps Europe interoperable with the rest of the world, prevents digital isolation and ensures European AI remains compatible with global markets. The multistakeholder moment The Statement emphasises building a ‘dynamic multistakeholder community’ for AI standards, one that is ‘inclusive, collaborative and consensus-based’. That may sound obvious, but it’s a subtle rebuke to the more top-down approaches emerging elsewhere. Europe has always prided itself on ‘multistakeholderism’, even if it sometimes forgets to invite stakeholders who aren’t regulators. The Seoul framing presents an opportunity for the bloc to rebalance: to ensure industry, academia, civil society, and, crucially, small companies and scale-ups have real influence in shaping how AI is governed internationally. Because while European corporations are well represented in standard-setting bodies, its startups often are not, and yet it’s the startups that will feel the effects most acutely. The global stage is shifting. Europe can’t just spectate. The Seoul Statement is part of a broader geopolitical rebalancing in AI. Leadership is no longer purely a transatlantic affair. South Korea, Japan, Singapore, the UAE and others are increasingly setting the pace, not just in technology, but in how AI is governed. Europe cannot assume that its frameworks will automatically become global defaults. It must engage deliberately, diplomatically and with humility. It must show up to standards bodies early, not late. It must ensure its safety narratives are backed by technical expertise, not just legislative brilliance. And it must invest in the infrastructure and talent that make participation credible. The bottom line The Seoul Statement echoes Europe’s values, but it also exposes Europe’s vulnerabilities. Alignment on paper is easy; influence in practice is earned. If Europe wants to remain the moral conscience of AI while also becoming a technological heavyweight, it must treat international cooperation not as a validation of what it has already done, but as a challenge to do more… and to do it faster. Because the future of AI will be shaped by those who build it, those who govern it and those who set the standards that sit between building and governing. Seoul is inviting Europe to help lead that middle ground.

The European agrifood entrepreneurial ecosystem is rapidly expanding: innovative agrifood startups are becoming increasingly numerous, particularly in sectors such as agtech, functional foods, biotech, and alternative proteins.  The European Union’s AgriFood Industrial Ecosystem generates around €603 billion in value add and supports 16 million jobs. The food and beverage industry alone accounts for €227 billion and employs 4.6 million people, with more than 99 per cent of companies being SMEs. However, the ecosystem must transform to become more sustainable and competitive. This places pressure not only on local startups and scaleup but also those seeking to enter the European market. Europe’s agrifood transformation demands innovation — and startups need a clear path to scale The pressure to reduce environmental impact, adopt digital technologies, and respond to increasingly conscious consumers is pushing the entire chain—from farmers to distributors — towards innovation. But at the same time, production costs in Europe (labour, energy, regulatory compliance) are high, posing a significant barrier for new companies seeking to scale.  Moreover, climate change adds uncertainty around agricultural productivity and the reliability of raw materials, demanding more resilient and circular models.  Another major challenge is market fragmentation, as each European country has its own regulations, distribution channels, consumption habits, and food-specific rules. For a startup aiming to sell across multiple EU markets, adapting to each context can be costly and time-consuming.  To address these barriers, the European Union and organisations such as EIT Food have launched programmes to support startups in the agrifood sector.  There is strong institutional support to strengthen European competitiveness in food innovation. EU programs and projects targeting startups and scaleups — especially those led by EIT Food — are helping drive a more sustainable, resilient and tech-enabled agrifood value chain. As well as a strong foundation for local startups, Europe represents a highly attractive market for internationalisation, as its diverse economies,  consumers, and regulations offer numerous opportunities for startups to scale beyond their local markets.  For a startup, expanding into new European markets not only means increasing sales but also gaining access to strategic partners (distributors, retailers, and companies within the food value chain),  optimising the supply chain, and diversifying risks.  In response to the opportunity for internationalisation, EIT Food has developed a tailored program called Sales Booster to help agrifood startups that are established in their home country’s entrepreneurial ecosystem and want to expand into new markets within the European region. Inside Sales Booster: EIT Food’s pathway for agrifood startups to enter new markets Sales Booster offers selected startups personalised support tailored to their growth stage, current needs, and geographical focus. Participants gain not only technical support but also recognition and visibility within the European food innovation ecosystem. The program is run through collective activities, such as online workshops and meetings with key industry players to facilitate networking, as well as one-to-one sessions.  Each startup has a dedicated Growth Advisor, expert guidance, and resources to design its expansion plan and ultimately develop an internationalisation plan for its target country or countries.  There’s also access to EIT Food’s wider network. Startups can request meetings with industry and business experts from across the ecosystem, as well as draw on support from an experienced pool of coaches. At the end of the programme, startups are expected to present a concrete action plan for their expansion and have established connections with experts across the European agrifood ecosystem.  Startups powered by EIT Food’s Sales Booster Here are some of the standout startups that have taken part in the programme. Many of them are pushing the boundaries in agrifood innovation — from biotech and agritech to functional foods.  They illustrate not only the breadth of solutions supported by Sales Booster, but also how the programme helps founders turn those innovations into solid expansion plans for new European markets. By backing these companies, EIT Food is both accelerating innovation and strengthening a more connected, resilient and sustainable agrifood system across Europe: Agritrack SA: a platform to automate post-harvest value chains, reducing losses and improving traceability.  Cynomys: IoT solutions to monitor environments (such as farms), enabling more sustainable resource management and improved animal welfare.  EarthAutomations: autonomous robots for agricultural tasks, facilitating automation even on smaller farms.  • BuzzUp: a startup that has developed a programme that “translates” the sound of bees to help farmers understand their condition and improve pollination, directly impacting productivity. Agricolus: a digital platform for precision agriculture, with satellite maps, sensors, and predictive tools to optimise crops. Rebread transforms leftover bread into valuable ingredients, promoting a circular economy in the food industry.  Image: Rebread's happy belly soda. Who can apply for EIT Food’s Sales Booster? Sales Booster targets two groups of innovative startups: agrifood ventures aligned with EIT Food’s mission areas, and non-agrifood startups whose solutions can be applied within the agrifood sector.  To be eligible, companies must be registered in one of the designated RIS countries, offer an innovative or impactful solution relevant to the agrifood industry, and aim to expand within EIT Food’s geographic footprint.  Ideal applicants are those with meaningful early sales traction, a consolidated home market, and a dedicated sales or business development team ready to scale internationally.

Tallinn-based logistics startup MyDello has raised €3.1 million in a funding round led by Icelandic venture capital firm Frumtak Ventures, with participation from existing investor Finnish early-stage venture capital firm Superhero Capital. Frumtak Ventures general partner Andri Heiðar Kristinsson and Jevgeni Kabanov, President of urban mobility company Bolt and a previous investor in MyDello, have joined the board. Freight and shipping across all transport modes account for an estimated 10–12 per cent of the global economy, with maritime shipping handling most international trade by volume, yet the sector still relies heavily on manual workflows, paper documentation, fragmented communication, and limited real-time shipment visibility. Founded by experienced logistics professionals, MyDello aims to address these challenges by digitalising international freight processes and reducing inefficiencies in global supply chains through a B2B platform for manufacturing, wholesale, and e-commerce customers that provides instant door-to-door pricing and routing across freight modes from a single inquiry, supported by agreements with 400+ carriers and partnerships including DHL, Lufthansa, Maersk, Qatar Airways, and Finnair. Businesses enter shipment details (origin, destination, dimensions, and weight) to compare quotes, book transport, and track deliveries in real time with an AI-powered delivery countdown, with a focus on complex long-distance international freight such as routes in and out of the EU, China-linked trade, and lanes between the Americas and Europe. In our coverage earlier this year, MyDello co-founder Magnus Lepasalu said sustainability is becoming a bigger priority in logistics and that the company’s platform is intended to help customers make more informed, sustainable choices, an approach it now plans to scale following the new funding. Since launching in 2021, the company has facilitated thousands of shipments and established partnerships with hundreds of carriers and industry participants. When we launched MyDello in 2021 we knew there was a better way for our industry to operate and in just a few years we have facilitated thousands of shipments, built up a deep customer base, and struck partnerships and agreements with hundreds of key carriers and industry peers. However, this is just the start, and we’re overjoyed to welcome Frumtak Ventures, and we thank them and our returning investors for their support, adds MyDello co-founder and CEO Joel Timm. The company reports onboarding 12,500 businesses from 110 countries and currently operates across 12 countries in Europe and China, with an aim to expand across Europe by 2027. The investment will be used to accelerate international expansion, starting with the UK, where the platform is expected to be available to customers from December. MyDello also plans to further integrate AI into its systems, with the goal of automating most shipment operations by the end of 2026.

The European platform for deploying safe and governed agentic workflows in healthcare, Mindoo, has secured €5 million in seed financing from 6DC, Syndicate One and a group of strategic angel investors. Healthcare organisations are increasingly balancing limited staffing with growing demand for care, leaving some workflows unfinished or not recorded because teams do not have the capacity to manage them. Mindoo addresses this by providing configurable AI agents that hospital teams use to handle structured intake, documentation drafting, follow-up interactions and front-desk communication within a single platform. The company currently offers four core agents that hospitals can adapt to their own protocols, languages, and speciality workflows: a receptionist agent for routine patient communication and registration, a pre-visit agent for structured intake and medical history, a scribe agent for drafting notes, letters, and orders, and a follow-up agent for post-visit communication and care pathways. We learned very quickly that workflows in healthcare cannot be adapted to a product. The product has to adapt to existing workflows. That is why Mindoo lets hospital teams configure and run their own agents, so automation fits naturally into how they already work, explained Gauthier Willemse, CEO and co-founder of Mindoo. Mindoo is currently deployed in hospitals in Belgium and Germany and is designed to integrate with modern EHR systems. The company plans to expand into the Netherlands and France as additional reference sites become operational. The investment supports Mindoo’s plan to provide hospitals and practices with a scalable AI workforce layer that can take on routine tasks, ease pressure on clinical teams, and keep organisations in control of their workflows. The funding will be used to develop the platform further, bring its four core agents to production readiness across multiple specialities, and expand the team across engineering, clinical, and deployment functions.

Lucis, a French startup focused on expanding access to preventive health testing, has closed an $8.5 million seed round led by General Catalyst, with participation from Y Combinator, Kima Ventures, Motier Ventures, Circle.Co, and North South Ventures. Founded by Maxime Berthelot, Baptiste Debever, and Max Guerois, Lucis offers a platform that translates blood test results into a set of indicators tracked every six to 12 months. It provides a structured view of more than 180 biomarkers, covering areas such as cardiometabolic health, hormones, inflammation, liver and kidney function, and selected micronutrients, with the aim of helping users identify potential imbalances and early signals associated with chronic disease. The company positions Lucis as a preventive tool to support understanding and monitoring of health, and notes that it does not replace medical consultation, diagnosis, or prescribing. Our mission is to empower individuals to take ownership of their health by giving them a clear view of key indicators before symptoms even appear, and providing practical guidance to improve their well-being, without replacing physicians, said Maxime Berthelot, CEO and co-founder of Lucis. Members complete check-ups through certified medical biology laboratories across Europe. Results are reviewed by a multidisciplinary medical team, with AI used to help surface key signals and track changes over time. Instead of receiving a static report, users access a dashboard and a prioritised action plan organised around five areas: nutrition, supplements, physical activity, sleep and recovery, and mental health. Lucis positions this model as an extension of broader consumer health tracking, adding biological markers and clinical oversight to support a more structured, public health–aligned approach. The new funding will be used to accelerate rollout in France, the UK, Ireland, and Portugal and into additional markets, expand its network of partner laboratories and clinicians, and further develop its AI-enabled preventive analysis and support platform.

Warsaw-based Jutro Medical, an AI-first primary care operator combining online and in-person care, has raised €24 million in new funding led by Warsaw Equity Group, with participation from Vinci, naturalX Health Ventures, Fluent Ventures, Aternus, KAYA VC, and Inovo VC. The round also includes a debt component from mBank and Orbit Capital. The raise extends the company’s previously announced Series A, bringing the total to €36 million. Founded in 2020, Jutro Medical has grown from a single clinic focused on technology-enabled care into an integrated primary care operator with its own electronic health record (EHR), standardised clinic operations, and AI-based tools. In its first four years, Jutro Medical prioritised building a proprietary EHR and the underlying software and data infrastructure used across its clinics. The company says this foundation has enabled it to add an AI layer more efficiently, allowing AI agents to support administrative tasks such as intake and drafting visit documentation. Clinicians begin appointments with relevant context prepared, review and adjust information as needed, and retain responsibility for all clinical decisions. Use of AI is optional, and patients can choose a traditional appointment. The company’s approach is positioned against broader pressures in primary care, where workforce shortages, rising administrative workloads, and uneven access continue to limit capacity. Primary care spending in Europe exceeds €200 billion annually, including around €9 billion in Poland, yet many clinics still rely on manual or paper-based processes that can slow access to care. Jutro Medical follows an acquisition-led strategy, bringing acquired clinics onto a shared operating and technology platform that includes a common EHR, workflows, and AI tools. The company says it added nine clinics to its network this year and is targeting around 20 acquisitions annually, with the aim of supporting more consistent service delivery and faster integration. By running our own clinics on our own software, we’ve learned firsthand which tasks can be handled by AI. Instead of hiring more staff, we now build AI agents that do the same work – freeing clinicians to practice medicine, not paperwork. These agents already manage thousands of patients interactions every month, says Adam Janczewski, founder and CEO of Jutro Medical. The new capital will be used to support further clinic acquisitions in Poland and to expand the model into other European markets. Jutro Medical also plans to continue developing AI agents to automate additional administrative and operational tasks, while clinicians focus on diagnosis and treatment. Over the longer term, the company aims to build a pan-European primary care operator by consolidating a fragmented market of small practices.

A Berlin-based audio startup, which leverages its own AI models to let users generate synched sound for video, has raised $41m in a seed round, led by Index Ventures and Andreessen Horowitz. The funding round in Mirelo also lured in Berlin-based investor Atlantic and California-based VC TriplePoint Capital. Mirelo has raised around $44m to date and has bagged angel investment from several tech luminaries, including Mistral co-founder and CEO Arthur Mensch and Revolut executive Antoine Le Nel. Mirelo, which has a 10-strong team, was founded by a pair of former musicians, CJ Simon-Gabriel, and Florian Wenzel, who met as AI researchers at Amazon. Mirelo’s big play is that while AI has transformed the creation of text, images and video, sound is lagging behind. It points out the laborious process of adding music and audio to visuals, involving creators and sound designers spending hours searching stock libraries and manually syncing effects. Mirelo, founded in 2023, has developed its own AI models for sound in video. It says a user can upload any video, and in a matter of seconds, Mirelo produces matching audio for anything happening on screen.  It says its sound generation tech is a good fit for AI-generated videos or the gaming worlds. It builds its own AI models from scratch, training them on data for which it says it has licensing deals in place. Its customers are typically individual creators and small studios while its API is used by companies wanting to leverage its models into their platforms or tools. Mirelo recently released a new video-to-sound model, Mirelo SFX v1.5, which it says can generate various soundtrack versions faster than real-time. The startup says its models require 50 times less compute than typical LLMs. The startup will use the funds to advance its tech and try and grow its customer base. Simon-Gabriel, Mirelo CEO, said: “Think of the difference between talkies and silent films – video without sound has so much less feeling and atmosphere. “Mirelo’s first step is about democratising access, empowering everyone to create the sound that their (AI) videos deserve. "But we’ll also empower professionals to rework audio, to do more of what they love, to be more expressive and imaginative in what they can achieve, while handling the boring stuff such as synchronisation. Our bigger mission is to become the audio layer for all visual content across videos, gaming, social media, films and beyond.” Wenzel said: “There’s a deep affinity between music and engineering; maybe that’s why so many of Mirelo’s team are musicians, and why musicians have always been early adopters of new technology. “There’s something about the intersection of mathematical precision and expressiveness that seems to draw people to both fields.” Guido Appenzeller, partner at Andreessen Horowitz, said: "To date, a16z has invested in multiple world-leading generative models each with a different focus area. Mirelo is tackling one of the most technically challenging and least explored areas of generative media: a specialised model for sound effect creation. “CJ and Florian have assembled a research-driven team whose breakthroughs in tokenisation, data curation, and conditioning rival far larger efforts and we’re excited to back Mirelo as they scale their technology for the next generation of video models.”

Iconic, an interactive entertainment and AI-native platform company, has raised $13 million in its seed round, co-led by venture capital funds Kindred and Northzone, with further investment from leading industry players.  The round also brings together a highly curated group of the world’s top AI, gaming, and system engineering leaders from Google, Meta, Disney, DeepMind and OpenAI. Founded by John Lusty and Junaid Hussain, Iconic began in 2023 as a small, technically focused team exploring how advances in AI could enhance human creativity and transform the way players interact with and experience games.   From the outset, the team was equally driven by a desire to improve life for developers, enhancing the creative process whilst reducing the rapidly increasing cost and complexity of building games, and it is this ethos that attracted CEO Andrew Bowell, formerly Product Head at Unity. Through its pioneering on-device AI technology, Iconic is bringing intelligence, agency, and personalisation to the heart of the player experience, allowing game studios to build entirely new genres of games whilst driving down development costs. Earlier this year, Iconic debuted the demo of its voice-driven narrative puzzle game. It enables every word spoken by players to actively shape the world they are playing in.  By applying SLLMs, the technology ensures that internet connectivity is not required, allowing game play across a range of environments without cloud costs or privacy issues. Since launching with NVIDIA at Gamescom, The Oversight Bureau has received strong, consistent praise for its unique level of immersion and responsiveness. With early prototypes demonstrating the potential of voice-driven, character-rich worlds powered by on-device intelligence, this became the backbone of Iconic’s formal launch in 2024, bringing talent from Unity, Meta, Sony, Microsoft, Cambridge University, and major gaming franchises, including GTA and Star Wars.  Andrew Bowell, CEO of Iconic, said, “Our voice-driven gameplay experience is transforming traditional entertainment, utilising novel technology and innovative digital systems to enhance creativity, revolutionise the player experience, and redefine the boundaries of gaming. We are excited to announce our successful seed round led by Kindred and Northzone, with further support from leading industry players, including Google, a testimony to Iconic building the next iteration of interactive entertainment.”

Emmi AI is an Austrian deep-tech company that builds AI-driven physics simulation technology to accelerate engineering processes in fields like Fluid Dynamics, Multiphysics, and Solid Mechanics. For a company doing this kind of work, how people collaborate matters as much as the tech itself. And it turns remote work on its head with its hybrid, remote-first approach.  Every month, they fly everyone to Linz, Austria, for a week. I spoke to Miks Mikelsons, COO, to learn all about it. A research-heavy team, with applied outcomes in mind Today, Emmi AI employs around 30 people, with research forming the backbone of the organisation. Roughly two-thirds of the team come from academic or scientific backgrounds. “We’re very research and science-heavy,” says Mikelsons. “About 20 of our people come from academia.” Around 40 per cent of the team is based across different locations such as Austria, London, and other parts of Europe. Competing for talent without forcing relocation Once a month, for a full week — always the first week of the month, Emmi AI brings everyone together to the same location and covers all the costs of travel and accommodation. Mikelsons  asserts:  “We’d rather spend an extra €1,000 per person per month on bringing people together than invest in the fanciest office or compete in the most aggressive hiring markets. This allows us to attract talent who might not want to relocate. “We’re still distributed across Europe, but this model works well here. It would be harder across the US, but in Europe it’s very achievable.” For someone deciding whether to stay in the US or return to Europe, this model is very compelling. For example, the company hired someone originally from Spain who had been in the US, at the University of Pennsylvania. Competing on culture, not compensation From the beginning, Emmi Ai decided that as a scaling company in one location, it needed to differentiate. “Especially for research-driven companies, culture matters a lot. We wanted strong chemistry and bonding early on, so we invested in being together." “We’re not the company offering the biggest salaries in AI research right now. Some people are getting extremely high compensation offers, and we don’t compete on that,” Mikelsons admits. And the result is that people recommend the company to their networks. “What we offer instead is a way of working and a culture people value. That’s how we’ve been able to attract talent from places like Oxford and Cambridge.” In-house tech by an all-star team  Emmi AI has developed its technology entirely in-house, with its core architecture built in Austria by co-founder and Chief Scientist Johannes Brandstetter and his research team. Brandstetter previously worked on Microsoft Aurora, widely regarded as the world’s first foundation model for weather forecasting. Following the breakup of that original team, the researchers went on to found their own companies. Brandstetter chose to return to Austria from Amsterdam to build Emmi AI. “We have our own technology stack,” says Miks Mikelsons, COO of Emmi AI. “The architecture was built by Johannes together with his team in Austria.” Deeptech for real-world problem solving  “Johannes is a pure researcher,” Mikelsons explains. Unlike many startup founders, Brandstetter comes from a purely academic background, with no prior business or operational experience. Emmi AI’s leadership team is intentionally structured to balance those strengths. “Together with Arno Hollosi, our CTO, and myself focusing on operations and scaling, we bridge deep research with real-world deployment.. As we always say, we apply groundbreaking research to real-world problems and focus on business needs,” Mikelsons adds. “That combination is still relatively rare.” How Emmi AI is rethinking how physical systems are designed and tested In simple terms, Emmi AI uses AI to run complex physical simulations — like fluid flow, heat transfer, structural mechanics, and other engineering problems — orders of magnitude faster than traditional methods. According to Mikelsons. “People sometimes ask,:'What is simulation and what does it do in engineering?' I always tell the story that a hundred years ago, people tested designs by simply crashing them into a wall. Then came wind tunnels, where you could test how a design behaves under air pressure. Later came numerical simulation — equations and formulas that calculate how a particular design will behave in the real world.” However, this process is very expensive and computationally heavy and can take days or weeks.  “With AI, we can now do it in seconds or minutes. That changes the way you design and work in engineering entirely,” he shared.  Industrial use cases: where simulation meets reality The company is active in sectors such as automotive and energy.  “For example, we have customers producing power transformers: those big machines you see near cities that convert high voltage to low voltage. They’re full of metal and oil, and they involve very complex behaviours like transient simulations.” Large grid assets such as power transformers are designed to last for decades, but they are also slow to replace. That reality shapes how electricity networks are operated today. “If you order one of these machines today—say from Brazil or another country — you might get it five years from now,” says Mikelsons. With replacement timelines stretching into years, grid operators have little margin for error. Assets are therefore run cautiously, often well below their theoretical limits, to minimise the risk of failure. “Because of that, grid operators run these systems very conservatively.”  AI-driven simulation offers a way to change that dynamic. By modelling how equipment behaves under different conditions, operators can gain a far more precise understanding of performance and risk. “What we can build are models that simulate operational behaviour,” Mikelsons says.  “That allows operators to anticipate challenges and actively control how these systems behave in practice.” Rather than relying solely on conservative assumptions, grid operators can use simulation to make informed, real-time decisions—unlocking." Letting the team self-organise In terms of employee adoption, Mikelsons asserts that it's all about setting clear rules and planning upfront. “From the interview process onward, people know exactly how this works. It’s always the first week of the month. It’s always the same location. Costs are covered. People really enjoy it. Some take the night train, others fly in. The organisational overhead isn’t actually that big.” In terms of logistics, the company’s office in Linz fits around 25 people comfortably, maybe 30 at a stretch and is hot desking by design.   The company is not aiming for hundreds of people, “but maybe 50 by the end of the year.” Emmi AI also organises activities outside work, such as dinners, bouldering, and spending time in nature.  “We try to make it special without wearing people out,” shared Mikelsons. One of the secrets is that the team increasingly self-organises. At the beginning, management structured everything. Now people suggest activities, breakfasts, and experiments. They try things, see what works, and adjust. For people thinking of doing something similar, Mikelsons advises that clarity is key. You need to be clear about the identity you want to build: “If you’re fully remote, set clear rules for that. If you meet once a year, plan everything around that. Budget, communication processes, meeting structures — everything follows from that decision." Ultimately, Emmi AI believes that the best companies don’t invest only in the next fundraising round or the next customer. They invest in how they collaborate and how they work together.

Most organisations do not wake up one morning and decide to overhaul how they manage messaging and streaming. The shift usually begins with something far less glamorous. A delayed release because a queue was not provisioned on time. A compliance reviewer asking for audit evidence that takes days to assemble. Or a capacity scare on a Kafka cluster that no one saw coming. The familiar moment in a war room, when everyone realises the issue is happening somewhere between five different platforms and no one has the full picture, is also a common trigger. These incidents are usually dismissed as “part of the job”. They sit quietly in the background, tolerated but not solved. They accumulate, and eventually the realisation sets in. The organisation is operating its most critical digital plumbing through a system of fragmented tools, tribal knowledge, spreadsheets, screenshots, and luck. The good news is that there is a way out of this. A new operational model is emerging that allows large organisations to run their messaging and streaming estates with far more efficiency, resilience, and auditability than what has been possible before. But before we get there, we need to understand how the current model became so strained. The Reality No One Talks About: Middleware Has Become Too Fragmented to Manage Conventionally If middleware were still a neat, single-platform world, most enterprises would not have a problem. But the world changed. Acquisitions happened, and digital programmes layered new technologies on top of old ones. Critical systems stayed on MQ, and cloud teams adopted native messaging. Modern apps moved to Kafka. Integration teams added Solace, and microservices brought in RabbitMQ. Different business units made different choices at different times. Now most organisations operate a collection of platforms that were never designed to be viewed or run together. This creates three immediate problems. 1. Operational Fragmentation Every platform has its own way of working. Kafka has partitions and consumer groups, and MQ has channels and queues. Solace has VPNs and message spools, and cloud brokers follow their own patterns. Tools are inconsistent, naming conventions drift, and monitoring is disconnected. Incident diagnostics spread across too many places, and the operational view becomes blurred. Teams spend time stitching context instead of solving problems. 2. An Unsustainable Human Workload The people who understand this infrastructure are both scarce and overloaded. They are asked to provision objects manually, review ACLs, check configurations, investigate drift, run failovers, and validate release plans. They also decode logs, triage incidents, and locate the source of message failures. Repetition becomes the norm, and heroics become the expectation. This is not a scalable operating model for a multi-platform estate. 3. Blind Spots in Risk and Compliance Most organisations can prove that “something happened,” but not necessarily “what happened,” “where it happened,” or “why it happened”. Regulators and audit teams want traceability, consistency, and evidence. Middleware estates rarely provide it. A fragmented environment makes even basic audit questions difficult. Who changed this configuration? Which systems participated in this transaction? Was the failure internal or external? Did messages retry, and was the security model consistent? These questions require coordinated visibility, which is difficult when data is spread across incompatible logs and systems. This gap is becoming more dangerous as regulations tighten around operational resilience. The Hidden Costs: Waste, Delay, and Defensive Operations. The consequences of this operating model are often underestimated because they are dispersed across many teams. Infrastructure Waste Most organisations cannot see true utilisation across all messaging technologies. They over-provision Kafka storage and leave unused queues and topics running for years. They maintain oversized clusters or duplicate environments because it is easier than cleaning up. Storage, compute, and licensing bills grow gradually. They are rarely challenged because no one has system-wide context. Slow Delivery and Change Friction Provisioning a new topic or queue should take minutes. In most enterprises, it becomes a mini-project involving approvals, compliance reviews, manual configuration, and cross-team coordination. Release cycles slow down not because of application development, but because of the plumbing beneath it. Incident Resolution Drag A business-critical slowdown might start in one platform and surface in another. Without visibility, teams chase symptoms. War rooms stretch into hours, and incidents that should be diagnosed quickly turn into cross-functional investigations. Mean Time to Recovery expands, and customer-facing systems suffer. Compliance Overhead Audit requests become painful exercises in log mining, screenshot gathering, Excel reconciliation, and interpretation. Evidence gathering interrupts real work. Compliance results take weeks. Reviewers lose confidence in the underlying controls, and findings start appearing in reports. These costs accumulate quietly but powerfully. A New Pressure Point: Auditability Has Become Strategic A decade ago, auditability was mostly an internal concern. Today it is a board-level conversation. Regulators across financial services, healthcare, energy, and the public sector now require organisations to prove the resilience and traceability of their operational systems. Messaging and streaming platforms sit at the heart of these systems. They remain some of the least auditable components in the digital landscape. Why Auditability is so Hard Today There is no unified audit trail. Kafka, MQ, Solace, RabbitMQ, and cloud brokers all produce different artefacts, and correlating them manually is slow and error prone. Configuration drift is constant, and even small changes create gaps in compliance evidence. Without unified configuration intelligence, drift remains invisible. RBAC inconsistencies multiply risk. Each platform has its own security model, and proving consistency across them is almost impossible manually. Incident reconstruction takes too long. When things go wrong, teams must recreate the past using logs from multiple systems, often with incomplete or misaligned timestamps. Compliance slows the business. Approvals, reviews, and evidence all take longer. This becomes a tax on every change and every release. Without built-in auditability, a middleware estate simply cannot operate at the speed the business requires. The Shift: Lean Operations as a Strategic Imperative Lean operations is not a slogan, nor is it about doing more with less. It is the recognition that the old operating model cannot sustain the scale, complexity, and regulatory expectations of modern middleware estates. A lean model has four defining characteristics. 1. Unified Visibility Teams need to see the entire estate in one place. This includes health, flows, dependencies, performance, lineage, configuration, and security. It means actual end-to-end operational clarity, not summaries or partial views. Without this, speed and reliability are impossible. 2. Automation and Controlled Self-Service Provisioning, validation, drift detection, ACL checks, failover routines, and compliance evidence should not rely on manual effort. Automation removes friction. Policy-based self-service allows developers to work faster without increasing operational risk. 3. Resource Optimisation A lean model gives clear insight into what is oversized, under-utilised, misconfigured, or simply no longer needed. The result is lower infrastructure cost, more predictable capacity planning, and fewer performance surprises. 4. Built-in Auditability Audit trails must be complete, consistent, and automatically captured. Configuration history must be reliable. Access models must be validated across platforms. Incident reconstruction must be fast, and evidence must be exportable without effort. Lean operations is what happens when you combine these principles. It is an operating philosophy supported by the right platform capabilities, not a tool. The Future State: Middleware as a Governed, Efficient, and Transparent Layer Organisations that embrace this model experience a radically different operational reality. Release cycles become smoother because provisioning and compliance do not hold them back. Outages become less frequent and shorter because teams can identify root causes quickly. Platform teams spend less time firefighting and more time improving. Infrastructure costs fall because utilisation is visible and manageable. Audit requests that once took days are delivered in minutes. Regulators gain confidence in the organisation’s operational discipline. The biggest shift, however, is cultural. Developers stop waiting for middleware teams. Middleware teams stop playing catch-up, and compliance teams stop battling for evidence. Everyone operates with the same truth, the same visibility, and the same level of control. This is the future state that progressive organisations are now moving toward. So What Makes This Future State Possible? Very few platforms are capable of supporting the operational model described here. Most observability tools focus on metrics rather than message flows. Most monitoring solutions are tied to a single platform. Integration tools typically manage connectivity, not operations. Open-source utilities provide valuable functions but lack governance, auditability, and cross-platform consistency. Cloud services help but introduce their own silos. To reach a fully lean operating model, organisations need something that is still rare. They need a unified operational command plane that spans every messaging and streaming platform in the estate. It must provide: A single operational view across all technologies. Transaction-level lineage and flow analysis. End-to-end audit trails. Consistent configuration and security governance. Automated provisioning and validation. Self-service within guardrails. Capacity and cost intelligence. Multi-cloud and hybrid compatibility. Integration with existing processes, not disruption of them. When these capabilities come together, the fragmented middleware world becomes manageable. It becomes transparent, and it becomes compliant. This is the model that forward-thinking organisations are now adopting. This is exactly the model made possible by meshIQ Core. meshIQ appears at the end of this story not because it is an afterthought, but because the logic leads naturally to it. Once you understand the operational, architectural, and compliance realities of modern messaging and streaming, the need for a unified control plane becomes obvious. meshIQ is one of the few platforms purpose-built to deliver it. For many organisations, it has become the turning point from reactive, high-cost operations to a lean, governed, and resilient operating model. Want to Explore This Further? If you want to understand how a lean operating model could apply to your own messaging and streaming landscape, meshIQ offers briefings and assessments for platform, architecture, and risk teams. You can start the conversation at meshiq.com/contact.

Europe's first National Security VC firm, MD One Ventures and Randox, a global diagnostics and healthcare company from the UK and Ireland, today announce the launch of Randox for Builders, a security and biotech incubator and accelerator. Randox for Builders gives early-stage companies the funding and hands-on support they need to grow faster.  At its core, Randox for Builders is about strengthening national resilience by developing technologies that will shape the future security and health of the UK and its allies.  By fast-tracking solutions with real-world impact, the incubator aims to ensure that the next generation of breakthrough capabilities is built, tested and deployed far earlier than traditional systems allow. Selected founders and their startups will gain access to Randox’s global leadership in diagnostics and biotechnology, leveraging resources rarely accessible to early-stage ventures, including: Access to advanced laboratories & manufacturing facilities    Clinical trials and validation Commercial partnerships Distribution channels Regulatory framework advice and support World-leading resources for IP and Research Deeply established routes to market - B2B and B2C Alongside investment, founders get instant access to a ready-made network of world-class scientific experts, R&D, and commercial resources.  The MD One Ventures team includes Co-founder Will McManners, who spent 10 years in the British Army, and served as an officer in a Specialist Military Unit, Commando and JTAC, before working at BlackRock, Investbridge Capital and Palantir. Alongside McManners, providing strategic oversight is Cecilia Fortugno, PhD, who serves as both Vice President and Chief Operations Officer at Randox Biosciences and the Senior Technical Advisor for the new accelerator. Wil McManners, Co-founder of MD One Ventures, commented: "This is not a traditional accelerator, it's a strategic partnership designed to de-risk and accelerate companies that solve national security and public health challenges. The access our founders receive to Randox's infrastructure is literally a money can't buy opportunity. We are looking to support world-class founding teams that can deliver solutions to fundamental issues affecting our National Infrastructure, Health and ultimately, Security". Dr Cecilia Fortugno, Vice President and Chief Operations Officer at Randox Biosciences and the Senior Technical Advisor for Randox for Builders, said: "For forty years, Randox has invested deeply in R&D to drive diagnostic and preventative healthcare. This program with MD Ones is a natural extension of that mission. By opening our technical infrastructure to the next generation of innovators, we are ensuring that the solutions to future health and security challenges are being built and scaled rapidly right now." The incubator has already started investing, with initial companies including Untap Health, which delivers automated wastewater-based diagnostics and Airfinity, which provides a health intelligence and bio risk forecasting platform, integrating AI-driven simulations.

As renewable generation expands and conventional baseload plants retire, electricity supply has become more volatile — amplifying price swings and increasing pressure on grid stability. Battery energy storage systems (BESS) address this imbalance by absorbing excess power when generation is high and discharging it when demand peaks. In doing so, they stabilise the grid, reduce renewable curtailment, and smooth electricity prices for both consumers and businesses. As a result, battery storage is now a bankable infrastructure asset. Today, Tier-one suppliers, primarily from China, offer containerised systems with performance warranties extending up to 20 years. Those guarantees underpin project-finance structures that can support up to 70 per cent debt financing — something that would have been unthinkable when the technology was still regarded as experimental. I spoke to Nikolas Samios, Managing Director, PT1, to understand the promise and opportunity of this rapidly evolving asset class. PT1’s thesis: Upgrading the physical world PT1 is an early-stage venture capital fund launched in 2018, focused on upgrading the physical world. Software and AI now underpin almost everything, but there is still a vast physical layer beneath that — energy systems, infrastructure, the built environment, robotics — that needs to evolve alongside it, and that’s where the Firm steps in. PT1 has made around 27 investments in Europe across two funds, and is headquartered in Berlin with a second office in London. It's now planning a third fund vintage for 2026.  PT1 focuses on three core areas: Electrification of everything, including energy, transport, and industrial systems. AI and robotics applied to the physical world, especially where labour shortages exist, and automation hasn’t yet penetrated — for example, construction. Infrastructure resilience, including climate resilience and the protection of critical assets such as grids and pipelines. The Firm doesn’t invest in defence per se, but Samois acknowledged that surveillance, maintenance, and monitoring of critical infrastructure is becoming increasingly important. Batteries inflection point Batteries often outperform gas-powered plants by responding faster, emitting nothing, and avoiding many of the siting and permitting constraints that plague thermal assets. “Batteries themselves are decentralised, modular, and quick to deploy. At scale, they also dampen peak pricing, lowering average electricity costs system-wide. That broader economic benefit helps explain the growing enthusiasm from both policymakers and investors.” Why PT1 followed the data, not the impact narrative Samios admits that PT1 were never impact-first investors. “Instead, we started by looking at the data.” Germany’s nuclear exit, coal phase-outs across Europe, and the acceleration triggered by Russia’s invasion of Ukraine have all amplified this shift toward battery storage. According to Samios, the second major driver was cost: “Battery prices fell much faster than even optimistic forecasts from consultancies like McKinsey or BNEF. Chinese industrial policy created massive manufacturing capacity, pushing prices down week by week. When you combine grid volatility with falling battery costs — and layer in AI-driven optimisation — you reach a point where battery storage becomes commercially viable without subsidies. That was the key insight: this could become a new, standalone asset class. One that large institutional investors would eventually allocate to, once risk and bankability were proven.” He admits that traditional renewable infrastructure has largely been commoditised. But, it’s increasingly hard for infrastructure funds to achieve double-digit internal rate of returns (IRRs) in solar or wind without taking emerging-market risk: “Battery storage changes that. It benefits from volatility rather than being harmed by it. And as volatility is structural, not temporary, the opportunity persists for decades.  Our role as an early-stage VC is to identify these asset classes early, take the initial technology and execution risk, and help companies mature to the point where they can absorb large pools of institutional capital.” For Samios, where innovation really happens now is in system control and trading: “Early battery projects followed a simple arbitrage model — charging during periods of excess solar and discharging at night.  Today, AI-driven trading systems analyse vast volumes of real-time data across multiple European electricity markets, optimising decisions in 15-minute intervals. Machines now outperform even the best human traders in this context, because they can process weather patterns, grid constraints, outages, and market signals simultaneously. That intelligence directly translates into higher returns on the same physical asset.” The Texas oilman test  Battery storage plays a different role in energy trading because it does not rely on subsidies in most markets.  According to Samios:  “That insulation from political swings is critical. At PT1, we focus on climate technologies that have reached a commercial tipping point — where they make economic sense even to conservative investors. A simple test we use is whether a Texas oilman, advised by Goldman Sachs and McKinsey, would invest purely on financial grounds. If the answer is yes, scale follows — and with scale comes impact. Battery storage passed that test.” Early conviction, institutional scale In just one week this September, two portfolio companies from PT1, Terra One and Voltfang, secured €1 billion to finance large-scale battery projects in Germany.  PT1 was one of the first institutional investor in both companies back since 2022, spotting the need for grid-scale storage before it became mainstream.  Terra One raised €150 million mezzanine financing, triggering an additional €500–600 million in project debt from banks, unlocking €750 million for ~3 GWh of new capacity “This is enough to power 20 per cent of German households for one hour,” shared Samios.  German battery specialist Voltfang launched a long-term partnership with infrastructure investor Palladio Partners to develop, finance and operate large-scale battery storage systems across Germany, targeting around €250 million in investments by 2029. This scales Europe’s largest second-life battery factory into repeatable grid projects. Flexibility is critical for risk mitigation PT1’s investment in Voltfang reflects the firm’s view that flexibility is a core form of risk mitigation in energy storage. From a venture perspective, Samios argues that the appeal lies in business models that are not locked into a single supply pathway.   “What we like about companies such as Voltfang is flexibility,” he says. “They’re not solely dependent on second-life batteries; they’ve built systems that can integrate batteries from multiple sources — unused first-life inventory, surplus stock, and second-life EV batteries that still retain 80 to 85 per cent of their original capacity.”  For stationary storage applications, energy density is far less critical than it is in vehicles, making second-life batteries particularly compelling.  This multi-source strategy improves supply resilience, lowers the carbon footprint of storage systems, and strengthens the overall investment case — especially for customers with explicit sustainability targets. Samois believes that in more liberalised markets in Germany, Australia, and parts of the US, private capital is clearly leading. Renewable energy created a globally investable infrastructure class, and battery storage now fits naturally into that same capital pipeline. “Battery projects can be structured very similarly to solar or wind assets, but with higher returns. That’s why large investors—pension funds, insurers, multi-asset managers—are moving quickly once bankability is demonstrated.” In contrast, gas peaker plants (power plants that generally run only when there is a high demand) require state guarantees to be investable, because they sit idle most of the time. Batteries operate autonomously, generate revenue continuously, and stabilise the grid without public subsidies. A broader European momentum builds behind storage Beyond the investments of PT1, over the past year, a wave of funding rounds and acquisitions has underscored growing investor confidence. In 2024, Swiss startup Libattion, which builds stationary energy storage systems using upcycled electric vehicle batteries, secured €14 million in funding, reflecting rising interest in circular and second-life battery solutions. Momentum has only increased in 2025. In January, large-scale battery storage developer green flexibility raised over €400 million to deploy utility-scale battery storage systems across Europe, marking one of the sector’s largest infrastructure-backed investments to date. Young company Scale Energy, developing decentralised industrial battery storage systems, raised a €2 million Seed round in February this year. There’s also momentum with companies like Delta Green which aims to turn ordinary European homes into a virtual power battery, enabling households to shift consumption, discharge batteries, and export rooftop solar at times of peak demand. However, this is a sector requiring deep domain expertise. You need founders who understand complex systems—regulation, infrastructure, financing, and often have decades of industry experience. According to Samios, the strongest teams combine that expertise with entrepreneurial ambition.  “Our role is not to build companies from scratch, but to support excellent teams with market access, strategic insight, and connections to later-stage capital. That’s where PT1 adds value: helping companies become bankable, scalable, and relevant to the largest capital pools in Europe.” Lead image: An edited Voltang battery storage photo.