In 2026, many startup founders are facing the same uncomfortable truth. Their product may be technically solid, and their team may be shipping fast, but growth stalls the moment AI agents become the first touchpoint in the customer journey. The interface has changed, and with it, so should we.

In previous years, you optimised for the App Store or Google search. Today, AI agents, AI-first browsers such as Atlas, and workflow tools inside Slack, Teams, and Notion are the default interfaces for knowledge and software. The first user of your product is now an AI system deciding whether humans will ever see you. If AI agents cannot understand or operate your product, you become invisible, no matter how good the human UX is.

As a result, you need to optimise for the AI layer that sits between you and your customer. But how do you speak the language that teams care about? You become AI-native.

Becoming AI-native is one of the best chances for startups to punch above their weight against incumbents. To help you get ahead of the market, this piece offers a practical definition of AI-native, a simple self-assessment blueprint, and a founder’s view on what needs to change in hiring, team structure, and culture in this new AI-powered era.

What AI-native actually means in practice

AI-native is a confusing term. Most startups have integrated some form of AI to speed up their day-to-day operations. That is not being AI-native. That is being AI-enhanced. The difference is fairly straightforward.

• AI-enhanced: This is internal. AI is used inside your company to speed up work, but the product itself still assumes a human user.
• AI-native: Your product is built so that AI systems outside your company can reliably read, query, and act on it.

Essentially, AI-enhanced makes you faster, while AI-native makes you discoverable and interoperable. The difference is fundamental to how you operate as a business, from messaging to product design, sales, marketing, and partnerships.

How to be AI-native

So how can you tell whether your product is AI-native or not? Here is what you need.

Machine-consumable surfaces

• Consistent structured outputs, stable schemas, and robust APIs.
• Semantic clarity with clear names, types, and contracts so agents can reason without hacks.

Documentation and knowledge for machines

• Documentation and FAQs written so that LLMs can parse them. They should be up to date, structured, and low in ambiguity.
• Internal knowledge formatted as graphs, schemas, or clean text, not just slide decks.

Agent-friendly interfaces

• Interfaces that support programmable navigation through links, IDs, and action endpoints, rather than relying only on visual affordances.
• Clear ways for agents to trigger workflows and retrieve results without scraping pixels.

Workflows optimised for AI decisions

• A default assumption that an agent will orchestrate multiple steps, not a human clicking through screens.
• Predictable timings, idempotent actions, and observable states so agents can recover from failure.

Predictability and clarity in responses

• Stable response shapes and clear error modes so agents can integrate once and trust the system.
• Think contract testing for agents, not just something that is good enough for a human reading a blog.

As you can see, becoming AI-native is a fundamental structural choice. It cannot be an add-on or a feature.

How startups can win big

You might be thinking that this gives startups a massive advantage over incumbents, and you would be right.

Startups do not have to overcome legacy systems. They are not carrying ten years of UI conventions, data debt, and one-off integrations. They can design clean schemas, transparent logic, and agent entry points from day one. Startups also tend to have smaller teams, which enables cheaper and faster experimentation with schemas, APIs, and AI-facing documentation.

This means startups can regularly test how well AI agents route to them in real workflows. In incumbents, everything runs through committees. They cannot pivot quickly, and they cannot test in the same way.

We have already seen this at Tastewise. When ChatGPT’s browser, Atlas, launched, many competitors had to scramble to adapt their content to this new AI-driven environment. Tastewise had already built an approach designed to thrive in AI environments, which put us in a strong position to scale in this new era.

AI agents tend to choose their preferred tools and stick with them. If you become an AI agent’s go-to option in your category, your ability to scale increases rapidly, as the agent does much of the heavy lifting. By making this transition early, you position yourself ahead of the industry and ahead of major changes that will shape it going forward.

Five questions to ask yourself

1. Can an AI agent understand what our product does from our public documentation in under 30 seconds?
2. Are our main outputs and events available as structured data with stable contracts?
3. If a copilot inside a customer’s workspace searched for tools like ours, would it find us and know how to call us?
4. Do we know which parts of our product are hardest for a machine to interpret today?
5. Is there a named owner responsible for AI legibility across product, documentation, and data?

If a few of these questions made you uncomfortable, that is a useful signal. Most teams are still designing for humans and hoping AI agents will improvise around the gaps. They will not. The shift to AI-native starts inside the company, long before it appears on your roadmap or homepage.

What changes inside your company

Hiring: An AI-native product needs fewer people obsessing over pixels and more people obsessing over structure. You want engineers who think in contracts, schemas, and events, not just screens. You want product managers who understand how LLMs read, rank, and chain calls. You also want people who enjoy naming things clearly and documenting why systems behave the way they do.

Front-end work still matters, but it sits on top of a stable, machine-readable core. When you are AI-native, the surface is the final layer you polish, not the only layer you invest in.

Team structure: Instead of organising purely around features, you begin organising around knowledge surfaces. For example, one team might own pricing logic and every surface where pricing appears, including APIs and documentation used by agents. Another might own customer state and lifecycle events and expose them in predictable ways. Another might own documentation, taxonomies, and examples and treat them as a product.

Each team has a clear mandate. Humans should understand their domain, and AI agents should be able to navigate it without hacks.

Culture: AI-native is a mindset as much as a technology stack. In practice, that means writing documentation and internal notes with headings, definitions, and context that a model can follow without guessing. It means treating internal decisions as things that will be read later by both a machine and a new teammate. It means defaulting to observable systems where you can explain, in plain language, what happened when an agent interacted with your product.

Transparency stops being a slogan and becomes the way you make your product legible to both humans and machines.

Why this becomes your edge

When AI browsers and agents started to matter, many companies discovered they had a visibility problem. Their content was locked in formats that worked for humans and little else. They had to rush to restructure their knowledge so agents could even find them.

At Tastewise, we felt the advantage of building for AI consumption early. When tools like Atlas entered the picture, our structured, machine-friendly approach meant AI environments could use our outputs without a rebuild. That did not make us smarter than our competitors. It meant we had done the groundwork.

The same opportunity exists for any startup willing to design for AI as the first user.

AI-native as the default

Over the next few years, AI agents will scan your documentation, test your APIs, compare you to alternatives, and decide what to surface to the humans you care about. Human UX still matters, but AI UX determines whether anyone ever sees that beautiful interface.

Start small. Pick one area of your product, make it fully legible to an AI agent, and give someone ownership of that work. Then repeat.

The real question for 2026 is simple. When an AI system looks at your product, does it know what to do with you? If the answer is yes, you are already ahead.

The post What AI-native means for startups in 2026, and why it is not just for big tech appeared first on EU-Startups.

There are around 350,000 people diagnosed with primary brain cancer every year, and 250,000 people die from it. Despite decades of progress in medicine, the tools primarily used to access, diagnose, and treat the brain remain limited — until now.

Robeauté is a Paris-based MedTech startup developing a new class of therapeutic microrobots designed to diagnose, treat, and monitor the brain with unprecedented flexibility. Operating at the intersection of robotics, physics, materials science, chemistry, biology, and medicine, the company has developed a modular medical device built around a universal robotic core with interchangeable micro-extensions.

I spoke to co-founder and COO Joana Cartocci to learn all about it. 

From targeted drug delivery to live data collection

Roughly the size of a grain of rice, Robeauté’s microrobots can navigate curved, non-linear paths through the brain’s extracellular matrix, safely reaching multiple sites of interest.

Depending on the pathology, each device can be equipped for a specific mission — delivering therapeutic molecules, implanting electrodes, or collecting cellular and live data via embedded sensors.

This modular architecture allows a single platform to be adapted across a wide range of clinical applications, from tissue sampling and targeted drug delivery to electrode implantation and real-time data collection from deep within the brain — opening new possibilities for both treatment and understanding of complex neuropathologies.

From extreme environments to the human brain

Robeauté’s founder, Bertrand Duplat, spent more than 30 years working in robotics, including at McGill University and the European Space Agency, specialising in robots designed for extreme environments. Earlier in his career, he also founded 3D software company Virtools, which Dassault Systèmes later acquired.

After decades working on undersea, nuclear, space, and archaeological robotics, Duplat decided to apply his expertise to medicine — a decision catalysed by his mother’s diagnosis with glioblastoma. He went on to found Robeauté with co-founder Joana Cartocci, an operations specialist.

“He had been doing robotics for 30 years in extreme environments and decided to put that experience to good use,” Cartocci said.

“That’s around the time we met. He asked me to join and lead operations — everything that’s not scientific — and we’ve been at it since 2017.”

Why most academic microrobots never leave the lab 

Crucially, Cartocci comes from an operational background. She notes that a lot of founders spin out of labs and struggle with the transition to entrepreneurship.

"It’s a completely different skill set. Time management, identity, and team alignment — those things make or break a company. My role is to take that weight off my co-founder so he can focus on what he does best: the technology. And get it to patients as fast as possible.”

Designing for control, not magnets

According to Cartocci, in most academic labs today, microrobots remain largely passive tools — probes or magnetic particles set in motion by very large external electromagnetic coils. “It’s extremely hard to scale,” Cartocci explained, “and it doesn’t give surgeons much confidence when it comes to control.”

Robeauté takes a fundamentally different approach. Its system is built around a tiny, active device composed of two parts: a carrier and an extension.

“The carrier contains our core technology,” Cartocci said.

“It’s what allows the robot to move non-linearly through the brain, while being continuously tracked with ultrasound, so we always know exactly where it is.”

The extension, meanwhile, is what defines the medical task itself. “That’s where you specify the pathology or the intervention,” she explained.

“It could be a biopsy tool, an electrode, a drug-delivery mechanism — whatever needs to be delivered locally.

It’s about the ability to reach multiple sites through non-linear routes and to do so with the right level of accuracy. That fundamentally changes how neurosurgery is performed today.”

The benefits of not being first 

Other companies that have tried to industrialise this academic approach— passive probes moved electromagnetically.

“They started before we did, so they had a first-mover advantage, but they’re struggling now, " shared Cartocci

“They’re not bankrupt, but they’re on the downfall. Our approach is entirely different, and we were always very clear about what we deliberately didn’t go for, because we knew it wasn’t the right solution.”

There are also companies doing microrobotics in vascular environments rather than directly in brain tissue. But none are at the maturity level of Robeauté, particularly in regulatory engagement and strong relationships with surgeons.

Why incremental innovation isn’t enough

Cartocci speaks of the urgency of the kind of medtech her company is developing against companies which add incremental value which are hard to mobilise real change around.

"Being less disruptive means adding even less value.  At Robeauté, we’re clearly in the transformational category. People are dying. 

The unmet need is enormous.

A billion people are affected by brain disease. The drug market alone is around $72 billion. Electrodes are now valued in the trillions if you include healthy patients.

The biopsy market is $4 billion. Brain–computer interfaces are now in the multiple billions. There’s so much that requires precision — having one tool that opens all doors — that unlocks a completely different way of performing brain surgery. We see tremendous excitement from the medical profession.”

Finding the right investors by letting go

In January 2025, Robeauté raised $28 million in funding. Cartocci describes the fundraising experience as “traumatic.”She admits.

“It’s brutal. But at some point, you reach a kind of surrender. You know your company, your value, your pitch — and the more you try to control things, the more frustrating it becomes. When you let go and surf the waves, you find the right people.

I spoke to another founder recently who described it as “conjuring destiny.” That resonated. You prepare, you show up, you’re relentless. You don’t take rejection personally. Everyone comes with their own constraints.”

Her biggest takeaway was learning to enjoy speaking to investors — understanding their language. “That wasn’t always the case.”

Following funding, Robeauté doubled its team, which, according to Cartocci, felt incredible:  “Before, it felt like we were constantly being held back. Now there’s space for iteration, greater experimentation, error, and proper building. 

Europe’s regulatory fragmentation problem

The company opened its US subsidiary, which is critical for go-to-market and clinical trials. The US will be its first market, which   Cartocci attributes primarily to regulations:

“In the US, you co-create your regulatory strategy with the FDA. In Europe, you complete everything, submit the file, and wait a year with no feedback.

For a platform technology like ours — moving from one indication to another — the US model makes a huge difference. Once you have FDA data, it becomes a blueprint for Europe.”

She also highlighted Europe’s fragmentation as problematic — “It would take as much time and money to open France as it would to open the entire US.”

That said, Robeaute is European, and as  Cartocci shared, “we care deeply about bringing this back here. Our investors believe in Europe and don’t want to just flip everything to the US. Healthcare values matter. If we don’t feed technology back into public systems, we push them toward the American model — and that’s a failure.”

For Cartocci, commercial brain microrobots are closer than they’ve ever been.

“We need collective effort — investors, developers, regulators — to make sure these solutions reach the market. We’re also hiring: scientific and technical roles, quality and regulatory roles, US go-to-market roles, and engineering roles. It’s a great time to watch — or join — the company.”

The company's next goal is first-in-human studies by the end of 2026.

Lead image: Robeauté. Photo: uncredited. 

Looking back at the evolution of healthtech, many revolutionary startups have been built, often completely shifting the way clinicians work. We have moved from digital record keeping to telehealth platforms, preventative healthcare devices, and now the latest wave of AI co-pilots. Decades of investing in digitisation have consequently turned healthcare into fertile ground for AI applications and automation.

Despite all that digitisation, the industry is still drowning in bureaucracy. Doctors spend almost as much time on admin as they do with patients. Filling forms, writing notes, juggling schedules, and navigating claims is inefficient, expensive, and a massive drain on the system’s capacity. AI can change that – not by replacing clinicians, but by taking the heavy administrative load off their shoulders.

According to the World Economic Forum, AI has the potential to bridge the gap for the 4.5 billion people who lack access to basic healthcare, and to help address the expected shortage of 11 million healthcare workers by 2030. Yet despite this potential, healthcare remains below average in its adoption of AI compared to other industries. That is especially striking when you consider that healthcare generates an estimated 30% of the world’s data, yet 97% of hospital data goes unused. The time is now to make use of that data by implementing AI and fully leveraging the opportunity.

Why remove admin from healthcare workers?

Healthcare services are, as we know, heavily dependent on a highly skilled workforce. Out of the €1.6 trillion spent on healthcare in Europe each year, roughly 50% goes to salaries. We do not think we can – or should – replace these individuals. They are doing astonishing work, and we are far from leaving our destinies in the hands of AI. But these healthcare professionals should be able to spend less time on admin and more time with patients. That is not just an efficiency play; it is also a staffing play. Burnout and workload are core drivers of shortages, and freeing clinicians from unnecessary admin is one of the most realistic ways to expand care capacity without needing millions of new hires who simply do not exist.

Why now?

Europe spends around €300 billion on healthcare administration every year. For the first time, we have the right technology to tackle this huge opportunity and, importantly, to generate meaningful societal impact at the same time. AI solutions are already automating everyday tasks like appointment scheduling, intake, documentation, and claims, with the potential to cut a meaningful share of healthcare spending. So how can AI transform the administrative workload of clinicians?

There are three areas that I believe have a high potential for AI to disrupt:

1. Patient scheduling: Out of administrative costs in European healthcare, about €90bn is spent on calls, booking and rescheduling appointments – where automated scheduling could make a major difference. Checking staff and patient availability, room capacity, back-and-forth around no-shows, and constant calendar updates is a fiddly task that consumes huge amounts of time.

AI can handle this end-to-end, and we see clear potential for verticalisation. Solutions like Roger specifically address dental practices; Wawa Fertility in the IVF space; and Vocca, which is moving to a cross-speciality offering. As AI voice continues to improve, we expect many more companies to verticalise in this space.

2. Patient record management: Maintaining and updating electronic health records. This market is estimated to account for roughly €65 billion annually across European healthcare practices. We have seen a boom in so-called AI scribe startups that record conversations between patients and doctors and fill out health records automatically, shifting a doctor’s attention away from the keyboard and onto the patient.

Companies like Abridge, Nabla and Tandem Health are early examples, while uncovr and Sonia are younger and, for now, more verticalised solutions. We believe the next wave of companies in this space will go beyond documentation, addressing more complex tasks and managing multimodal, end-to-end workflows for clinicians. The most influential healthcare AI companies will not just transform a single segment; they will reshape multiple interconnected workflows across the broader healthcare ecosystem.

3. Medical billing and claims: A lot of time is spent by healthcare and back-office professionals entering the correct codes. There is an estimated €50 billion to be freed up here, where AI can process large volumes of data quickly, likely with greater precision and fewer errors. Platforms like Nelly and Phare Health are already shaking up the billing process, and this category feels overdue for automation.

Long-term, new start-ups will likely push forward along the lines of a few different themes. Firstly, companies that build solutions on top of data that has historically been unused, like the 97% of hospital data mentioned earlier. Second, end-to-end multimodal technologies that integrate several data types into coherent workflows and deeply embed into clinical workstreams. And thirdly, domain-specific solutions that target particular verticals rather than staying broad and high-level. That is where we expect both the biggest efficiency gains and the deepest, most durable impact.

An evolving ecosystem

There are still real constraints today: nuance, edge cases, and trust in clinical settings cannot be solved overnight. But the technology is catching up fast. If we make healthcare more efficient, the wider world works better. Few markets touch all of us, and as populations age and disease burdens rise, this is one of the most important arenas for AI to deliver impact without increasing cost: a rare and timely proposition for an overextended system.

The post AI for healthcare admin: Meet the startups that are providing the right tech at the right time appeared first on EU-Startups.

Europe’s DeepTech ecosystem is approaching an important crossroads. Across the continent, universities and research institutes continue to generate breakthrough science, supported by one of the world’s strongest pipelines of technical talent. The ingredients for globally relevant DeepTech companies are already present.

Japan’s recent commitment of more than €33 billion to European DeepTech and AI highlights this potential from the outside. It is a reminder that Europe has the scientific strength to shape future industries but has yet to fully prove that this research can consistently translate into scaled companies. Whether the region can build true technological sovereignty will depend on how effectively founders, investors, and institutions adapt to the long horizons and structural demands unique to DeepTech innovation.

The paradox facing European DeepTech

Forecasts suggest that European DeepTech could generate around one trillion dollars in enterprise value by 2030. While DeepTech as a whole is attracting a record share of European funding, early-stage rounds are down by 30% since their peak in 2021.

This also happens to be the stage where founders face some of their toughest hurdles. Long development cycles, highly technical milestones, and limited commercial traction make traditional venture investors hesitant. Many VCs acknowledge the importance of DeepTech but still default to evaluating companies through software-style metrics.

Japan’s current strategy highlights this gap. Its investment vehicles target Europe’s scientific innovation while offering industrial scale, manufacturing experience, and long-term capital. With the right investment structures, Europe can pair its research excellence with pathways that support long-cycle technologies. 

Europe’s structural advantages

Europe enters this moment with real strengths. Technical education is one of them. Across the EU, about one quarter of all Master’s degrees are awarded in STEM subjects. In countries such as Germany, more than one-third of tertiary graduates hold a STEM qualification. These numbers reflect a strong pipeline of technical talent.

Funding support is another advantage. Europe offers substantial grant programmes for early DeepTech work. The European Innovation Council provides grants of up to €2.5 million, along with potential equity financing. The EIC Pre-Accelerator supports smaller DeepTech organisations through grants of €300,000 to €500,000. These resources exist in meaningful volume. The challenge is not their availability, but how effectively founders integrate them into an investment strategy.

What founders can do to succeed

Founders can take several concrete steps to make an early-stage DeepTech fundable.

1. Teams need to speak both technical and commercial languages: When a company frames progress only through research milestones, investors struggle to understand how development connects to market outcomes.
2. Grants should be approached as essential capital: Competitive grant wins validate technical merit and extend runway, and they work best when paired with early venture support. For VCs, non-dilutive funding reduces risk perception and strengthens conviction around long-cycle technologies.
3. Create intermediate milestones that are understandable beyond specialist audiences: These may include paid pilots, environmental tests, regulatory-aligned prototypes, or limited-scope deployments with industry partners. They demonstrate motion, even if they are not final products.
4. Early commercial relationships matter: Many corporates in Europe sponsor pilots or co-development projects. Several major European companies, including Atos and Siemens, already operate innovation programmes that support early technical validation. These partnerships give founders something tangible to show investors.
5. Translate research timelines into business timelines: Industry expects predictable communication and steady progress. The scientific world allows long, exploratory cycles. Commercial environments do not.

Where European VCs can strengthen the early-stage environment

Europe’s venture ecosystem has an opportunity to evolve in parallel with the region’s scientific strengths. Generalist funds already play an important role at the earliest stages, yet specialised DeepTech VCs often benefit from investors who can draw on technical networks, industrial partners, or sector-specific knowledge. Expanding this pool of expertise, especially around seed and Series A, would create clearer roads for founders working on long development timelines.

Patient capital is also part of this shift. Many DeepTech companies progress through stages that resemble life sciences, where well-defined milestones and longer horizons are standard. European funds are already beginning to adopt elements of this approach as hard technology becomes more central to the economy. Life sciences investors in the United States have adapted to similar challenges, and their experience offers a useful reference point for European DeepTech venture capital as it develops its own investment frameworks. 

Europe’s sovereignty opportunity

Europe’s position in DeepTech relates directly to technological sovereignty. Japan’s investment programme shows that global actors see European research as a foundation worth building on at an industrial scale. This external confidence should motivate European institutions and investors to improve the early-stage environment rather than assume that technical excellence will translate into commercial leadership on its own.

The ingredients already exist. Europe has strong universities, a large population of STEM graduates, and grants that actually deploy capital. What is missing is alignment between founders who build long-cycle technologies and investors who can support them with expertise, patient capital, and structured pathways to commercial validation.

Final thoughts

DeepTech momentum in Europe depends on two shifts happening at the same time. Founders can increase their chances of success by building teams that translate science into market progress by using grants strategically and forming early partnerships. Investors can support this transition by expanding their technical networks, adopting long-term horizons, and developing evaluation frameworks suited to DeepTech trajectories. 

If both sides move in this direction, Europe can turn its scientific strengths into companies that scale and play a defining role in the next generation of global technology.

The post Can Europe become the global centre of gravity for DeepTech? appeared first on EU-Startups.

Alvotech, a Reykjavík-based BioTech company specialising in the development and manufacture of biosimilar medicines, has secured a €100 million senior term loan facility to strengthen liquidity and support the execution of its strategic priorities in 2026.

The term loan facility bears an interest rate of 12.50%, payable monthly in cash, and has a maturity date of 2 years. The transaction, led by GoldenTree Asset Management, replaces the company’s previously disclosed working capital facility (ABL) and provides Alvotech with access to the full €100 million for the duration of the loan term. The structure offers enhanced operational flexibility, Alvotech claims. 

“This €100 million financing underscores the long-term commitment of our financing partners at GoldenTree and their alignment with Alvotech’s strategy. Their support strengthens our ability to execute on our growth plans, invest in R&D, and deliver high-quality biosimilars to patients worldwide,” said Robert Wessman, Chairman and CEO of Alvotech.

Founded in 2013 by Wessman, Alvotech aims to be a global leader in the biosimilar domain. “A biosimilar is a biologic medicine that is highly similar to and has no clinically meaningful differences from an existing approved biologic medicine, or reference product. Biosimilars (like reference products) are produced in living systems,” Alvotech explained on its website. 

According to the Icelandic company, five biosimilars are already approved and marketed in multiple global markets, including biosimilars to Humira® (adalimumab), Stelara® (ustekinumab), Simponi® (golimumab), Eylea® (aflibercept) and Prolia®/Xgeva® (denosumab).

Its current development pipeline includes nine disclosed biosimilar candidates aimed at treating autoimmune disorders, eye disorders, osteoporosis, respiratory disease, and cancer.

In December 2025, Alvotech announced the successful placement of €91.9 million ($108 million) senior unsecured convertible bonds due 2030. Additionally, in June 2025, the company announced the repricing of its existing facility to an interest rate of SOFR plus 6.0% per annum, equivalent to approximately 9.8% based on the 30-day average SOFR rate of ~3.8%. In June 2024, it announced the successful arrangement of a strategic refinancing transaction maturing in June 2029, also led by GoldenTree Asset Management. 

Alvotech stated that its R&D pipeline currently includes 30 products in development. The company is expanding its production capacity and strengthening its supply chain to support four global product launches planned through 2026.

The company claims to have established a network of commercial partnerships to support market access in regions including the United States, Europe, Japan, China, other Asian countries, and parts of South America, Africa, and the Middle East.

The post Icelandic BioTech Alvotech secures €100 million term loan facility to bolster liquidity and R&D investment appeared first on EU-Startups.

Pragmatech, a Spanish HealthTech startup focused on AI solutions in pharmacology, microbiology, and infectious diseases, has raised €650k in funding to boost the commercial deployment of iAST®, its antibiotic prescribing software that obtained CE marking in July 2025. 

The round was led by First Drop, which invested €300k, and included Urriellu Ventures with €175k. The round also comprised an ENISA loan of €180k and the conversion into equity of €162,500 in convertible notes from a previous round.

Pragmatech is an Oviedo-based startup founded in 2021. According to the company, its flagship product, iAST®, is the first CE-marked AI-powered software designed to support clinical decision-making in antibiotic prescription. It has been clinically proven to reduce prescribing errors and recommend treatments with lower resistance potential, it claims. 

“With this funding round, we are strengthening our ability to bring iAST® to the hospitals and healthcare professionals who need it most. The support of investors like First Drop and Urriellu Ventures reinforces confidence in our technology and the positive impact it is having on clinical practice,” said Javier Fernández, co-CEO of Pragmatech.

With a B2B model, the company aims to lead the digital transformation of infectious disease management, enhancing patient outcomes and healthcare system sustainability.

Pablo Valledor, co-CEO and CTO, said, “This funding not only enables us to roll out iAST® at a commercial level, but also provides us with the resources necessary to continue refining our products and to ensure that artificial intelligence supports clinical decision-making in a safe and effective manner.”

The post Spanish HealthTech Pragmatech raises €650k to roll out its CE-marked AI antibiotic prescribing software appeared first on EU-Startups.

No company can afford to innovate in isolation. The speed at which new technologies and business models emerge often outpaces the traditional R&D cycle of large organisations. To bridge this gap, a growing number of corporates have created a dedicated role: the Innovation Manager. 

The idea of formal innovation management emerged in the 2000s, influenced by Henry Chesbrough’s concept of “open innovation”. Corporations realised that ideas and technologies often come from outside their own labs. To capture this potential, they began appointing professionals whose sole responsibility was to scout, test, and scale external innovation. Today, the Innovation Manager has become a key figure in large organisations, especially in industries under high pressure from digital disruption, sustainability targets, or new consumer expectations.

What an Innovation Manager actually does

The role is far more than startup scouting. Innovation Managers are strategic translators between the outside world and the corporate core. Their responsibilities typically include:

• Trend & Technology Radar: Systematically monitoring global innovation trends, new technologies, and business models. This involves attending startup events, working with accelerators, scanning patents, and engaging with universities and research institutions.
  
• Strategic Alignment: Mapping external opportunities against the company’s long-term priorities. Not every exciting startup is relevant. Innovation Managers filter based on corporate strategy, market positioning, and concrete business challenges.
• Startup Engagement & Pilots: Designing collaboration formats, from hackathons to proof-of-concept projects. They act as “dealmakers,” negotiating pilot conditions and coordinating across procurement, IT security, and legal teams.
  
• Internal Evangelism: Building an innovation culture inside the company. This means creating awareness among business units, training colleagues to work with startups, and overcoming resistance to change.
  
• Scaling & Integration: Ensuring pilots don’t remain isolated experiments. Innovation Managers push successful solutions into the mainstream business and measure their impact on KPIs such as cost savings, customer satisfaction, or new revenue streams.
  
• Governance & Reporting: Tracking the performance of innovation initiatives, reporting to top management, and justifying budgets in terms of ROI and strategic relevance.

An Innovation Manager brings together a diverse set of competencies. At the core is a strong business understanding, which allows them to align external innovations with corporate strategy and market needs. This is complemented by solid technological know-how, enabling them to assess new solutions and engage with startups on equal terms. Also important are change management skills, since innovation often faces resistance and requires building internal acceptance.

Building an Innovation Culture

Of course, in practice, innovation is about much more than launching pilot projects. It starts with company culture. An Innovation Manager is therefore not only a scout of external trends and startup partnerships but also an architect and amplifier of an innovation-friendly culture inside the organisation. The key challenge is to ensure that innovation becomes part of everyday work and decision-making.

With more than 30 years of experience in the energy industry, Daniela Hertzer, Innovation Manager at LEAG, explains: “Innovation management means challenging routines and the apparent certainties of existing solutions. Those who want to establish something new do not always encounter enthusiasm, and that is precisely where the task lies: creating spaces and work processes in which change is recognised and accepted not as a risk, but as an opportunity.”

Creating an environment where innovation becomes a continuous organisational mindset is one of the Innovation Manager’s most important contributions. Such a cultural foundation is essential; without it, even the most carefully designed pilot projects risk remaining isolated experiments rather than evolving into catalysts for systemic transformation.

The rise of the Innovation Manager marks a shift from closed R&D toward open collaboration. These professionals link corporate priorities with external innovation, turning promising ideas into concrete business outcomes. Innovation today is less about inventing alone and more about building the right connections, a mindset that will define which companies stay ahead in the long run.

Disclaimer:

Responsibilities and focus areas of Innovation Managers vary significantly between companies. Depending on organisational structure, industry, and strategic priorities, the role may encompass different functions. Infineon defines the Innovation Manager role in its own way, just as every company interprets and implements it differently. The section above is intended to provide a general overview of what the work of an Innovation Manager can look like.

The post From scouting to scaling: How innovation managers drive corporate transformation appeared first on EU-Startups.

Let’s talk about something your sales team probably isn’t doing but should be: turning social media into an actual revenue stream.

Most companies limit their social media presence to generic updates that generate little engagement. But real impact comes from using these channels strategically, to create a pipeline, book qualified meetings and close actual deals. The challenge is that many B2B sales teams still see social media as a secondary tool, something owned by marketing, rather than a core driver of business growth.

But here’s what the top performers already know: social selling isn’t replacing your outbound strategy. It’s supercharging it.

Why your cold emails are getting ignored (and what social can do about it)

Today’s prospects are overwhelmed by generic outreach messages that all sound the same.

“Hey [First Name], I noticed your company…” messages that clearly came from a template. LinkedIn connection requests that immediately pitch. 

The average decision-maker gets 100+ sales emails per day. Your message is competing with 99 others, and most of them sound exactly the same. Social media, however, offers a completely different dynamic.

When someone sees your helpful content on LinkedIn before you reach out, you’re not a random salesperson anymore. You’re that person who shared that insightful post about scaling sales teams. The one who commented thoughtfully on their company announcement. You’ve built credibility before the conversation even starts!

The modern sales playbook: content + automation

Here’s what smart sales teams are doing right now:

Instead of posting sporadically, they focus on building a consistent presence that positions them as trusted experts in their field, engaging with prospects in a genuine and value-driven way. 

And they’re using AI tools to scale the parts that don’t require a human touch, like finding the right leads, enriching contact data, and automating initial outreach. Consider how much time your sales team spends each week on tasks like:

• Researching potential leads
• Finding verified contact information
• Manually updating your CRM
• Writing personalised outreach messages
• Following up with prospects who went silent

The content that actually moves deals forward:

Before we get into automation, let’s talk about the content part. Because you can’t automate your way to credibility, you have to earn it.

Share insights, not ads. The salespeople winning on LinkedIn aren’t posting about their product features. They’re sharing market trends, industry challenges, and tactical advice that their prospects actually care about.

If you sell to CFOs, post about cash flow management strategies or navigating economic uncertainty. If you sell to sales leaders, share what you’re learning about AI adoption in sales or building high-performing teams.

Engage before you pitch. Before reaching out to a prospect, spend two weeks engaging with their content. Like their posts. Leave thoughtful comments. Share their insights with your network. When you finally send that connection request, you’re not a stranger; you’re someone who’s already added value.

Tell stories, not specifications. Instead of “Our platform increases efficiency by 40%,” try “Here’s how a VP of Sales went from 20 hours/week on admin work to 5 hours using automation.” Stories are far more memorable than statistics.

Content types that drive the B2B pipeline:

Not all social content is created equal when it comes to generating revenue. Here’s what actually works for B2B sales:

• Industry insights and trends. Share your take on market changes, new regulations, or emerging technologies affecting your prospects’ businesses. Position yourself as someone who understands their world.
• Problem-solving content. Short tactical posts about solving specific challenges your prospects face. “3 ways to reduce customer churn” or “How to get executive buy-in for new tech” perform way better than product pitches.
• Customer success stories. But not the boring case study kind. Real stories about specific problems and how they got solved. Names, numbers, and details make it credible.
• Controversial takes (when you have receipts). Thoughtful contrarian perspectives on industry norms get attention and spark conversations. Just make sure you can back them up.
• Behind-the-scenes looks. How your team works, problems you’re solving, lessons you’re learning. Humanises your brand and builds a connection.

The reality check:

Let’s be honest about something important: AI can’t build genuine relationships for you. It can find the right people. Research them thoroughly. Draft personalised messages. Follow up consistently. Book meetings on your calendar.

But it can’t have authentic conversations. It can’t read between the lines. It can’t navigate complex objections or negotiate deals. That’s still on your team. And honestly? That’s the fun part anyway.

What AI does is eliminate the tedious research, data entry, and repetitive outreach so your team can spend their time on what humans do best: building trust, solving problems, and closing deals.

Getting started: The first 30 days

Week 1: Build your targeted list using AI data sourcing. Who are your ideal buyers? What signals indicate they’re ready to buy? Let the AI research and compile a list of qualified prospects.

Week 2: Start engaging with their content. Like, comment, share. Not with your company account, with your personal sales rep accounts. People buy from people, not logos.

Week 3: Launch your first automated outreach campaign. Personalised messages based on research, sent across email and LinkedIn. Set up the follow-up sequences.

Week 4: Your team starts jumping into conversations when prospects show interest. Focus on the warm leads while AI continues nurturing the cold ones.

The sales teams seeing the best results aren’t choosing between traditional outbound and social selling. They’re combining both, using AI to scale what used to be impossible to scale.

The bottom line is that social selling isn’t replacing cold calling or email outreach; it’s making them work better!  When you reach out to someone who’s already seen your valuable content, engaged with your insights, and recognises your name, your conversion rate skyrockets.

But you don’t have to do this manually at scale. That’s where AI comes in, not to replace your sales team, but to give them superpowers. Find better leads faster. Enrich your CRM automatically. Personalise outreach at scale. Book more meetings. Close deals faster. The choice isn’t whether to start using AI in your sales process. It’s whether you want to be ahead of your competition or behind them!

The post From posts to profit: How to convert social content into revenue in 2026 appeared first on EU-Startups.

Belgian-American startup Aidoptation has secured €20 million in additional financing to accelerate the development and industrialisation of advanced AI-driven autonomous systems. 

The round included €10 million from SFPIM, €5 million from John Cockerill Defence, and €2.5 million each from Ethias Ventures and Belfius Bank & Insurance.

“What started on the racetrack is now scaling into environments where performance under extreme conditions truly matters. With growing demand from governments and industry, this round allows us to move faster, test harder, and expand the reach of our technology,” the company mentioned in its LinkedIn post. 

Founded in 2025, Aidoptation is the commercial spin-off from the Indy Autonomous Challenge IAC), an American competition where AI-driven robotic cars race at high speeds on iconic circuits. The company is headquartered at DronePort in Sint-Truiden, Belgium. DronePort is a 30-hectare airport business park and test centre for carbon-free and autonomous mobility. 

Aidoptation develops autonomous driving technology for dual-use applications in commercial automotive, police and first-responder vehicles, and defence-relevant environments. Its core product, EdgeDrive, is designed for high-speed autonomous driving and has reached Technology Readiness Level 6 to 7 for passenger vehicles capable of highway-speed autonomy above 90 km/h.”

The fresh capital will be used to support R&D in AI-driven autonomous systems, the industrialisation and scaling of validated technologies such as EdgeDrive, and the development of strategic industrial and defence partnerships in Europe, the US, and Asia. 

“From an economic perspective, the investment anchors high-value R&D and industrial capacity in Belgium, supports innovation-led productivity, and uses public capital to crowd in private and strategic investment while preserving governance control. From a defence and security standpoint, it strengthens NATO-aligned innovation, reduces dependency risks in critical autonomous technologies, and reinforces the Belgian and European defence-industrial base through trusted transatlantic cooperation,” DronePort mentioned in a press release. 

The company raised a €5 million Seed funding round in February this year, supported by the Limburg investment company LRM and Ethias Ventures, the investment arm of insurer Ethias.

The post Belgian-American startup Aidoptation secures €20 million to scale autonomous driving from racetrack to real-world mobility and defence appeared first on EU-Startups.