 raises €2.4M to reinvent FMCG brand loyalty 

We’re excited to announce our investment in 

 to reinvent FMCG brand loyalty. The round was led by Speedinvest, with participation from us at True, Fund F, Kima Ventures, Techmind VC and several angels. 

, 10% is a real-time data platform that removes intermediaries, enabling brands to collect, visualise and activate receipt-level data while rewarding users through a cashback app. By putting power back in the hands of both brands and consumers, 10% is creating a fair, transparent loyalty model built on real customer relationships. 

Excited to support 10% as they build a fairer, smarter model of loyalty for millions of shoppers.

To find out more about how 10% transforms everyday purchases into shared value for brands and consumers, download the document below.

We’re excited to announce our investment in 10%, a real-time data platform that removes intermediaries, enabling brands to collect, visualise and activate receipt-level data while rewarding users through a cashback app.

10% raises €2.4M to reinvent FMCG brand loyalty 

Function Health and the New Consumer Health Playbook

In the U.S., one of the brightest names in venture is 

. For an annual subscription, members receive personalised blood tests, detailed insights, and recommendations on nutrition, fitness, and lifestyle. It’s preventative, proactive, and designed with the polish of a modern lifestyle brand. Investors have taken note: Function Health has raised over 

 from the likes of a16z and another entrant, 

The breakthrough is less about science and more about delivery: Function Health solved a problem that has tripped up countless healthtech startups - how to make preventative care feel desirable and worth paying for. It blended healthcare, consumer branding, and technology into a neat package: recurring revenue, scalable data, and a premium-feeling user experience.

No surprise, then, that European founders are eyeing the model. From London to Berlin, startups are now pitching the “Function Health of Europe.” But the big question remains: can that model be transplanted? Or does Europe require its own formula?

The Obvious Challenge: Selling Health in a Market That Expects It Free

The elephant in the room is Europe’s national healthcare systems. In the U.S., healthcare is fragmented, expensive, and riddled with friction. Consumers are accustomed to paying their way through the maze, so a slicker alternative feels like progress.

In Europe, by contrast, public healthcare looms like the cathedral in a town square: vast, immovable, and culturally sacred. Most citizens have never had to pay directly for care. It’s not that problems don’t exist - they do - but the usual venture logic (“if the pain is real, people will pay for the fix”) doesn’t map neatly here.

Americans are told prevention is an investment: spend on early tests today to avoid catastrophic costs tomorrow. That logic works in a system where a hospital visit could bankrupt you. In Europe, the state is expected to catch you if you fall. Which raises the core challenge for startups: 

how do you not only solve a pain point, but also persuade Europeans to open their wallets for something they’ve never paid for before?

Health and Wellness is the New Social Currency

That said, ageing populations, staff shortages, and austerity cuts have stretched public systems thin. And while European healthcare excels at reactive medicine, prevention is not its forte. This is the supposed opening: the prevention gap that public systems don’t cover.

While Americans tend to spend on primary healthcare - such as tests, doctors’ appointments, and treatments - European consumers are more likely to direct their spending towards wellness. The popularity of Oura rings, continuous glucose monitors (CGMs), boutique gyms, and cold-plunge clubs shows Europeans are willing to spend - but mostly on lifestyle-driven products that feel tangible, social, or even Instagrammable (or TikTokable, if that’s the right way of putting it).

Health and wellness is social identity and status. A CGM is data, yes, but it’s also a conversation starter. A £275-a-month gym membership is part exercise, part community, part signalling. In many ways, health has taken the place that travel once held as a cultural marker of taste and wealth.

For founders, the lesson is clear: in Europe, one method of getting people to pay for health is by bundling it with other forms of value - social capital, identity, experience. Prevention alone may not be strong enough as a proposition unless it’s packaged in a way that feels aspirational or communal.

Distribution is another critical fault line. In the U.S., fragmentation makes D2C appealing. A startup that offers a cleaner, simpler experience can stand out in the chaos.

Europe is the opposite: most markets have just a handful of entrenched distribution channels - whether it’s the UK’s NHS or Germany’s GKV, other national insurers, or dominant lab networks. Winning access to these can take years, but once you’re in, the moat is formidable.

Trust plays a central role. Europeans are more likely to trust an insurer or national health body than a shiny consumer app. Which means startups need more than clever UX: they need clinical validation, regulatory approval, and deep local expertise to gain credibility. For those who succeed, however, embedding within these institutions can create distribution advantages that are far more defensible than pure consumer marketing.

Europe’s consumer health market isn’t a copy of America’s - and it doesn’t need to be. Function Health has proven what’s possible when preventative care is reimagined as a consumer experience. But in Europe, the winners will be those who adapt the idea to local realities: public systems, cultural expectations, and different spending behaviours.

That means designing services that sit at the intersection of 

 -credible enough to be trusted, engaging enough to feel aspirational, and integrated enough to reach scale.

Function Health may be the American poster child. But in Europe, the winners won’t just be posters. They’ll be passports - giving consumers entry into healthier, more personalised lives while fitting seamlessly into the systems and cultures they already trust.

This blog is co-authored by our own Kasia Cheng and Hanna Villa from Vorwerk Ventures, sharing perspectives on consumer health. 

Everyone’s trying to build the “Function Health of Europe” — but should they? In the U.S., Function Health cracked the code by making preventative care desirable, a subscription that feels more like a lifestyle brand than a medical service. The European opportunity looks very different.

Everyone Wants to Build the “Function Health” of Europe. Should They?

Few innovations in recent years have reshaped our world as rapidly as language models (LLMs). Their ability to converse in natural language and deliver seemingly coherent answers to any question thrown their way gives the 

 of true intelligence. But as we race to integrate them into workflows, it’s worth stopping to ask: are we giving them more credit than they truly deserve? 

This post explores one of the biggest limitations of LLMs: their tendency to hallucinate. It covers what hallucinations are, why they happen, and how to work around them. Because, as AI becomes more deeply embedded in how we live and work, understanding its weaknesses as well as its strengths will be key to realising its full potential.

Hallucinations occur when an LLM produces plausible 

 but incorrect information. Sometimes it’s a minor factual error resulting from inaccuracies in its training data. Other times it’s a complete fabrication – an entirely false statement which has no basis in its training data at all. Models have been caught inventing case law for legal briefs, generating fictional citations for academic papers, and convincingly summarising documents they’ve never seen. 

More troubling, perhaps, is the confidence with which these models present this incorrect information. Responses come across as smooth, authoritative, and impeccably structured –it’s easy to take them at face value. This is not by accident, but a function of how they are trained. 

LLMs are built to predict the most likely continuation of text, not necessarily the most accurate one

. Furthermore, they are fine-tuned to be agreeable, often conditioned to produce the response that feels most satisfying to the user. When faced with a question outside their knowledge, they fill in the gaps with content that sounds convincing.  

Of course, not all models hallucinate at the same rate and performance varies depending on the task. Benchmark tests, such as Vectara’s evaluation of models summarising news articles, show error rates ranging from under 1% to nearly 30%.  

Surprisingly, it isn’t simply a matter of picking the newest or most powerful model. In fact, so-called “reasoning models,” designed to break problems into steps, often perform worse. OpenAI itself has reported that its GPT-o3 and GPT-o4 models are substantially more prone to hallucinations than some earlier versions. 

Many experts now believe while efforts can be made to minimise hallucinations, they will never be fully eliminated. This is because errors are possible at every stage: training data is always incomplete, retrieval and classification are imperfect, and the generative process itself is inherently probabilistic. 

Although hallucinations may be here to stay, there are practical steps you can take to reduce their impact. 

RAG systems improve reliability by allowing an LLM to pull in fresh, relevant information before generating a response, rather than having it rely solely on its training data. They typically work by accessing a database or API to find documents related to your query, before passing that context to the model for use in its answer.

The field is moving fast, too. The French startup

 is developing an API that gives developers access to up-to-date web content from premium, trusted outlets, which can then be fed into an LLM to enrich its outputs. Knowledge graph technology such as 

’s GraphRAG takes this approach even further. Instead of feeding the model disconnected text snippets, a knowledge graph organises information into entities and relationships, mapping how facts connect to one another. This gives the model a much clearer picture of context to draw on and 

 to improve the accuracy of generalist LLMs like ChatGPT by over 25%.

Leverage vertical or domain-specific models

General-purpose LLMs are designed to handle a wide range of topics, which makes them more prone to hallucinations when venturing into niche areas. Domain-specific models, on the other hand, are fine-tuned on a set of data tailored to a particular industry or field. Because they’re trained on specialised knowledge, they can deliver outputs that are more accurate, consistent, and context-aware.

Early adopters include fields where reliability is most critical, such as law and medicine, but other industries are catching on too. BloombergGPT was custom trained for finance, while Walmart built Wallaby for supply chain and customer service tasks using decades of internal data. One startup in the field,

, trained its proprietary “Legal Pre-Trained Transformer” on over 150 million legal documents to achieve greater accuracy in analysing contracts and other legal content.

Build guardrails – both technological and human

As well as seeking to reduce the frequency of hallucinations, it is also important to build guardrails, both human and technological, that identify inaccuracies in outputs as early as possible.

, provide real-time guardrails to detect and mitigate hallucinations, while also addressing unsafe content, jailbreak attempts, and policy breaches. Tools like this act as automated checks, catching problems before they reach end users and giving teams greater visibility into model behaviour.

Not every safeguard needs to be advanced, though. Simple practices, such as asking the model to provide sources or explain its reasoning, are valuable when using LLMs for research. For important tasks, it’s also essential to independently verify that those sources actually exist — and that they say what the model claims. These habits not only catch errors, but also help you recognise the model’s blind spots, making it easier to apply the technology responsibly in the future.

Large Language Models may be inherently flawed, but that doesn’t make them useless. They will sometimes make things up, misrepresent the truth, or present information with misplaced confidence — but then again, so do humans. The key is not to expect perfection, but to understand how they work, recognise their limitations, and put strategies in place to mitigate their weaknesses.  

Our latest blog unpacks the common issue of Large Language Models and their tendency to hallucinate. Advisory Analyst Cara Hogg explores what they are, why they happen and offers practical strategies for working around them. 

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10% raises €2.4M to reinvent FMCG brand loyalty

Insight

Everyone Wants to Build the “Function Health” of Europe. Should They?

Insight

Large Language Models: Smarter than us — or confidently wrong?