There’s a lot of attention on how AI can help model our genomes, proteins, and cells. But much of what shapes real-world health outcomes happens outside of our DNA, and rather within the trillions of microbes living in our bodies.

Today, that layer remains one of the most complex and most under-modeled systems in human biology.

That’s what Outpost Bio is tackling: building frontier models at this interaction layer.

Today, we’re thrilled to announce that Merantix Capital has co-led Outpost’s $3.5M pre-seed alongside Seedcamp, with additional participation from OpenSeed VC, Defined, and strategic family offices and angels.

Outpost is led by a deeply impressive founding team: CEO Dr. Jenny Yang (Oxford PhD and former Marie Curie Fellow with a background in clinical machine learning), and COO Alex Merwin, former Head of Growth for Health & Bio Startups at AWS.

As Jenny put it, “Building the most comprehensive dataset in human microbiology gives us a true foundation layer for modeling how microbial communities interact with drugs and other chemical inputs.”

I recently sat down with Jenny and Alex to discuss why microbiology is the next intelligence layer in medicine, what a true moat looks like in techbio, and how Outpost can reshape drug development and consumer health over the next decade.

Our edited conversation is below.

Adrian: Most biological frontier models focus on human DNA. You’re building at the interaction layer across microbial systems. What does that mean in practice, and why are you focusing on this space?

Jenny: Most biological AI models focus on human DNA — our genomes, proteins, and cells. We focus on the trillions of microbes that live in and on us, which directly interact with chemicals and shape how we metabolize drugs, absorb nutrients, and respond to products. These microbes contain far more genetic diversity than the human genome and can vary dramatically between individuals, making them a major — and highly personal — driver of health. We’re building models that capture this interaction layer so we can predict how this hidden ecosystem shapes real-world outcomes.

Adrian: How does Outpost Bio move beyond traditional microbiome research approaches?

Jenny: Traditional microbiome research has largely been limited to correlations, identifying associations between microbes and health outcomes, often relying on low-throughput systems like mouse models or bioreactors that can’t capture the diversity of real human microbial communities. Our lab-in-the-loop platform instead tests diverse, human-derived microbiomes against defined chemical perturbations at scale, directly measuring how drugs and compounds are transformed across different ecosystems. Machine learning then analyzes this highly dimensional, combinatorial space to pinpoint causal microbial drivers and determine the most informative next experiments. This closed feedback loop moves the field from correlation to mechanism, enabling us to model human microbiology at a scale and precision that wasn’t previously possible.

Adrian: Many techbio AI companies talk about proprietary data sets. What is the true moat here?

Alex: In practice, data alone is rarely defensible — it depreciates, it can be replicated, and in microbiome specifically, public datasets are growing fast. Our moat isn’t a static dataset. It’s the closed loop between wet lab and ML that generates new data designed to be maximally informative for model training.

We run high-throughput perturbation experiments on diverse, human-derived microbial communities — stool-derived communities, or SDCs — that capture the real complexity of the gut. Each experimental cycle feeds our models, and those models tell the lab exactly what to test next to fill the gaps in our understanding. That feedback loop means our dataset compounds in value with every iteration, and it’s tightly coupled to the models trained on it. You can’t just download it and replicate the insight.

On top of that, we’re building the infrastructure to make this data ML-ready at scale — enforced metadata, standardized representations, and a benchmarking suite that we intend to open-source. The companies that define how the field measures progress tend to stay at the front of it.

Adrian: Where do you go first as your commercial wedge and why?

Alex: Every oral drug is exposed to the gut microbiome before reaching systemic circulation. Microbial enzymes can activate, inactivate, or transform compounds — sometimes generating toxic metabolites or contributing to inter-patient variability in response.

Despite growing evidence, microbiome–drug interactions are not systematically profiled in preclinical development. There is no high-throughput, diversity-aware screen that captures how compounds behave across real human microbial communities.

We provide that missing layer. Our platform measures and models how compounds are metabolized across diverse microbiomes early in development — flagging potential toxicity signals, identifying variability risk, and informing formulation and clinical strategy before costly trials begin.

We’re starting with pharma because the unmet need is clear, the economic stakes are high, and structured pilot partnerships allow us to generate the high-quality data that continuously improves the platform.

Adrian: You describe building the most comprehensive dataset in human microbiology. What does that sort of data layer enable, and where do you see Outpost building from here?

Jenny: Building the most comprehensive dataset in human microbiology gives us a true foundation layer for modeling how microbial communities interact with drugs and other chemical inputs. Instead of isolated studies or correlations, we generate structured, high-throughput, causal data that allows AI models to learn transferable rules of microbial–chemical interaction. That data layer enables predictive tools for pharma, food, and consumer health companies to anticipate metabolism, toxicity, and variability before going into costly trials. From here, we see Outpost expanding into broader human microbiomes and becoming the intelligence layer that powers microbiology-driven product development.

Adrian: In 10 years, what looks different in consumer health or medicine because Outpost Bio exists?

Jenny: In 10 years, taking a drug or trying a new health product won’t feel like guesswork. Companies will be able to predict how different microbiomes metabolize a compound before it reaches patients or consumers, reducing unexpected side effects and variability in response. Personalized recommendations, from prescriptions to probiotics to skincare, will account for your microbial ecosystem, not just your genetics. Outpost will help make personalized medicine a reality by expanding it beyond human genomics to include the trillions of microbes that shape how our bodies actually respond.

Adrian: Thanks to you both. We’re excited to see what the future holds for Outpost Bio!

Check out Jenny’s recent panel at the Merantix AI Campus.

I’m very pleased to announce that FICUS Health has closed €3 million in funding, led by Redstone and following Merantix Capital’s initial investment through our venture studio.

Read more about the news in Handelsblatt.

When Benjamin Pochhammer first joined us on the AI Campus, he was looking into the intersection of AI and health, having previously founded CASPAR Health. Very quickly, Benny demonstrated not only passion for improving the lives of practitioners and patients, but also that he has the deep knowledge and expertise to build something big in this crucial area.

FICUS Health was born, centering on the rehabilitation space. Why? As Benny describes it, “Rehabilitation is a highly specialized environment with multidisciplinary teams, strict documentation rules, and a unique payor landscape dominated by the German Pension Fund. You can’t succeed here with a generic AI solution. You need deep domain knowledge.”

It’s a critical time, with clinics facing rising costs and workforce shortages. That’s why FICUS is so essential for practitioners, giving them more time to care for patients and reducing documentation effort by 70%.

FICUS has been off and running, working with nearly 100 rehabilitation clinics across Germany already.

I recently sat down with Benny and his cofounder Mario Elstner for a conversation about FICUS, how AI is impacting the patient journey, and where things go from here.

Our edited conversation is below.

Adrian: What is FICUS and why is it so important right now?

Benjamin: FICUS is an AI platform that transforms documentation processes in the rehabilitation sector. We automate one of the most time-intensive tasks in the clinical workflow, giving healthcare professionals more time for what matters most: their patients. For rehabilitation centers, FICUS becomes an enabler, helping them create efficient processes, maintain quality, and stay operational despite growing resource constraints.

The need has never been greater. The healthcare sector is facing a historic workforce shortage. At the same time, rising costs and tight budgets put additional pressure on medical centers while documentation workloads remain disproportionately high. And we haven’t even mentioned demographic change yet. Europe will see a massive increase in people over 65, driving chronic conditions and dramatically increasing the need for rehabilitation services.

FICUS directly addresses these structural pressures by giving time back to medical teams and enabling the facilities to operate more sustainably.

Adrian: How do you see AI impacting the health care space?

Mario: AI already impacts healthcare immensely, and the most immediate change is happening in the administrative space. Clinicians spend a disproportionate amount of time on tasks that don’t require their medical expertise. So, AI won’t replace healthcare professionals, but it will become a powerful agent that takes over repetitive work. Rehabilitation centers using AI will set new standards for efficiency and quality that others will ultimately follow.

Beyond that, AI will help connect the entire patient journey. Healthcare is still extremely fragmented; data and information gets lost between roles, systems, and stages of care. AI has the potential to turn these fragmented workflows into intelligent, adaptive systems that are interoperable by design.

This shift is one of the very few scalable solutions we have for addressing workforce shortages and structural constraints in health systems.

We’ve loved having the FICUS team on the AI Campus and as a really important part of our ecosystem.

Benjamin: From day one we have actively used the Merantix ecosystem. In the early stages it gave us a space to brainstorm openly and challenge our assumptions. And being part of Merantix also makes it much easier to attract top talent.

Mario: One moment that captures this collaboration was in the very early days. Before FICUS even had a real product, the Merantix team helped us shape and test our first MVP. This early support enabled us to enter the rehab market quickly, validate the concept with real users and build from day one with direct user feedback.

And we still benefit hugely from the broader community, from the many HealthTech events and panel talks to deep-dive sessions with other tech experts, founders and engineers. It is an ecosystem that keeps opening doors and brings the right people into the conversation.

What will you use the new funding for?

Benjamin: We’ll use the new funding to significantly advance our product and develop additional applications along the entire rehabilitation journey. A major focus will be interoperability, making sure FICUS connects seamlessly with existing clinical systems and becomes part of the daily workflow. At the same time, we’re strengthening our organizational backbone and scaling our commercial and implementation teams to expand our market position across the German-speaking region.

There’s a lot of competition in the AI health space. How are you taking on that challenge to differentiate and build a moat?

Benjamin: Our biggest differentiation is our focus. Rehabilitation is a highly specialized environment with multidisciplinary teams, strict documentation rules, and a unique payor landscape dominated by the German Pension Fund. You can’t succeed here with a generic AI solution. You need deep domain knowledge. Having built a successful tech company in the rehabilitation sector before with CASPAR Health, I’ve seen firsthand how the system works, where the real bottlenecks are, and what rehabilitation centers actually need.

Mario: We also build a moat through context and data. FICUS structures patient conversations, medical records, and anamnesis data in a way that gives our AI a uniquely detailed understanding of rehabilitation workflows. Combined with auditor insights and continuous feedback from clinicians, this creates a compounding learning loop that makes FICUS increasingly accurate and tailored to this space.

What do you think the health care space looks like in 5-10 years after AI is more fully implemented across health systems?

Mario: I think AI will fundamentally change the health care space over the next decades. It will support data collection about patients during anamnesis, augment their judgement in diagnosis drawing on knowledge bases and medical literature, and support with treatment suggestions and therapy monitoring leveraging historical data.

For patients, this has the potential to result in better access to medical information and care, more timely treatment and a greater focus on preventing medical issues in the first place. Right now we’re only taking the first small steps, but in the long run, for a sector that’s notoriously underfunded and under demographic pressure, AI may prove to be truly transformational.

Thanks very much, and congrats!

Vara, a Berlin-based health tech company specializing in breast imaging, has received the only CE certificate on the market for an AI that can serve as an independent second reader in mammography, both for screening and diagnostic use.

Across Europe, national screening programs face a capacity crisis: more women are eligible for breast cancer screening than ever before, while the number of radiologists declines.

The “double-reading” standard, where two radiologists independently interpreting each mammogram, remains best practice. But as workloads rise by 30–40%, this gold standard is at risk of becoming unsustainable.

AI as an independent second reader has been expected for years. Until now, no system had met the regulatory, ethical, and evidence bar to make it a reality.

How Vara got there step by step

Independent AI in healthcare isn’t mainly about writing code. It’s about earning trust.

Vara’s path reflects a decade of deliberate progress through the four milestones that regulators and clinicians have long demanded:

a) Data foundation: Vara partnered with screening centers across Germany, Europe and emerging markets to build one of the world’s largest curated breast-imaging datasets with far more than 15 million biopsy- and 2 year follow-up proven mammograms.

b) Retrospective validation: Early models showed strong performance but, like most AI in medicine, still lived in the “retrospective” world – safe in research, hard to translate into practice.

c) Clinical integration: Vara then embedded its AI as a support system (“concurrent read”) across the German National Screening Program, now covering ~40 % of all centers. This workflow allowed radiologists to use AI safely while building real-world evidence across a whole nation.

d) Real-world prospective evidence: In 2025, Vara initiated and co-authored the PRAIM study, published in Nature Medicine – the largest real-world, prospective AI study in healthcare. Across 460,000 women screened in 12 centers, PRAIM demonstrated statistically significant improvement in cancer-detection rate (+17.6% in cancer detection rate), reduction in false positives (-20%), and meaningful workload reduction for radiologists (-56.7%).

e) Building real-time monitoring: If an AI system takes independent decisions, it is crucial (also as part of the new EU AI Act) to ensure human oversight and understand how real-world adaptations impact the models’ performance. Vara is the only company on the market with a real-time monitoring system built into its software.

Only after that sequence – data, validation, integration, real-world evidence, and accountability – did Vara apply for regulatory recognition of AI as an independent read.

What’s behind the CE certification

The new CE mark, issued under Europe’s Medical Device Regulation (MDR), authorizes Vara’s AI to act as an independent second reader in mammography.

That means:

• In a double-reading program, one of the two reads can now be performed autonomously by AI.

• In diagnostic workflows, where a second human read is often no longer economically feasible, AI can now restore that safety net.

This is not “decision support.” It is an independent diagnostic interpretation, regulated as a high-risk Class IIb medical device under MDR.

Equally important, the certification acknowledges Vara’s real-time performance-monitoring system, which tracks how the AI behaves across populations, scanners, and time.

That transparency is what makes autonomy possible.

What’s next

Regulatory approval is the beginning, not the end.

For AI to play its full role in screening programs, national guidelines and reimbursement frameworks must evolve.

In Germany alone, 3,000 additional cancers would be found early enough every year. The survival prognosis for early-stage breast cancer is very high (>90%) but drops significantly if found too late. Additionally, 30,000 unnecessary recalls (with enormous emotional pain associated with them) could be prevented.

Germany is uniquely positioned to lead.

Its screening program has just completed a 10-year evaluation confirming significant mortality reduction. Its health-technology-assessment (HTA) framework provides a clear, evidence-based process for evaluating new methods.

The opportunity now is to adapt that process for AI.

If successful, Germany could become the first country in the world to formally integrate AI as a standalone reader in a national cancer-screening program.

A decade ago, AI in radiology was overhyped. Today, it’s finally proven.

And with this CE certification, the field moves from speculation to implementation – responsibly, transparently, and at scale.

The logistics industry is chaotic and full of manual planning, spreadsheets, and phone calls.

It’s the kind of terrain ripe for AI and automation, and a space we’ve been really interested in at Merantix Capital.

Which is why I’m thrilled to introduce our investment in Arqh, which today announced its $3.8 million pre-seed financing round led by our friends at Founderful.

Check out the story here.

Arqh fuses language models with a proprietary optimization engine, a unique agentic system that continuously re-plans real-world logistics operations and explains its choices to keep human dispatchers in the loop.

I recently sat down with Arqh’s founder Antonia Unger to discuss the current logistics landscape, how AI will reshape the supply chain, and where Arqh goes from here.

Antonia’s background in the logistics space showed her that there was a huge opportunity. “Working side‑by‑side with dispatchers, we saw firsthand what their day‑to‑day looks like and were shocked by the inefficiencies and dinosaur software being the status quo,” she told me.

Here’s our edited conversation below.

Adrian: What’s wrong with the logistics industry today?

Antonia: Critical planning — daily dispatch/orchestration and long‑term strategic decisions — remains largely manual. To automate and optimize these decisions, companies to date had two options: Off‑the‑shelf solvers that capture only a small fraction of real constraints (time windows, HOS, dock capacity, equipment compatibility, service levels, live vs. drop, cross‑border rules, etc.). Or bespoke systems that require 10+ MSc/PhD‑level specialists to build and continually retune as operations evolve.

The net effect is organizations revert to spreadsheets and phone trees. Not because they want to, but because current systems can’t represent the business as lived or re‑optimize fast enough when conditions change.

Our motto: Run your operations with zero spreadsheets and zero PhDs.

How does Arqh work? You talk about your system being both “right brain” and “left brain.” Can you explain what you mean by that?

To build the complete intelligence for logistics there are two requirements: handle the messiness of the real world and deliver mathematical precision. These are our two “brain halves.”

Our “Right Brain” is based on language models and understands policies, tribal knowledge, unstructured inputs and can capture intent and context. Our “Left Brain” is a rigorous optimization layer that encodes constraints, proves feasibility, and delivers measurable outcomes, i.e. the “logic & math half.”

Only when both work in tandem can you make complex optimization usable for non‑technical, rightfully skeptical industry veterans. And as dynamic as the real world, not a static one‑time algorithm.

Can you walk me through a very clear use case of Arqh in plain terms, if I’m not someone super knowledgeable about how logistics works?

One concrete use case is daily dispatch planning for a regional last‑mile fleet (e.g., northern Switzerland).

Without Arqh: At 17:00, a team of planners exports orders from the ERP and begins planning 8–15 trucks per person, based on orders and (optionally) service‑level agreements (SLAs). It’s a long, iterative process to satisfy resource, customer, product, and legal constraints. When execution starts at 07:00 the next day, an employee calls in sick, a dock runs late, traffic builds, or a customer doesn’t open. The plan breaks and firefighting begins.

With Arqh: Arqh ingests orders, driver hours‑of‑service (HOS), equipment, dock calendars, SLAs, and live feeds (traffic, weather, carrier updates). It quickly generates a baseline plan that adheres to all constraints and is 10%+ more efficient than a human‑generated plan.

As disruptions hit, the planner communicates in plain English and re‑optimizes the plan.

The result is faster planning, fewer manual calls, fewer empty miles, better on‑time performance, and calmer operations when things change mid-day.

Other use cases where Arqh supports decision‑making include: Strategic what‑if analysis of warehouse locations, fleet mix, and customer tariffs. Streamlined communication among managers, dispatchers, drivers, and end customers. And easy onboarding of new customers and operational changes.

What’s your background and how did you first identify this problem in the logistics space?

My co-founder Mert and I have an ETH Zurich / EPFL background. We both worked in consulting and organically ended up partnering on multiple logistics and transportation projects. Working side‑by‑side with dispatchers, we saw firsthand what their day‑to‑day looks like and were shocked by the inefficiencies and dinosaur software being the status quo. It makes sense as logistics hasn’t historically attracted as much builder attention, but the gap was bigger than we expected, so we started Arqh to close it.

What is the go to market plan for Arqh?

We’re taking a verticalized approach, focusing first on B2B last‑mile logistics. We’re seeking strategic partners in each sub‑vertical to co‑develop technical blueprints of the optimizer and ensure we cover all requirements in those sub‑verticals.

And market timing is perfect. Language model capabilities are exploding but they lack logic and haven’t yet been connected with powerful, custom optimization. We’re building that bridge.

I’m Swiss, so maybe a little biased here… but can you talk a bit about the Swiss AI ecosystem and how it’s matured and grown in order to help you all succeed?

Swiss talent is highly skilled, and universities are open to collaboration. From very early on we worked with research labs at EPFL and kept close ties with the ETH AI Center. Although ultimately customers are what count, the ecosystem is buzzing, the network is strong, and the talent hungry. We wouldn’t want to build Arqh anywhere else.

Where do you see the future of logistics going? What do these systems look like in 5-10 years?

Over the next decade, logistics will evolve into an AI-orchestrated, self-optimizing ecosystem. In 5–10 years, the work that today consumes armies of planners —dispatching, scheduling, capacity planning, rate discovery -—will be handled continuously by intelligent systems that learn and adapt in real time. Humans won’t disappear, but their focus will shift: they’ll set strategy, tune risk preferences, and manage relationships.

Because data standards and interoperability will have matured, these systems won’t just optimize within a single company, but across entire supply-chain ecosystems. The result are networks that are leaner, greener, and more resilient. Margins strengthen, emissions fall, and operators spend their time steering the system instead of fixing it.

What will you use the funding for?

Growing our core engineering team in order to be able to develop the language layer and optimization blueprints more rapidly in different sub-verticals.

Taking this verticalized approach will get is where we need to be to be able to really disrupt.

Thanks very much for the chat! We can’t wait to see where Arqh goes from here.

Check out the great coverage in Handelsblatt, Tech EU, and Tech Funding News.

Here’s why we’re really excited. When you look at the AI agent landscape, things are moving quickly with desktop and web workflows. But mobile, the dominant user interface, is still untapped. We’re soon going to be looking at a world where you don’t need to switch between six apps to check flights, book a hotel, file your expenses, and update your calendar and colleagues. You’ll tell your agent to “book my trip to Berlin next week for the client meeting.”

That’s what Droidrun is building, and the company has already gone viral for its open-source efforts, with 900+ developers signing up for the framework in 24 hours, collecting 3,300+ GitHub stars and becoming the fastest-growing mobile agent project.

Droidrun does that by taking a different approach. “We give the AI the exact structure of what's on screen,” co-founder and CEO Christian Ninstel explained to me recently. “It's like creating an API for every mobile app that's ever been built.”

We’ve been wowed by Droidrun’s speedy traction and the execution abilities of the team. I recently sat down with Christian to hear about Droidrun’s growth and where things go from here. Below is our edited conversation.

Adrian: You built Droidrun after hitting a wall with existing mobile automation. What exactly was broken, and how did you solve it differently?

Christian: We needed AI agents to retrieve product insights from viral TikTok videos for our previous startup. Sounds simple, right? But every existing mobile automation solution failed spectacularly on real apps. They all break when apps update their UI or when you need to handle dynamic content.

The problem is that they try to "see" screens like humans do, which is slow and unreliable. We took the opposite approach: converting mobile interfaces into structured text that LLMs can process directly. Instead of trying to find a button by its visual appearance, we give the AI the exact structure of what's on screen. It's like creating an API for every mobile app that's ever been built.

900+ developers signed up in 24 hours after launch. That's incredible traction for a dev tool. What drove that viral moment?

We launched with a bold claim: "AI can now use your phone." But more importantly, we had a working demo that actually delivered on that promise. Developers had been frustrated by the same mobile automation problems we faced.

The virality came from community channels like Reddit, X, and open-source visibility. We hit 1,000 GitHub stars in under 48 hours because developers could immediately see the value and start testing it.

To maintain growth, we're doubling down on community, developer experience, shipping cloud runtimes, and integrating with major agent frameworks like LangChain, CrewAI, and AutoGen.

Can you walk me through some concrete use cases where Droidrun is already creating value?

We're not building apps, we're building the infrastructure layer that powers them. Think of us as the execution runtime that other companies build on top of.

QA Infrastructure: For example, QA Startups can integrate Droidrun to offer "AI-powered mobile testing" to their customers. Instead of manually writing test scripts, QA teams just describe what they want to test in natural language, and our agents execute across real devices. We're seeing testing companies add mobile AI capabilities to their platforms in weeks, not years. We also discovered that our cloud infrastructure is what a lot of companies can use to scale up and decrease costs.

RPA Platform Extensions: Major automation platforms could integrate Droidrun to extend their desktop workflows to mobile. Imagine a business process that starts in Salesforce, moves to email, then needs to update a mobile-only app - that final step was impossible before. Now RPA providers can offer end-to-end automation that includes mobile.

What's interesting is that we're often faster and more reliable than desktop automation. Desktop RPA deals with complex browser environments, bot detection systems, and constant UI changes. Mobile apps are more predictable; they have consistent UI patterns and fewer anti-automation measures. Our structured text approach bypasses the visual complexity that slows down desktop automation.

Agent Framework Infrastructure: LangChain, CrewAI, and AutoGen are integrating Droidrun so their agents can execute mobile actions. Developers building AI assistants can now say "and also handle mobile workflows" without building mobile automation from scratch.

Enterprise Automation APIs: Companies in fintech and logistics could use our APIs to automate mobile-only business processes. One financial services company built an automated compliance system that verifies transactions across 12 different banking apps - something that was previously 100% manual.

Developer Tool Platforms: Testing and monitoring platforms are white-labeling our technology to offer "mobile AI agents" as a feature. They focus on their core product, and we handle the complex mobile execution layer.

The pattern is clear: everyone wants mobile AI capabilities, but building reliable mobile automation is incredibly hard. We're the infrastructure layer that lets other companies ship mobile AI features without spending years solving the core execution problem.

Mobile is notoriously locked down, especially iOS. How are you handling the technical challenges of cross-platform support?

We already support iOS in private beta, but you're right - iOS is more restrictive. We had to work around stricter sandboxing and app store limitations.

Our approach is hybrid: Android for open experimentation and rapid iteration, iOS for enterprise integrations where we can work within corporate device management systems.

The key insight is that enterprises want this capability badly enough to work within platform constraints. Long-term, we're building toward a unified cross-platform runtime that's open, fast, and production-ready.

Where do you see the biggest opportunities for Droidrun in the next 2-3 years?

Short-term: High-friction industries where APIs don't exist or fall short - fintech, healthcare, logistics. These sectors run on mobile apps but lack automation infrastructure. Banks need to verify payouts, hospitals need to update patient records, logistics companies need to track shipments - all through mobile apps.

Long-term: We're building toward a world where mobile becomes an agentic operating system. Instead of opening ten apps to book a business trip, you express intent and agents execute across your entire mobile ecosystem.

The market timing is perfect - AI agents are exploding, mobile usage is dominant, but the two haven't connected yet. We're building that bridge.

You've raised €2.2M in a challenging funding environment. How are you planning to use this capital?

Three priorities:

1. Team expansion. Growing our core engineering team to ship faster.

2. Cloud infrastructure. Launching managed runtimes for Android and iOS so developers & enterprises can deploy at scale.

3. Ecosystem growth. Deep integrations with agent frameworks and developer tools, plus early proof-of-concept products.

The key is staying open-source at the core. That's what drove our initial traction, and it's how we'll build the ecosystem. The funding lets us accelerate without compromising on that developer-first approach.

Thanks very much for the chat, we’re excited to see where Droidrun goes from here!

Today we have some great news from Libra, our stellar legal AI company which has just announced its latest update: Libra v2.

I’ve been super impressed with the Libra team under the leadership of founder Viktor von Essen, a trained lawyer who joined us at Merantix Capital as a founder-in-residence in 2023.

Libra’s mission is straightforward but challenging: becoming the go-to AI service for lawyers when it comes performing the more menial and time-consuming tasks of legal work, from research to contract review.

With Libra v2, the company has taken a big step forward. I recently sat down with Henrik Volkmann, Libra’s Director of Growth who joined the company in March, to hear about v2 and Libra’s roadmap for the future.

Adrian: Can you tell me a little about Libra v2? What’s the vision behind this update and how does it reflect what you’re hearing from the lawyers on the platform?

Henrik: Our goal is to redefine efficiency and precision in legal work. We've achieved this by integrating advanced AI, a sleek user interface, and practical tools, including our new Libra AI, which offers direct interaction and intelligent, agent-based reasoning tailored to the legal field. We're also rolling out a suite of new features and significantly improving the overall user experience. This update is directly driven by feedback from lawyers, ensuring that Libra v2 supports every aspect of their workflow, from initial research to final contract review. Ultimately, we want legal professionals to focus on what they do best: providing exceptional legal counsel

.Tell me about Noxtua, a specialized LLM for legal. What makes it uniquely suited for legal work compared to general-purpose models?

Noxtua is a specialized LLM for legal tasks, developed by our close partner, Xayn. It's fine-tuned using court decisions and legal documents like contracts, and it's updated monthly to incorporate new court decisions. A key advantage of Noxtua is its accuracy; it finds real court decisions and avoids hallucinating, providing links to the published decisions for each quote. Additionally, Noxtua excels at using legal language and writing legal documents in a manner preferred by lawyers.

What kind of use cases does Libra v2 excel at?
It's often tasks such as summarizing information or creating a first draft, but using our new Review feature for analyzing legal documents against internal standards and extracting relevant information from large documents is definitely a “killer feature.” This allows lawyers to quickly digest complex information and ensure compliance.

What do you think the future of AI looks like in the legal world, and how will Libra be a part of that?

We see AI becoming increasingly integrated into all facets of legal work, from initial research and case preparation to contract negotiation and even litigation support. Libra aims to be at the forefront of this transformation, continuously evolving to meet the changing needs of the legal profession. In the future, we will crack even longer and more complicated workflows and processes of legal work, automating and streamlining even the most intricate tasks. We envision a future where AI augments, rather than replaces, human expertise, allowing lawyers to focus on the strategic and nuanced aspects of their work that require human judgment and empathy.

What has surprised you about how Libra is being used so far by lawyers?

A surprising finding is that not only young lawyers but also older, more experienced lawyers are showing a great deal of interest and enthusiasm for Libra, recognizing its potential to streamline their workflows and enhance their practice.

Privacy concerns must be at the forefront of lawyers’ minds, particularly interacting with AI. How do you make sure that Libra is compliant and safe to use?

Regarding compliance and safety, Libra is compliant with GDPR and BRAO, and holds an ISO 27001:2022 certification. We meet the very strict compliance requirements that German lawyers have. To ensure the secure processing of sensitive client data within Libra, we have established comprehensive non-disclosure agreements with subprocessors such as Microsoft and Deutsche Telekom. German lawyers are required to have these agreements in place with us and our subprocessors. This allows us to compliantly process sensitive personal data.

With more than 3,000 lawyers already using Libra, where do you see the biggest opportunities for further growth? What’s the roadmap ahead?

The biggest opportunity for further growth is right in front of us. Most law firms are not yet using AI tools in their practice, and we often have the chance to be the first to demonstrate the possibilities. We're also engaging with many corporate legal departments. In the near future, we're focused on building more relevant features for legal professionals that will simplify their work and free up their time for actual legal tasks. We see significant potential in integrating more knowledge databases to enhance legal research, and also in further integrating Libra into the software lawyers already use, such as Microsoft Word and Outlook, as well as their document management systems.

Can you tell me a little about your background and your new role at Libra?

I am a fully qualified lawyer by training but have been a tech enthusiast since childhood, enjoying building computers and solving technical problems. At university, I founded a student-run non-profit to provide law students with experience in legal technology. Over the last years I gained experience from other startups and also worked in international law firms like Hogan Lovells and Noerr. At Libra, I am the Director of Growth and responsible for developing our customer base, which also involves a lot of work on the product side. I believe it's very important to have a legal background when selling to lawyers.

A bit of a self-serving question, but here goes: How does our Merantix ecosystem help you in your career as well as your work so far at Libra?

Being part of the Merantix ecosystem has been absolutely fantastic! The Merantix AI Campus is a vibrant hub, and the network it provides is truly incredible. I've had the chance to connect with so many brilliant colleagues from other AI startups, and the knowledge exchange has been phenomenal. We're not just talking about technical details either – we're diving into go-to-market strategies and sharing valuable insights that are directly impacting our growth at Libra. And let's not forget the little things that make a difference – I've even perfected my latte art skills using the awesome coffee machine! It's a supportive and inspiring environment that's definitely fueling our success.

Thanks very much for the chat and congrats on Libra v2!

I’m very excited to announce that Merantix Capital has co-led the $3.1 million pre-seed round in Graph Therapeutics, a next-generation tech bio company leveraging AI to bring precision medicine to inflammation and immunology drug discovery.

Already a seasoned entrepreneur with a tech bio exit under his belt, CEO Gregory Vladimer joined Merantix Capital in 2024 as a founder-in-residence to build his next venture with us from the earliest incubation stages.

Greg teamed with us with an eye on what he wanted to create. His last venture, Allcyte (acquired by Exscientia), utilized AI in the oncology drug discovery and precision medicine space. As Greg explains it, cancer is binary: it’s either alive or dead. Immune diseases, however, are much more complicated — you do not want to simply kill the portion of the immune system that is diseased. So when it came to what challenge he wanted to tackle with his next venture, the mission was clear: use AI and precision medicine tools on the important and unmet medical need within Immunology and inflammation diseases.

Graph’s fundraising round was also co-led by Squareone and includes participation from NAVEC Investment Management and strategic angel investors such as Mehdi Ghissassi via the Atomico Angel Program.

Greg, an American-born entrepreneur based in Vienna, recently visited us on the AI Campus in Berlin. We sat down for an interview about his background, his vision for Graph, and what this new investment means for the company.

I really like Greg’s humility and how he always puts the patient at the center of what he’s doing. Especially his last point about how, if Graph does its job with drug discovery successfully, “The patient shouldn't even notice our technological advances — they'll just experience better outcomes.”

You can read our edited conversation below:

Adrian: Can you tell me a little bit about how you became an entrepreneur and your previous venture?

Greg: I've been studying immunology since I started my PhD in 2008. Working in immunology and trying to understand how really complex diseases affect people and how we treat those has become kind of the source of a lot of my forward momentum and building within biotech and tech bio. After my postdoc in systems immunology, I started a company called Allcyte that was really dedicated to precision oncology and immuno-oncology. At Allcyte, we defined precision medicine as, “The right drug, right time, right patient,” but then very quickly that definition morphed into our ability to use precision medicine to predict how the next generation of drugs could work in the clinic for better drug discovery. After the acquisition of Allcyte by Exscientia in 2021, we really gained a lot of knowledge and first hand experience of drug discovery and development, especially through the use of AI and more scalable, disease relevant oncology model systems.

How does GraphTx build on what you were doing before — but more towards your roots in immunology?

The immune system is the makeup of an incredible number of cell types that all need to work together in an organized fashion to ward off diseases. As soon as one of these becomes dysregulated, you end up with inflammatory diseases or autoimmune diseases where you have tissue damage and systemic problems.

A lot of techbio companies are in oncology, especially younger companies, because cancer is very binary. Cancer is alive or it's dead, right? It has kind of one outcome:you want to kill off cancer. The immune system requires us to find drugs that can rebalance a lot of different cell types that are dysregulated. We don't want to kill dysregulated immune cells, because the second you kill it, you open up the patient to more infection, and that balancing requires a more global understanding of what's occurring.

So we're taking our background from the oncology and immuno-oncology days and our background from more complex modeling days, and now challenging ourselves to look at the immune system.

It sounds like that is much harder.

It is much harder!

How does AI help enable this kind of work?

The tools are now available. We've spent the better part of 10 years through Allcyte and through Exscientia building tool chains and utilizing computational and biological models, and scaling these in order to better understand oncology. Now we're at that forefront, and the ability to change over to a more complex model system is there.

Where does GraphTx fit along the chain of how a drug gets to a patient?

GraphTx begins operation at the starting point of the drug discovery value chain, where our impact can be most significant. We first focus on identifying novel targets - molecular areas in diseases where drugs could have the most beneficial effect. Through what we call multimodal perturbation modeling, we study diseases comprehensively at clinical, molecular, and genetic levels, both at baseline and under various conditions.

We then work through the value chain adding decision making data, where we can, to support the development and discovery process. As a techbio company, we leverage advanced disease modeling and high-quality data to enhance decision-making throughout the drug discovery and development pipeline. This spans from initial target discovery through pre-clinical work, including identifying patient enrichment strategies for trials. Our ultimate goal is ensuring the right patients receive the drugs we're developing or supporting.

You’re an American building in Europe. What advantages do you see here versus the US?

This is my third company that I'm building (second I’ve founded) in Europe, and what we've realized is that there's an incredible amount of untapped potential, both from the financial — where you've got a lot of investment groups who are interested in tech, bio, and biology within Europe — but more importantly is you have an incredible number of very smart, very innovative people that don't necessarily want to move to the US or UK. They're looking for good jobs and good opportunities in Europe.

Going to the US is a great opportunity for a lot of companies, but nothing has held us back so far, and I think we have been extraordinarily successful in building tech, bio, and biotech companies within Europe. My goal is to continue this EU-centric build with Graph.

You’ve now announced your $3.1 million pre-seed financing round. How is this going to help set you on your way?

Raising this much in pre-seed for a tech biocompany in Europe has been really incredible, and the support that we've received from our investors, Merantix Capital and SquareOne, has enabled us to rapidly grow. I think from the techbio point of view, it enables us to think a little bit further in advance on not just how to stay at the bleeding edge of what we're building, but then how to actually write what the bleeding edge is when it comes to disrupting drug discovery and development in immune-mediated diseases.

Ultimately, it gives us the ability to get to where we need to be for seed and get to where we need to really be to disrupt drug discovery and development faster.

If I might ask a self-serving question: How has it been working with Merantix Capital?

My relationships are mostly in biotech, so Merantix Capital opened up an incredible number of doors into the tech and deeptech sector that allowed us to really focus on what we wanted to build first - our platform. I think the personal relationships are one aspect of it, but then executing on those personal relationships and using them to your advantage is something that we would not have been able to have done had it not been for Merantix events, connections, and introductions.

Last question. What do you think drug discovery looks like in 5-10 years? Not just how the industry is transformed, but how that might impact the lived patient experience?

In 5-10 years, if we're successful in implementing AI in drug discovery, the patient shouldn't even notice our technological advances - they'll just experience better outcomes. What matters to patients is getting effective treatments with fewer side effects, delivered to the right people at the right time.

As scientists working with AI, our responsibility is to be both innovative and careful. This means gathering high-quality, purpose-specific data to train our models, and making informed decisions based on both positive and negative results that AI provides. Rather than pushing for complete AI automation of drug discovery, I envision AI being thoughtfully integrated at key decision points throughout the process, always with the patient's needs as our north star.

The impact of these decisions will be significant, which is why we must implement AI strategically and responsibly, keeping the end goal - better patient care - clearly in focus.

Thanks, Greg!

Today, I’m excited to announce that Merantix and KPMG have entered into a new long-term strategic partnership.

We’ll work together on projects across the entire Merantix group. KPMG will team up with Merantix Capital portfolio companies, work with AI solutions business Merantix Momentum, and become a member of our larger community at the AI Campus in Berlin and as one of the presenting partners of AI House Davos in January. We’re also proud to say that KPMG has invested in our latest AI Fund, which invests in and builds category-defining companies on AI’s application layer in sectors including healthcare, manufacturing, and cybersecurity.

You can read more about the partnership here.

While the partnership spans our entire group, I wanted to talk a little bit here about what this partnership means specifically for Merantix Capital. One of the reasons I’m so thrilled about this new joint effort is that it embodies what we’re trying to achieve: bringing different corners of the AI ecosystem together to make something greater than the sum of its parts.

As a deep-tech GP, I think it’s crucial for VCs and corporate partners to work together creatively. That’s why I really enjoy working with corporate LPs, and KPMG is really a dream partner, given their trust, network, and industry knowledge.

One of the things we bring to the table, when working with corporate partners, is offering early visibility into the most innovative AI startups, whether that’s early-stage product assessment, strategic investment opportunities, and even future potential acquisition prospects upon venture maturation.

The opportunity to see and shape a startup’s progress from such an early stage and gain first access to the latest product development is so crucial, and we believe that has never been more urgent in the European environment, which so desperately needs to modernize and forge strong ties between startups and established companies. As seasoned AI investors with a robust AI community, we see ourselves as trying to build that “trust bridge.”

To that end, it’s wonderful to team with KPMG and see their similar passion for implementing AI throughout industries. That’s why this deal is about much more than a mere investment in our fund. It’s about joining together to drive AI forward in Europe.

You can read more about the partnership here.

Here’s an impressive statistic: 40% of breast cancer screenings in Germany are managed on Vara’s AI platform.

That figure is a testament to the rapid success that Vara has had since being founded in 2018 by CEO Jonas Muff and CTO Stefan Bunk here at Merantix Capital.

But things are just getting started. In an explosive new study with The Lancet, Vara once again has demonstrated the power of artificial intelligence when it comes to catching cancer. “Different AI strategies can indeed increase cancer detection rates by up to 8.6% and dramatically reduce the workload for radiologists—by as much as 84% in some cases,” Stefan told me in a recent conversation.

Furthermore, on Tuesday, Vara announced that it has raised an additional funding round of $8.9 million USD (€8M EUR), which it will use to further its expansion into emerging healthcare markets, with India as the primary focus. The company last year launched a partnership with NM Medical, a leading provider of imaging services in Mumbai, Bangalore, and Pune.

I sat down with Stefan to chat about the study, Vara’s fundraising news, and AI’s impact on cancer treatment, to find out what’s next for this auspicious company. Our edited conversation is below:

Adrian: You just published an explosive new study in The Lancet Digital Health about breast cancer screening. Can you tell me a little bit about how this study came together and what it found?

Stefan: A few years ago, we published our first big study in Lancet Digital Health about combining the strengths of AI and radiologists. In that study, we analysed how AI can improve radiologists and how that affects the radiologists main metrics (sensitivity and specificity). However, in practice it’s not one radiologist alone who reads mammograms (x-rays of the breast). In breast cancer screening, every case is read by two radiologists independently and if at least one radiologist finds suspicious lesions, there is follow up diagnosis via for example ultrasound or MRI. Therefore, just helping one radiologist actually does not directly help women get their breast cancer detected earlier. What if the radiologist with the help of our AI finds a cancer – but the other radiologist anyways would have found it? Then the AI did not actually detect cancers earlier.

Therefore, in this study, we took a full screening program perspective and analysed the metrics that truly matter: breast cancer detection rate and low false positive rate for women participating in screening. In this screening program perspective, an important open research question also is how AI and radiologists best work together. Should AI take over the work of one radiologist? Or should the AI focus on the easy cases, leaving the harder ones for radiologists? What is the best strategy to combine AI and radiologists? Partnering with the University of Cambridge and The Royal London Hospital, we used data from over 1.9 million women in the UK, Sweden, and Germany to analyse various AI integration strategies within existing mammography programs. What we found was promising: different AI strategies can indeed increase cancer detection rates by up to 8.6% and dramatically reduce the workload for radiologists—by as much as 84% in some cases. This research not only demonstrates AI’s standalone potential but also underscores how hybrid approaches that combine AI and radiologist expertise yield the best results, enhancing screening accuracy and efficiency for global healthcare systems.

What does the study mean in practice for your business?

Our goal has always been to leverage AI to make breast cancer screening more accessible and effective, and the study’s findings validate our strategy. The research provides strong evidence that AI can play a practical, impactful role in mammography screening already today, and can create win-win situations for everyone involved: higher detection rates and lower false positive rates for women, lower workload for healthcare providers. We’re already integrating these findings to expand our technology’s reach and effectiveness, focusing on settings where access to skilled radiologists is limited, such as emerging markets. This study equips us to scale confidently and underscores Vara’s role as a leader in AI-driven breast cancer screening.

What do you think AI’s impact will ultimately be when it comes to cancer screening in terms of impact? What do you think cancer treatment will look like in 5-10 years?

AI has transformative potential in cancer screening, especially as it scales to support early detection on a global level. We envision AI not just as a supportive tool but as a critical component of standard care that enables screenings to be quicker, more precise, and accessible even in regions with limited resources. Looking five to ten years ahead, we foresee AI models being applied in a highly personalised fashion, where screenings and treatments are tailored to individual risk factors and health profiles. This shift could bring earlier and more targeted interventions, drastically improving patient outcomes and reducing the burden on healthcare providers.

Longevity is a big topic right now. Do you think AI will expand the human lifespan?

Absolutely, AI has the potential to extend human lifespan, though it’s likely to be through improving quality of life and early disease detection rather than adding years outright. With its ability to detect diseases like cancer at early, more treatable stages, AI can significantly reduce mortality rates and enhance patient outcomes. Additionally, AI can optimise treatment plans, making healthcare more proactive and personalised. While it’s early to predict the full extent of AI’s role in longevity, its preventive and precision-based capabilities certainly pave the way for healthier, longer lives.

You’re a German company originally but have recently launched in India. Can you tell me about why you’ve targeted that market and the traction you’ve seen there thus far?

India faces a pressing need for accessible breast cancer screening due to a shortage of radiologists and limited early detection options, particularly in rural areas. Expanding into this market allows us to directly address these challenges by providing AI-driven screening that supports radiologists and improves early diagnosis. Since launching, we’ve seen positive traction, with healthcare providers increasingly adopting our technology. This early adoption underscores AI’s potential to improve accessibility and outcomes in India’s healthcare system, helping make critical screening services available to more people.

You recently announced new fundraising. Can you tell me a little bit about what that means for Vara and how you’re going to use the money?

This new round of funding is a major step forward for Vara, allowing us to deepen our R&D efforts and accelerate our expansion into emerging markets where our technology can make the greatest impact. We’ll be investing in refining our AI models, making them more adaptable and effective across diverse healthcare environments. Additionally, these funds will support the growth of our screening infrastructure and partnerships worldwide, bringing us closer to our goal of universal, data-driven breast cancer screening. Ultimately, this funding fuels our mission to improve early cancer detection and patient outcomes on a global scale.

Our venture Briink announced some exciting news today: the company has secured €3.85 million in funding to mark the next chapter in their mission to empower ESG and sustainability teams through AI.

This milestone couldn’t have come at a more important time. Given new EU regulations, companies are now about to face fresh challenges when it comes to reporting their environmental impact. I’ve been so amazed at how Briink has grown, already working with more than 40 enterprise clients to ensure accurate and compliant ESG data driving real impact. 

Merantix was proud to invest again in Briink, alongside co-leads EquityPitcher Venture and 13 Books Capital, with additional support from seed + speed ventures and a group of notable angel investors all believing in this company’s great mission. 

I spoke about the news with cofounder Tomas van der Heijden, who I’ve had the pleasure of working with ever since he joined Merantix as a founder-in-residence a few years ago. We covered how AI is shifting what’s possible in sustainability and Briink’s big plans for the future. Check out our conversation below:

Rasmus: What inspired the creation of Briink, and what specific gap in the ESG and AI space were you aiming to fill?

Tomas: Briink was born from a belief that we need to harness the power of AI toward the most important mission of our time: avoiding disaster and accelerating the transition to a sustainable future. In our very first workshop over two years ago, we captured this in our mission statement: “We want to support those truly working for a better future and call out those who are not.” That vision still guides us today.

ESG can be complicated, often misused for greenwashing or treated as a compliance formality. This is why Briink exists. We’ve built AI tools to cut through the complexity, verify ESG claims with real evidence, and empower teams to work faster and more effectively—all while ensuring every decision is grounded in truth.

For us, this isn’t just about technology—it’s about doing what’s right for our family, friends, and our collective future. With Briink, we aim to prevent greenwashing, drive impactful action, and help ensure that regulations are strong enough to guide us toward a thriving and resilient planet.

How do you see AI shaping the future of ESG compliance and sustainability efforts for businesses?

AI is set to play a transformative role in the future of ESG compliance and sustainability by augmenting the capabilities of teams and empowering businesses to navigate an increasingly complex landscape. As regulations become more stringent and the demand for transparency grows, businesses are tasked with managing vast amounts of ESG data while dealing with a shortage of specialized knowledge and labor. AI can enhance the effectiveness of existing teams, enabling them to work faster, smarter, and with greater precision.

Rather than replacing human expertise, AI enhances it by taking on time-consuming tasks like data gathering, processing, and analysis. This gives ESG teams the ability to operate at a scale previously unimaginable, handling large volumes of complex data with speed and accuracy. By simplifying the most intricate parts of ESG compliance, AI allows teams to focus on critical actions, such as developing sustainability strategies and engaging with stakeholders.

AI also plays a key role in ensuring the accuracy and credibility of ESG data. With real-time validation and deep analysis, AI tools help verify ESG claims, reducing the risk of greenwashing and building trust in the data. This enables businesses to confidently act on insights and make decisions that align with their sustainability goals.

What are you going to use the fresh funding for?

We’re using the fresh funding to push the boundaries of AI in the ESG space by developing specialized AI agents that can take on entire roles within ESG teams, not just individual tasks.

These AI agents will autonomously extract and verify ESG data, provide insights, and ensure compliance across multiple frameworks. Whether it's generating reports, drafting policies, creating improvement plans, or keeping ESG data up-to-date, these agents will help teams move beyond analysis to driving real action.

Our ultimate goal is to transform how businesses approach ESG. We’re empowering companies to scale their sustainability efforts more efficiently and effectively, enabling them to make greater progress toward their sustainability goals.

Obviously trust is really critical when it comes to sustainability. AI can be such a black box, so how do you ensure that your models not only operate ethically but also transparently?

Trust is absolutely critical when it comes to sustainability. At Briink, we've developed proprietary innovations that allow our AI models to achieve upwards of 90% accuracy when it comes to analyzing ESG data, but accuracy is just one part of the equation. Transparency and user control are equally important, and we’ve built our systems to ensure both.

Our approach is based on four key innovations. First, we have tailored data pre-processing specifically for ESG documents, allowing us to clean and structure information in a way that suits this unique space. Second, we’ve developed advanced prompt engineering with complex AI chains and agents specifically designed for ESG use cases. Third, our models undergo ESG-specific fine-tuning, enriched with deep ESG knowledge to ensure the most relevant and accurate results. Finally, we’ve designed our user interface to integrate human-in-the-loop workflows, meaning users can review, verify, and refine the AI's outputs, keeping them in control of the decision-making process.

To further enhance transparency, we also employ sentence-level source referencing. This feature allows users to trace every AI-generated insight back to its original source, providing full visibility into how the model arrived at its conclusions. By combining cutting-edge AI capabilities with transparency-focused design, we ensure that our models not only perform at a high level but also build trust with users, empowering them to take confident, informed actions in their sustainability efforts.

How does Briink differ from the competition in this space?

Briink sets itself apart from the competition by focusing on domain-specific AI solutions tailored exclusively for the ESG space, unlike generic tools like ChatGPT or Microsoft Co-Pilot. In the world of ESG, there are unique challenges that require specialized knowledge and precision. ESG data is highly complex, often scattered across various documents, reports, and regulatory frameworks, with nuances that general AI models simply aren't equipped to handle. Our AI is fine-tuned to understand and process these intricacies, ensuring the accuracy, relevance, and depth required for ESG-specific workflows.

Moreover, within the ESG software space, what really sets us apart is our focus on solving the hard, day-to-day problems ESG teams face—not just helping with occasional reporting. ESG compliance isn’t a once-a-year checkbox; it’s an ongoing challenge that involves constantly gathering, verifying, and analyzing large volumes of data.

The regulatory landscape and thinking around sustainability and ESG is shifting. How do you stay ahead of the curve?

The regulatory landscape around sustainability and ESG is indeed shifting rapidly, and at Briink, we stay ahead of the curve by focusing on one of the most critical trends we’re seeing globally: the demand for more transparent, audit-proof data. As regulations evolve, it’s no longer enough for companies to simply report on ESG initiatives; they now need to ensure that their data is verifiable, consistent, and able to withstand rigorous scrutiny from auditors, investors, and regulators alike.

Our AI models are built to address this shift head-on. We’ve designed our tools to not only automate data extraction and verification but to ensure that the data ESG teams work with is fully transparent and traceable. This means that every insight, every ESG claim can be backed by auditable evidence, which is critical in a regulatory environment that increasingly demands proof over promises.

How do you ensure Briink can help find accurate and reliable data across industries that can maybe be inconsistent in their data?

ESG is still quite a new and fragmented space, with numerous regulatory standards and voluntary frameworks across industries. This fragmentation creates major headaches for ESG teams, as they struggle to keep up with varying requirements and scattered data. However, it also presents a significant opportunity for AI and LLM solution providers. At Briink, we built our platform with this challenge in mind, ensuring it's adaptable and flexible enough to meet the needs of diverse industries and frameworks.

Our platform is designed to be flexible on both the user and technical sides. Users can easily create, customize, and analyze their own ESG questionnaires, allowing them to adapt to specific industry needs or changing regulatory landscapes. On the technical side, we’ve built our stack in a model-agnostic way, incorporating both open-source and closed-source models. This ensures that our platform can evolve as new standards emerge and integrate the best AI models available.

Additionally, we’ve focused heavily on creating a robust data infrastructure and document processing system. Our platform can handle diverse document types, and our tailored pre-processing techniques ensure that data from various frameworks is captured accurately and consistently. This flexibility allows Briink to support ESG teams in navigating the complex regulatory environment across industries with ease.

Bigger picture, do you think Briink’s offering can not only help companies think about their compliance, but also help them think about driving real sustainability innovation?

Absolutely. While Briink’s platform helps companies simplify ESG data verification, it also enables them to drive real sustainability innovation. By providing real-time, verifiable ESG data, we empower businesses to move beyond just meeting regulations, offering insights that can highlight gaps, identify opportunities, and support proactive sustainability strategies.

Briink helps companies not only comply but also innovate—whether it's developing greener products, optimizing supply chains, or setting ambitious ESG targets. In the bigger picture, our platform acts as a catalyst for companies to lead in sustainability, driving meaningful impact beyond compliance.

Where do you see ESG going in the next 5 or 10 years?

In the next 5 to 10 years, I see ESG being transformed by AI, as it brings down the cost of intelligence to near zero across industries. For ESG, this means reducing the cost of non-financial and sustainability analysis to the point where it’s possible to perform detailed, real-time ESG assessments of all entities at massive scale and depth. AI will enable companies to analyze their sustainability data effortlessly and continuously, unlocking insights that were previously too expensive or complex to obtain.

In this future, AI-driven ESG analysis will leave no room for greenwashing, as all claims can be verified in real-time with auditable evidence. Decision-makers will be able to make optimal choices on non-financial matters because they’ll have access to accurate, transparent data at their fingertips. Moreover, this transparency will provide a solid foundation for stronger, more ambitious regulations, as regulators will have the data needed to enforce accountability and drive higher standards.

With this vision, Briink is positioned to play a key role in making this future a reality. Our recent funding allows us to scale our platform and deliver the kind of impact we’ve always imagined—helping businesses, investors, and regulators achieve their sustainability goals with unprecedented clarity and confidence.

Thanks very much for the chat!