Top 10 AI Startups to Watch in Denmark in 2026

Table of Contents

• Denmark’s AI buffet
• Corti
• Veo
• GetWhy
• Go Autonomous
• Colossyan
• Teton.ai
• Uizard
• modl.ai
• Ento
• Pandektes
• How to read this Top 10 plate
• Frequently Asked Questions

Corti

From a basement server rack in Copenhagen to emergency call centres around the world, Corti has become Denmark’s closest thing to a canonical AI scaleup. Its core product: an AI assistant that listens to emergency calls and clinical conversations in real time, flagging signs of cardiac arrest and other critical conditions while auto-drafting documentation for patient records.

The clinical stakes are brutal. Dispatchers have seconds to decide whether a caller’s confused description hides a cardiac arrest; misclassify it, and survival rates plummet. Studies cited in ArcticStartup’s coverage of Corti’s Series B and in investor materials show Corti detecting cardiac arrest with around 92% accuracy in under 50 seconds, outperforming human dispatchers in many scenarios.

Under the hood, co-founder and deep-learning PhD Lars Maaløe and team built domain-specific models tuned to medical speech, accents, and real-world audio noise - not generic call-centre data. Just as importantly, Corti sells workflow, not just models: it integrates into emergency call systems and hospital EHRs so that alerts, summaries, and coding flow straight into existing processes instead of yet another dashboard no one opens.

Capital has followed. According to PitchBook’s profile of Corti, the company has raised roughly $110-115M (~750-800M DKK), including a landmark $60M Series B backed by Danish and international investors. That puts it firmly in “infrastructure for healthcare AI” territory rather than point-solution startup.

For AI/ML talent in Denmark, Corti is a real-world lab in operating high-risk AI under the EU AI Act. Roles range from model and data-engineering through to MLOps, clinical-validation science, and compliance engineering - showing how you can work on life-and-death applications without leaving Copenhagen’s bike lanes or the security of the Danish welfare state.

Veo

On pitches from Valby to Vermont, Veo’s green camera boxes have quietly become part of the touchline furniture. The Copenhagen company sells AI-enabled camera systems that automatically film matches, track the ball and players, and generate highlights without a human operator - solving the reality that outside top leagues, most football and handball games were never recorded, let alone analysed.

Instead of focusing only on elite federations, Veo packages hardware, edge AI, and cloud analytics into a single product that grassroots and semi-pro clubs can actually afford. A subscription model turns everything from youth academies to lower-division Superliga sides into viable customers. Startup intelligence platforms like Tracxn’s overview of Danish AI startups estimate Veo has raised around $115M (~800M DKK) and is used by thousands of teams worldwide.

Owning the full stack matters. Because Veo controls camera hardware, on-device models, and cloud services, each weekend’s games feed a proprietary data moat: hours of video, tracking data, and labelled events. That dataset compounds into sharper models for tactical analysis, player development, and eventually scouting and opponent preparation.

Looking ahead, Veo is well-placed to move up the value chain:

• Richer analytics for scouting, load management, and performance benchmarking
• Deeper integrations with club workflows, from talent ID to fan engagement
• Potential convergence with sports-data and media incumbents

For Danish AI/ML professionals, Veo offers a different flavour than healthtech: computer vision on real-world video, edge deployment challenges, and product work that reaches kids’ teams in Køge as easily as colleges in the US. Its presence in rankings such as Seedtable’s list of top Copenhagen AI startups also underlines a broader story: you can help build a globally recognised consumer-facing AI brand without moving your life - or your football loyalties - out of Denmark.

GetWhy

Instead of a mirrored room with eight strangers and a two-way glass, GetWhy promises qualitative insight at the speed of your TikTok feed. The Copenhagen-based company uses what it calls agentic AI to watch and interpret video feedback from consumers, surfacing themes, sentiments, and concrete recommendations for product and marketing teams.

In the classic model, global brands commission agencies, recruit participants, run moderated sessions, then manually code hours of transcripts. It can take weeks and many thousands of kroner before anyone sees a slide. GetWhy’s platform compresses that cycle to days: upload video responses, and AI agents identify patterns, segment respondents, and draft narratives that CMOs actually recognise as proper qual, not just word clouds.

Funding signals that this is more than a clever demo. According to F6S’ directory of Danish AI companies, GetWhy has raised around $43.1M (~300M DKK), backed by growth investors like PeakSpan Capital to scale into Fortune 500-style accounts. That war chest is being spent on enterprise go-to-market, not just features: integrations with insight repositories, security reviews, and rollout playbooks for global brand teams.

What differentiates GetWhy is its vertical focus. Rather than offering a generic LLM workspace, it bakes in research methodologies, discussion guides, and reporting structures familiar to in-house insights departments. Its video-first approach also builds a proprietary dataset of facial expressions, tone, and phrasing over time - exactly the kind of high-signal, high-trust data Denmark is known for stewarding well. As EU-Startups’ coverage of Danish scaleups notes, this kind of deep verticalisation is a hallmark of the country’s most promising companies.

For AI/ML talent in Copenhagen, GetWhy offers roles that blend model-building with behavioural science: multimodal modelling on video and text, domain adaptation for different cultures, and a front-row seat to how big brands actually operationalise “voice of the customer.”

Go Autonomous

In a country that runs on precision engineering and export-heavy manufacturers, Go Autonomous is tackling one of the least glamorous but most valuable problems in B2B: the flood of messy emails, PDFs, and spreadsheets that still drive ordering and quoting for global industrials.

Think of a Grundfos- or Danfoss-style manufacturer receiving thousands of purchase orders and RFQs in dozens of formats and languages. Humans still spend hours deciphering line items, cross-checking prices, and keying everything into SAP or similar ERPs. Go Autonomous builds AI agents that read, understand, and act on this unstructured communication - parsing PDFs, drafting clarifying emails, and updating back-office systems so revenue flows with far fewer human touchpoints.

According to ArcticStartup’s report on Go Autonomous’ early funding, the company initially raised a €3.1M seed round; more recent rounds bring total funding to roughly $13.3M (~90M DKK) in a Series A that positions it among Europe’s leading “autonomous commerce” players. That capital is going into deep ERP integrations and domain-specific models tuned to long-cycle industrial sales, discounts, and channel structures rather than generic email summarisation.

From a career perspective, this is Denmark’s industrial DNA meeting state-of-the-art AI. Roles span:

• Building and fine-tuning LLM-based agents that can safely execute business actions
• Designing the control layer and UX that let CFOs and sales ops set guardrails on autonomy
• Integrating with complex stacks - SAP, Salesforce, custom middleware - inside export-heavy enterprises

Analysts tracking Nordic startups, such as Startup Savant’s Europe-wide watchlist, increasingly point to this kind of vertical B2B AI as a sweet spot for the region. If you want to work on agentic systems that move real euros through the pipes of European industry - while still biking home along Copenhagen’s harbour in time for dinner - Go Autonomous is one of the clearest bets.

Colossyan

For HR and L&D teams inside Danish and European enterprises, updating training videos is a recurring headache: every regulatory tweak or product update means new scripts, studio time, and coordination across markets. Colossyan, headquartered in Frederiksberg, flips that model by turning scripts directly into hyper-personalised avatar-based videos in minutes.

The platform’s focus is corporate learning rather than splashy marketing content. Users write or paste text, choose an AI avatar and language, and Colossyan generates training or internal comms videos that can be re-versioned for different roles and geographies at almost zero marginal cost. That fits neatly with heavily regulated sectors that Denmark is strong in - pharma, finance, energy - where policies and documentation change often.

According to independent startup trackers, Colossyan has raised around $28.2M (~195M DKK), including a sizeable Series A led by Lakestar and Day One Capital, to double down on this L&D niche. It’s now frequently cited among the best Scandinavian genAI startups, signalling that this is not just a point solution but part of a broader shift in how enterprises produce video.

Three design choices stand out for AI/ML professionals evaluating Colossyan:

• A tight vertical focus on learning workflows (SCORM/LMS exports, version control, localisation)
• Heavy investment in multilingual speech and lip-synchronisation models
• Early attention to governance as EU rules on synthetic media and the AI Act take shape

Tools in this category are starting to feature in user-driven rankings such as the top AI tools in Denmark by reviews, where buyers look beyond avatar “wow” factor to reliability, security, and support. For talent in Copenhagen, Colossyan offers hands-on work in generative video, speech, and compliance-aware product design - plus the chance to see your models deployed inside organisations from local scaleups to global enterprises within biking distance of Frederiksberg Have.

Teton.ai

Walk into any Danish hospital ward on a night shift and the constraint is obvious: too few nurses, too many patients to watch. Monitoring who is trying to get out of bed, who has been still for too long, and who might be deteriorating clinically is exhausting, low-visibility work. Teton.ai’s answer is a computer-vision “co-pilot” called Nightingale that quietly watches over rooms and flags risk situations to staff.

The system uses cameras and AI models to detect patterns such as attempts to leave the bed, absence of movement, or unusual activity. Instead of replacing clinical judgment, it pushes alerts to dashboards or mobile devices, helping nurses prioritise attention and document what actually happened. This framing - augmenting the workforce rather than automating care - is one reason Teton.ai is frequently cited among the top Nordic impact-driven AI companies.

On the funding side, Teton.ai has raised about $21.3M (~145M DKK), including an earlier €4.8M seed round led by investors like Plural and Frontline Ventures. Those resources fund not only model development but also the hard yards of clinical pilots across Danish and wider Nordic hospitals, where staff are vocal, unions are influential, and data protection standards are high.

For AI/ML practitioners, Teton.ai offers hands-on exposure to:

• Robust computer vision in low-light, cluttered hospital environments
• Edge and on-prem deployments that satisfy strict privacy constraints
• Regulatory alignment with EU Medical Device Regulation and the AI Act, including post-market monitoring

It also exemplifies how Denmark’s welfare state and research infrastructure combine. Hospitals and municipalities can co-fund pilots; universities contribute clinical AI expertise; parks like those highlighted by DTU Science Park’s alumni network provide a template for scaling deeptech. If you want your models to ship into environments where failure has a human cost, Nightingale’s quiet watch over Nordic wards is a compelling place to learn.

Uizard

Long before “prompt to product” became a buzzphrase, Uizard was already turning sketches into working prototypes from a small office in Copenhagen. The idea is simple but powerful: take hand-drawn wireframes, screenshots of legacy tools, or short text descriptions, and let neural networks convert them into interactive UI prototypes that non-designers can tweak and share.

For product teams, that short-circuits a painful loop. Instead of waiting days for a designer to translate napkin ideas into Figma and weeks for developers to build click-throughs, Uizard gives founders, PMs, and even customer-facing teams a way to test flows themselves. It’s one of the reasons international rankings like BuildMVPFast’s list of AI startups to watch highlight Uizard as proof that generative design has moved from “cool demo” to concrete productivity gains.

Funding underlines the point. According to global startup trackers, Uizard has raised about $18.6M (~130M DKK), including a $15M Series A, after launching in 2018. That early start gave the team time to assemble proprietary datasets of UI patterns and tune models specifically for product design rather than generic image generation.

From a practitioner’s perspective, Uizard sits at the crossroads of computer vision, sequence modelling, and UX. Work typically revolves around:

• Parsing sketches and screenshots into structured layout representations
• Generating components consistent with modern design systems
• Collaborative features that let agencies and in-house teams iterate together

For Copenhagen-based talent, Uizard also plugs straight into the city’s dense ecosystem of digital agencies and consultancies. Many of the startups featured on Failory’s overview of Copenhagen startups rely on fast prototyping; Uizard is both a tool they can use and a company where you can help define what “AI-native” product design looks like from the Nordics outward.

modl.ai

modl.ai sits at the point where Denmark’s AI research culture collides with a global entertainment business worth hundreds of billions of kroner. Founded by professors Georgios Yannakakis (DTU) and Julian Togelius (IT University of Copenhagen), the company builds bots that can play games like human - and superhuman - QA testers, exploring levels at scale to expose bugs, exploits, and difficulty spikes long before launch.

Instead of hiring large manual QA teams to grind through builds, studios integrate modl.ai’s engine-agnostic tools so AI agents can stress-test mechanics, balance settings, and even content pacing. That makes it a natural fit with the “quiet superpower” positioning of Denmark’s tech scene, where deep university research frequently spins out into commercial products, as profiled in Nucamp’s overview of Denmark’s thriving tech hub.

Funding-wise, modl.ai has raised about $23.1M (~160M DKK) from backers including Microsoft’s M12 and PreSeed Ventures, enough to move from consultancy-style pilots to a proper middleware business. The technology is already used with both indie and AAA studios, positioning the company as an infrastructure layer rather than just another tools vendor.

For AI/ML practitioners, working at modl.ai means solving problems like:

• Training reinforcement-learning agents that mimic different player personas
• Designing analytics pipelines that turn playthroughs into actionable suggestions for designers
• Packaging complex AI into SDKs that plug smoothly into major game engines

The rise of companies like modl.ai is one reason commentators on the Nordic ecosystem, such as those featured in TechCrunch’s look at the Nordic startup scene, increasingly point to Copenhagen as a place where applied research in AI finds real commercial traction. If you want to work on agents today that could influence tomorrow’s robotics and simulation work, this is one of Denmark’s most interesting labs.

Ento

In Aarhus, far from the harbour cranes of Copenhagen, Ento Labs is turning building-meter data into a quiet lever for the green transition. Its platform ingests consumption data from thousands of meters, combines it with weather and building characteristics, and uses AI to spot anomalies and recommend specific actions that cut kWh and CO₂ - essentially a scalable, always-on digital energy advisor.

The timing is no accident. Denmark has a legally binding target to cut emissions by 70% by 2030, and buildings remain one of the hardest sectors to decarbonise. According to TechSavvy’s profile of Ento, a major energy group has already invested, with the explicit statement that they “have a global ambition” for scaling the technology. Venture funds like byFounders and Voyager Ventures add climate-tech experience and international networks.

In practice, Ento is built for organisations that own or manage large portfolios:

• Municipalities trying to prioritise renovation budgets across schools and care homes
• Housing associations balancing tenant comfort with energy bills
• Retailers and logistics players optimising stores and warehouses

By turning millions of raw data points into a ranked list of actions, Ento helps over-stretched facility teams focus on the highest-impact fixes first, rather than sifting through spreadsheets. The company’s listing on The Hub’s overview of Nordic startups highlights growing demand beyond Denmark, reflecting that this is a problem every grid faces.

For AI and data professionals, Ento offers applied work in time-series modelling, anomaly detection, and decision-support interfaces - all within biking distance of Aarhus University and a strong local cleantech cluster. It’s a reminder that some of the most impactful AI in Denmark runs not in headline-grabbing robots, but in the basements of very ordinary buildings.

Pandektes

European lawyers and in-house counsel now navigate a thicket of EU regulations, national laws, and case law in more than twenty languages. Traditional databases still rely on keyword search and manual filtering, which is slow and brittle when you are advising on cross-border deals or compliance questions that span several jurisdictions at once.

Pandektes, founded in Copenhagen, tackles this by offering an AI-powered legal research platform built specifically for European law. Instead of simple keyword hits, it provides contextual search across legislation and case law and can generate targeted summaries of relevant provisions. According to LegalTech-Talk’s report on Pandektes’ seed round, the company has raised about €2.9M (~22M DKK) to expand this capability across the EU.

Trust is central in a sector where confidentiality is non-negotiable. Analysts highlight Pandektes’ ISO 27001:2022 certification as a key differentiator for conservative law-firm and corporate buyers. Operating from Denmark, with its reputation for high governance standards and strong data protection, also positions the company well as EU institutions roll out the AI Act, DSA, DMA, and sector-specific rules that demand rigorous handling of sensitive data.

Under the hood, the work spans several technical and product tracks:

• Multilingual NLP tuned to dense, formal legal language
• Retrieval-augmented generation that keeps citations and context precise
• Integrations into document- and matter-management systems used by European practices

For AI and ML professionals in Copenhagen, Pandektes offers a path into “AI for regulation, built inside regulation”: applying cutting-edge language models in one of the most compliance-heavy domains in Europe, while collaborating daily with lawyers, policy specialists, and knowledge managers. It’s a distinct flavour on Denmark’s AI buffet, but one that will only grow more important as legal teams race to understand and operationalise new EU rules on AI itself.

How to read this Top 10 plate

Standing at that Torvehallerne counter, the “Top 10 chef’s favourites” board is both a relief and a limitation. It narrows your choice, but you know there’s more flavour hiding just beyond the sign. This ranking works the same way: it zooms in on a few visible dishes while an entire kitchen of Danish AI experiments keeps cooking in the background.

The companies on this list reflect a particular lens: AI-native products that align with Denmark’s structural strengths, show clear traction, and open real career paths in Copenhagen, Aarhus, and beyond. As commentators like Peter Fisk note in his piece on Denmark as a “quiet superpower” of innovation, the country’s edge comes from this mix of deep science, design sensibility, and long-term industrial focus.

To read the list usefully, treat it less as a scoreboard and more as a map of flavours in the national AI buffet:

• Health & welfare AI: regulated products built on high-trust clinical and public-sector data
• Green & industrial AI: tools aimed at decarbonisation, logistics, and manufacturing efficiency
• Horizontal generative tools: platforms that slip into everyday workflows in design, legal, research, and learning

Behind all of them sits an invisible kitchen: university labs, the welfare state that lets people take deep-tech risks, and major foundation and state-backed funding. Initiatives like the Novo Nordisk Foundation-backed Danish Pioneer Centre for AI and the coming national AI supercomputer aren’t on the “Top 10” board, but they season every dish.

Use this plate to orient your own AI/ML journey: which flavour pulls you most - health, green, B2B, or horizontal tools - and which hub makes sense for your life. Then, like any good Copenhagen lunch, wander back to the counter and look for the things that didn’t fit on the sign.

Frequently Asked Questions