Most large language models today run in the cloud. This means organisations must rely on external servers, incur high compute costs, and relinquish control over sensitive data.
NobodyWho takes a different approach by focusing on Small Language Models (SLMs) that run locally on laptops and smartphones. By keeping AI on the device, data never leaves the user’s control. This setup allows offline use, improves privacy, and removes the need for costly server infrastructure.
The Copenhagen startup has raised €2 million in pre-seed funding to bring smaller, more efficient AI models directly onto users’ devices. The funding round was backed by Nordic early-stage investors PSV Tech and The Footprint Firm, with participation from Norrsken Evolve.
“While the rest of the world is racing to build ever-growing LLMs based on a bigger-is-better approach, we believe the next leap forward in AI will come from making models smaller, local, and human-centric. The future of AI won’t be won by size, but by decentralised models that anyone can run on their own devices,” says co-founder & CEO, Cecilie Waagner Falkenstrøm.
Sustainable approach to AI
Founded by entrepreneur and artist Cecilie Waagner Falkenstrøm, along with co-founder and CTO, Asbjørn Olling, NobodyWho’s engine enables Small Language Models (SLMs) to run locally on laptops and mobile phones, so organisations and individuals keep full control over their data.
Founders explain to TFN, “The challenge was that most developers want to build with LLMs without needing deep LLM expertise. NobodyWho exists to make that possible, while supporting data sovereignty, reducing costs and energy use, and removing dependency on centralised cloud infrastructure.”
With device-first architecture, no data needs to leave the device, enabling true data sovereignty and privacy by design. Running ever-larger cloud-based LLMs comes with a massive energy bill and a growing CO₂ footprint, from training to inference.
However, the company’s local-first SLM architecture significantly reduces this footprint by shrinking models and placing them closer to where they are used. Early benchmarks show up to 100x lower training footprint and up to 500x lower inference footprint.
The platform allows developers to fine-tune and run AI models directly inside mobile and desktop apps, even without prior experience in large language models.
With this approach, NobodyWho delivers offline, data-secure, privacy-preserving and sustainable AI for both people and planet. More than 5,000 developers currently use the company’s open-source SLM engine.
Unlike OpenAI’s GPT models, Anthropic’s Claude, Google’s Gemini, NobodyWho competes on privacy-by-design, energy efficiency, and European AI sovereignty.
What about diversity?
When we asked about the diversity, the company comments, “We’re a small and growing team, led by a female founder and CEO. As we scale, we’re actively focused on building a diverse and inclusive team across both technical and commercial roles.”
What’s next?
The new funding will be used to expand NobodyWho’s technology and grow adoption of local, on-device AI across Europe. Founders conclude, “We want to make NobodyWho a core infrastructure for local AI and follow an alternative path to cloud-based AI by providing open-source fast, private, and cost-efficient local LLMs – and helping position Europe as a global leader in on-device AI.”
Christel Piron, co-founder and General Partner at PSV Tech, says, “Backing NobodyWho was a no-brainer for us: this is an exceptional team building critical European AI infrastructure that is privacy-protecting, energy-efficient, and accessible to developers and companies everywhere – exactly the kind of technology PSV Tech exists to support and we want to see scale from the Nordics.”
Sofie Käll, CIO at The Footprint Firm, continues, “By enabling high-performance SLMs to run directly on users’ devices, they significantly reduce the carbon footprint of AI inference while enhancing data privacy, lowering operational costs, and strengthening European AI sovereignty. NobodyWho is pioneering the infrastructure that makes these ultra-efficient models truly plug-and-play for developers. This is a transformative climate-tech opportunity in one of the fastest-growing emissions categories, and we’re excited to support a team capable of moving the industry toward more responsible AI.”
