AI-nativecompany building.

Processes that take two hours today run in twenty minutes. Each team member increases their individual performance. The team as a whole becomes faster than the sum of its parts. And the results show in weeks.

Kickoff with your entire team · Weekly sessions on your real processes · Custom AI workflows ready to use · Twelve weeks · Up to 8 people · No generic curriculum · Everything built on your reality.

A workshop shows what's possible. This program builds it with you. Knowledge stays with your team. Your processes run without us afterward.

Founders who want to invest their teams in AI competence. Ventures that want to stop experimenting and start scaling. Teams that want to use AI daily, not just know about it.

Processes that take hours today run in minutes. Decisions are made faster and better reasoned. AI is not a tool running on the side, but embedded in how your company works daily. And knowledge stays. Permanently anchored in your team.

We start with a deep understanding of your processes. We decide together what the biggest lever is. Then we build together: custom AI workflows, tailored solutions and LLMs that fit your infrastructure. We iterate based on real data. We transfer responsibility step by step to your team. With shared responsibility for results · DACH-focused.

We develop functioning systems together. We sit with your team. We share responsibility. And we're still there when something doesn't work. Human-AI teams outperform pure automation by 68.7%. That's the foundation we build on.

For-profit and nonprofit companies that want to build hybrid teams and AI-natively. Founders and leadership teams that understand that operational AI transformation requires responsibility and space.

Your company grows faster because the right decisions are made earlier. Scalable business models emerge with hybrid Human-AI teams that outperform what traditional structures could ever deliver. And you have someone who's really there, who thinks with you, builds with you, shares responsibility with you.

Business model development and validation · AI-native product development and tech solutions · Go-to-market strategy and first customers · Team building and leadership structure · Brand and positioning · Funding strategy and investor access · Scalable processes with hybrid Human-AI teams.

We start with a deep understanding of your company and the job that needs to be done. We validate quickly with real data and real market feedback. Then we build together what grows. What emerges carries us both.

Co-building is the opposite of advisory. We don't sit outside. We build inside. No concept that gets handed over. Operational depth, AI-native infrastructure and shared responsibility for results. We're liable too.

Founder personalities in the growth phase who want to build scalable business models and tech solutions with a real partner. Ventures that want to grow AI-natively. Teams looking for someone who's really there.

A new company emerges from your existing strengths, with startup speed, AI-native infrastructure and clear milestones. New business models are tested quickly without jeopardizing your core business. What emerges stands on its own feet with governance that scales and a model that lasts.

Opportunity analysis and market validation · Business model design and business case · AI-native product development and MVP · Go-to-market strategy and first customers · Organizational structure, governance and legal setup · Team building and leadership · Funding strategy and investor access · Scaling and handoff.

Explore, we analyze your existing strengths, market opportunities and the jobs no one is doing yet. We identify where a new business model creates real added value.

Validate, we test quickly with real market feedback. Hypotheses are proven or disproven before we build. Using lean methods and AI-native infrastructure.

Build, we develop product, team, brand and go-to-market together. Startup discipline meets operational experience. We fill critical roles until permanent positions are found.

Scale, we scale the business model and hand off step by step. The venture becomes an independent unit. We stay as long as it makes sense.

We build more than solutions. We build the entire venture, from the first idea to an independent unit. Not a consulting engagement that ends when the concept is handed over. We sit with your team, share responsibility and are liable for results. 76% of corporations use venture building for new revenue streams.

Mid-market companies and large enterprises that want to build new business models from existing strengths. Organizations that understand the next growth driver lies outside the core business. Leadership teams ready to start building.

Portfolio companies scale faster than market average. AI investments become real growth drivers. EBITDA grows through operational depth. Your fund scales your access to decision-makers in sectors where operative reality counts.

PE holding periods have risen to 6.7 years, the highest since 2005. Financial leverage alone no longer creates returns. 70% of PE firms are increasing AI investments by 25% or more. Funds that evaluate AI investments with real industry knowledge and operational experience have a structural advantage over those who only look at pitch decks.

Operational Alpha for Portfolio Companies
AI-native transformation that moves EBITDA · Co-building for ventures in growth phase · Hybrid Human-AI teams that deliver faster · Scalable business models from existing strengths.

AI Judgment for Due Diligence
Technical validation of AI investments based on real business and operator experience · Evaluation of AI readiness and scaling potential · Industrial and regulatory depth in sectors that matter · Responsible for 400M EUR+ project and portfolio volume.

Access
Decision-maker network in DACH that has grown over 18 years. Bridges between operational reality and capital.

Venture partnerships emerge through trust, impact and domain expertise. We bring operational depth that scales: portfolio companies grow faster, funds become more attractive to founders, to LPs and to everyone who really wants to scale.

VC funds that want to bring operational depth to their portfolio companies · PE firms that want to use AI transformation as a value driver for their portfolio companies · Investors with portfolios in sectors where operational reality outweighs technical theory.

The half-life of professional knowledge is declining structurally. What graduates learn today becomes partially outdated in a few years. At the same time, many educational institutions lack the agility to respond. AI fundamentally changes this: personalized learning at a scale that was previously impossible. Adaptive systems that adjust learning paths in real time. Educators who focus on what AI cannot do: human connection, judgment, context.

The global EdTech market grows from $187B (2025) to $437B by 2033, CAGR 10.8%. The AI in Education sub-market grows faster: from $7B to $41B by 2030, CAGR 42.8%. Europe accounts for around 30% of global market volume. Personalized learning through AI increases course completion rates by 70%. 60% of educators in Europe are already using AI daily.

Regulated vocational training: nursing, medicine, trades, law. Markets with certification requirements and structural skills shortages. Upskilling and workforce development: corporate learning is the fastest growing sub-market. Human-AI co-learning: hybrid learning architectures outperform both purely human and fully automated learning. Lifelong learning: platforms that integrate continuous learning into daily work.

The Earth system sets the boundaries within which all human systems operate. Seven of nine planetary boundaries have already been crossed. Technology built without this understanding will accelerate the problem, not solve it.

We build with founders who understand the full picture: environmental, social and economic systems as one. Regeneration as the organizing idea behind every solution we build together.

The ClimateTech market grows from $37.5B (2025) to $220B by 2035, CAGR 24.6%. The global market for clean energy technologies reaches over $600B per year by 2030 according to the IEA. 83% of institutional investors now require planetary-boundaries alignment as part of their fund terms. The global circular economy opportunity: $4.5 trillion (Ellen MacArthur Foundation).

Energy and networks: renewable generation, storage technologies, grid intelligence. Circular economy and materials: solutions that end as resources. Water and soil: water infrastructure, soil regeneration. Food systems: regenerative practices, precision agriculture, alternative proteins. Industrial decarbonization: cement, steel, chemistry. Hard-to-decarbonize industries need fundamentally new approaches.

Transport networks bring people to places. Energy networks supply societies. Water systems secure life. Digital infrastructure carries the data economy. Infrastructure is the prerequisite for all other sectors. And it stands at a crossroads: aging infrastructure, climate adaptation needs, new regulatory requirements, and structural pressure for digitalization meet massive public investment capital all at once.

Global infrastructure investment needs through 2040 total $106 trillion (McKinsey Global Institute). Digital infrastructure alone accounts for $19 trillion through 2040. VC investments in the built-world sector have increased tenfold between 2017 and 2024.

Civil engineering: roads, bridges, tunnels, utilities. Building and built world: buildings as physical and digital systems, BIM, constructive AI, prefab technologies. Transportation and mobility: intelligent traffic systems, autonomous logistics. Energy infrastructure: networks, storage, decentralized generation. Water and environmental infrastructure: critically underinvested, regulation-driven. Digital infrastructure: data centers, fiber optics, edge computing, digital twins.

Societal challenges arise from structural failure, economic inequality, and political priorities. They are solved through structures that function: economically sustainable, operationally scalable, socially rooted. Social-tech entrepreneurship combines social mission with technological innovation in models that are self-sustaining.

10 million social enterprises worldwide generate $2 trillion in annual revenue and employ 200 million people. 50% of these companies are led by women. The global impact investing market crossed the $1-trillion mark in 2025.

Social enterprises with tech at their core: from educational access to housing, from care infrastructure to economic participation. NGOs in operational transformation: established organizations wanting to scale operationally. CivicTech and GovTech: technology that improves public services. Community and social infrastructure: housing, mobility, healthcare, and education as social essentials.

DeepTech is different from conventional startup building. Development timelines are longer. Capital intensity is higher. Valuation logic is more complex. And the core technology itself is still being developed while the venture is being built. The critical difference is whether the core technology itself still needs to be invented.

36% of all European VC investments today flow into DeepTech. 76 European university spinouts have achieved unicorn or centaur status. McKinsey projects $1 billion enterprise value from European DeepTech by 2030. The global DeepTech market grows with CAGR 48.2% to $714.6B by 2031.

Novel AI and agentic systems: AI foundational technology, new architectures, reasoning systems. Robotics and autonomous systems: industrial robotics, surgical robots, autonomous navigation. Advanced materials: new materials for energy, construction, medicine. Computational biology and chemistry: AI-driven drug development, protein folding, synthetic biology. Novel energy: fusion energy, small modular reactors, green hydrogen. Quantum technologies: quantum computing, quantum sensors.

Those who digitalize existing processes optimize yesterday. What manufacturing companies need today is a different question: what job should this production do tomorrow? What processes emerge when decision logic is handed over to machines? 50% of manufacturers will highly automate their core processes by 2030.

Global manufacturing industry: $16 trillion. Smart manufacturing market: $175B in 2025, CAGR 9.3% to $274B by 2030. AI in manufacturing: from $34B (2025) to $155B by 2030, CAGR 35.3%. Industry 4.0 as a whole market: $149B (2025), $1.2 trillion by 2035.

Industrial AI and predictive systems: AI applications that monitor production processes and predict failures. Smart manufacturing and digital twins: digital representations of physical production. Supply chain and logistics: AI-driven supply chains. Robotics and automation: industrial robots and collaborative systems. B2B platforms: AI-native production control that replaces legacy structures.

Health systems worldwide are under pressure: structurally, demographically, economically. Aging populations increase demand. Skills shortages reduce capacity. Chronic diseases displace acute care. AI-supported diagnostics, telemedicine, personalized treatment plans, scalable mental health interventions are usable tools in markets with regulatory complexity.

The global HealthTech market grows from $908B (2023) to $3.1 trillion by 2033, CAGR 13.1%. Digital health grows from $199B (2025) to $573B by 2030, CAGR 23.6%. Mental health: 1 billion people globally affected, $2.5 trillion in economic costs per year. Longevity: $27B (2023) to $77B by 2030.

Regulated care and health education: markets with certification requirements and structural skills shortages. Mental health and wellbeing: evidence-based, scalable digital interventions. Digital health and telemedicine: remote treatment, remote patient monitoring, digital diagnostics. Longevity and prevention: genomics, wearables, preventive platforms. Care infrastructure and care tech: digital solutions for care facilities and home care.