AI for owner-led companies.
Start small, get it into production fast. We find the first lever that pays for itself, and build it, without a large consulting team.
The starting point
- AI Readiness Assessment
Structured report: data situation, systems, organization, prioritized AI levers
- AI Agents
AI agents and workflows in production, with access to real systems and data
- Agentic Engineering
Measurably faster development cycles at equal or better quality; playbook and trained team
- Fractional CTO
Technical leadership on demand: architecture decisions, team building, vendor decisions
Cases
| Situation | Implementation | Outcome | |
|---|---|---|---|
| EdTech company, engineering organization | Engineering team used AI coding tools individually, without a shared workflow. | Toolchain, adapted workflow, and quality gates for AI-assisted development. | The team works agentically as the default, with controls that fit the new way of working. |
| EdTech company, content production | Content production ran step by step by hand and depended on a few people. | n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off. | The content team works on quality, not on mechanical intermediate steps. |
| Own products: FoodCheck and FitWise | Own product idea in nutrition and fitness, with AI as the core function. | Flutter apps with OpenAI integration, own backend APIs, own AI services. | Two own AI products in production, fully built and operated in-house. |
| Enterprise commerce platform, Hamburg | Enterprise commerce provider with a tightly coupled architecture that had grown over time. | Headless architecture with Kafka event streaming and a staged AWS migration. | The platform runs decoupled, with Kafka as the backbone instead of a central monolith. |
| D2C food brand, Bremen | D2C food brand without permanent technical leadership, open shop and ERP decisions. | Shop evaluation, new architecture, and a guided ERP migration as interim CTO. | Decisions were made and implemented, no longer postponed. |
| E-Commerce, Munich | Several teams shared one frontend and had to coordinate releases. | Micro-frontend architecture with Next.js SSR, backend-for-frontend, and its own DevOps pipeline. | Teams ship their part of the frontend independently, without a shared release window. |
| E-commerce marketplace | Marketplace with over 1 billion products needed a dedicated search architecture. | Scalable search architecture concept with a migration path from the existing solution. | The platform has a search architecture designed for its actual size. |
| EdTech company, customer service | Customer service answered recurring questions manually from scattered knowledge. | AI agent with access to the knowledge base, with clear escalation to humans. | Recurring inquiries run productively through the agent, complex cases stay with the team. |
| Digital consultancy, Hamburg | Digital consultancy needed external architecture expertise for three parallel client projects. | Tech DD of an e-commerce architecture, greenfield CMS concept, architecture for a credit platform. | The consultancy could continue all three client projects on a reliable technical foundation. |
| Real estate service provider | Real estate service provider without a documented technical picture and without a fixed stack. | Tech DD report, greenfield target architecture, and a concrete tech stack decision. | The company has a documented foundation and a fixed stack to build on. |
| Real estate platform, Berlin | Real estate platform in Berlin, technical risk unclear ahead of a decision. | Tech DD report with a risk picture and a target software architecture. | The decision rested on a documented technical foundation. |
EdTech company, engineering organization
Situation
Engineering team used AI coding tools individually, without a shared workflow.
Implementation
Toolchain, adapted workflow, and quality gates for AI-assisted development.
Outcome
The team works agentically as the default, with controls that fit the new way of working.
EdTech company, content production
Situation
Content production ran step by step by hand and depended on a few people.
Implementation
n8n pipeline with LLM steps for drafting, structuring, and formatting, with human sign-off.
Outcome
The content team works on quality, not on mechanical intermediate steps.
Own products: FoodCheck and FitWise
Situation
Own product idea in nutrition and fitness, with AI as the core function.
Implementation
Flutter apps with OpenAI integration, own backend APIs, own AI services.
Outcome
Two own AI products in production, fully built and operated in-house.
Enterprise commerce platform, Hamburg
Situation
Enterprise commerce provider with a tightly coupled architecture that had grown over time.
Implementation
Headless architecture with Kafka event streaming and a staged AWS migration.
Outcome
The platform runs decoupled, with Kafka as the backbone instead of a central monolith.
Situation
D2C food brand without permanent technical leadership, open shop and ERP decisions.
Implementation
Shop evaluation, new architecture, and a guided ERP migration as interim CTO.
Outcome
Decisions were made and implemented, no longer postponed.
Situation
Several teams shared one frontend and had to coordinate releases.
Implementation
Micro-frontend architecture with Next.js SSR, backend-for-frontend, and its own DevOps pipeline.
Outcome
Teams ship their part of the frontend independently, without a shared release window.
Situation
Marketplace with over 1 billion products needed a dedicated search architecture.
Implementation
Scalable search architecture concept with a migration path from the existing solution.
Outcome
The platform has a search architecture designed for its actual size.
EdTech company, customer service
Situation
Customer service answered recurring questions manually from scattered knowledge.
Implementation
AI agent with access to the knowledge base, with clear escalation to humans.
Outcome
Recurring inquiries run productively through the agent, complex cases stay with the team.
Situation
Digital consultancy needed external architecture expertise for three parallel client projects.
Implementation
Tech DD of an e-commerce architecture, greenfield CMS concept, architecture for a credit platform.
Outcome
The consultancy could continue all three client projects on a reliable technical foundation.
Situation
Real estate service provider without a documented technical picture and without a fixed stack.
Implementation
Tech DD report, greenfield target architecture, and a concrete tech stack decision.
Outcome
The company has a documented foundation and a fixed stack to build on.
Situation
Real estate platform in Berlin, technical risk unclear ahead of a decision.
Implementation
Tech DD report with a risk picture and a target software architecture.
Outcome
The decision rested on a documented technical foundation.