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 | |
|---|---|---|---|
| Industrial software company, PE portfolio | DD findings met unreliable releases and operational knowledge held by individuals. | A prioritized work plan with release rules, backup coverage and operational documentation. | The team delivers more reliably and can share critical tasks. |
| 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. |
| Manufacturing company, family-run | Business teams tested AI tools without a shared overview or approval rules. | An AI inventory, documented risk reviews, an approval process and role-specific training. | Owners can review new uses and track changes to existing applications. |
| B2B services company, owner-led | Sales searched old proposals and personal folders for suitable services. | A proposal agent with approved components, sources and open assumptions. | Sales starts with verifiable drafts and agrees on commitments with project managers. |
| Financial services provider, owner-led | Case handlers only looked for missing documents during application processing. | A preliminary review agent with source references, review rules and a traceable audit trail. | The team receives prepared cases and retains the decision on every application. |
| 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. |
| Property management company, owner-led | Property managers manually reviewed scattered contracts and attachments before every handover. | A document agent with property-specific checklists and evidence for flagged issues. | The team can request specific missing documents and review critical passages. |
| 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. |
| SaaS company, PE portfolio | Architecture knowledge and security evidence were scattered across teams and repositories. | A technical data room with verifiable statements and clear points of contact. | Management can answer buyer questions consistently and explain open risks. |
| 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. |
| PE fund, partner-led | Portfolio companies assessed AI ideas against different criteria. | A shared assessment, agents in production and a reusable operating model. | The fund can compare initiatives while portfolio companies own operations. |
| B2B platform with add-ons, PE portfolio | The acquired companies managed customers and services in separate systems. | A shared target architecture with data ownership and a phased migration path. | The platform can retire systems in a controlled sequence and keep serving customers. |
| Specialist publisher, family-run | Editors manually transferred published articles into other formats. | An n8n pipeline with LLM drafts, source references and editorial approval. | Editors work on prepared variants and control distribution. |
| E-commerce brand, owner-led | The team used extensive ticketing SaaS for a narrowly defined support workflow. | A custom support agent with an inbox, case handling interface and escalation. | Customer service works in its own workflow and handles the remaining exceptions. |
| 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. |
| PE fund with advisory boards, partner-led | Pitch decks and management reports contained AI promises that were hard to verify. | A shared review framework with evidence requests, red flags and DD assignments. | The investment team and advisory boards can question AI claims more precisely and commission reviews. |
| E-commerce company, owner-led | Developers used AI tools individually and assessed results differently. | A configured toolchain, tested quality checks and a shared measurement plan. | The team can use agents for suitable tasks and review changes in a controlled way. |
| Continuing education provider, owner-led | The service team wanted its own workflow to prepare responses to recurring course inquiries. | A working n8n workflow with LLM drafts, approval and a documented handover. | The team can run, check and adapt the workflow itself within clear boundaries. |
| B2B services company, family-run | The leadership team discussed AI ideas without shared selection criteria. | An assessed process list with initial initiatives, owners and next steps. | Leadership can start implementation and explain why other ideas were deferred. |
Industrial software company, PE portfolio
Situation
DD findings met unreliable releases and operational knowledge held by individuals.
Implementation
A prioritized work plan with release rules, backup coverage and operational documentation.
Outcome
The team delivers more reliably and can share critical tasks.
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.
Manufacturing company, family-run
Situation
Business teams tested AI tools without a shared overview or approval rules.
Implementation
An AI inventory, documented risk reviews, an approval process and role-specific training.
Outcome
Owners can review new uses and track changes to existing applications.
B2B services company, owner-led
Situation
Sales searched old proposals and personal folders for suitable services.
Implementation
A proposal agent with approved components, sources and open assumptions.
Outcome
Sales starts with verifiable drafts and agrees on commitments with project managers.
Financial services provider, owner-led
Situation
Case handlers only looked for missing documents during application processing.
Implementation
A preliminary review agent with source references, review rules and a traceable audit trail.
Outcome
The team receives prepared cases and retains the decision on every application.
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.
Property management company, owner-led
Situation
Property managers manually reviewed scattered contracts and attachments before every handover.
Implementation
A document agent with property-specific checklists and evidence for flagged issues.
Outcome
The team can request specific missing documents and review critical passages.
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.
Situation
Architecture knowledge and security evidence were scattered across teams and repositories.
Implementation
A technical data room with verifiable statements and clear points of contact.
Outcome
Management can answer buyer questions consistently and explain open risks.
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
Portfolio companies assessed AI ideas against different criteria.
Implementation
A shared assessment, agents in production and a reusable operating model.
Outcome
The fund can compare initiatives while portfolio companies own operations.
B2B platform with add-ons, PE portfolio
Situation
The acquired companies managed customers and services in separate systems.
Implementation
A shared target architecture with data ownership and a phased migration path.
Outcome
The platform can retire systems in a controlled sequence and keep serving customers.
Specialist publisher, family-run
Situation
Editors manually transferred published articles into other formats.
Implementation
An n8n pipeline with LLM drafts, source references and editorial approval.
Outcome
Editors work on prepared variants and control distribution.
Situation
The team used extensive ticketing SaaS for a narrowly defined support workflow.
Implementation
A custom support agent with an inbox, case handling interface and escalation.
Outcome
Customer service works in its own workflow and handles the remaining exceptions.
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.
PE fund with advisory boards, partner-led
Situation
Pitch decks and management reports contained AI promises that were hard to verify.
Implementation
A shared review framework with evidence requests, red flags and DD assignments.
Outcome
The investment team and advisory boards can question AI claims more precisely and commission reviews.
Situation
Developers used AI tools individually and assessed results differently.
Implementation
A configured toolchain, tested quality checks and a shared measurement plan.
Outcome
The team can use agents for suitable tasks and review changes in a controlled way.
Continuing education provider, owner-led
Situation
The service team wanted its own workflow to prepare responses to recurring course inquiries.
Implementation
A working n8n workflow with LLM drafts, approval and a documented handover.
Outcome
The team can run, check and adapt the workflow itself within clear boundaries.
B2B services company, family-run
Situation
The leadership team discussed AI ideas without shared selection criteria.
Implementation
An assessed process list with initial initiatives, owners and next steps.
Outcome
Leadership can start implementation and explain why other ideas were deferred.