Berlin · 10+ years · DACH enterprise

Ways to work together

I embed with engineering-led companies that need product leadership they can trust with their most complex systems, whether that's a six-person team or a platform org serving millions. Three engagement models, each shaped around your team.

AI Discovery Workshop

1 day · credited

A day with your leadership and engineers to map where AI actually pays off — and what your regulator will allow.

  • Your processes mapped, AI use cases ranked by payoff and feasibility
  • EU AI Act and KRITIS/BSI exposure flagged per use case
  • A prioritised roadmap your leadership and engineers agree on
  • One focused day, on-site or remote

Advisory & Sprints

credited toward embedded

An outside eye to frame the problem, pressure-test it, and hand your team a plan they can execute.

  • Product, platform, and org audits with action plans
  • Strategy sprints and roadmap resets
  • Production-AI and compliance readiness (EU AI Act, KRITIS, BSI)
  • Due diligence and vendor selection

Embedded Product Lead

Most common

Monthly retainer

You have the engineers. I own the roadmap, discovery, and the operating rhythm that outlasts the engagement.

  • Full roadmap ownership: strategy through delivery
  • Discovery, prioritisation, and stakeholder alignment
  • Operating cadence and KPI dashboards that stick
  • Production AI: evals, observability, and safe rollout

How an engagement unfolds

From first standup to clean hand-off

Every engagement follows the same arc: listen first, ship to production, then leave behind a team that doesn't need me.

  1. 01

    Discover & Frame

    Weeks 1–2

    I embed in your Slack, standups, and incident calls and spend the first week listening — mapping the dependency graph and how decisions actually get made — then frame the real problem. Security and compliance (GDPR, BSI, KRITIS) shape the architecture from day one, not the week before launch.

  2. 02

    Build & Ship

    Ongoing

    I own the roadmap end to end and we ship to production — evals, observability, and safe rollout included. A weekly operating cadence and KPI dashboards keep progress visible to everyone.

  3. 03

    Measure & Hand Off

    Closeout

    We prove the outcome against the KPIs we set, then I leave behind a team that can run and extend the platform without me — operating rhythm and playbooks, not a dependency on me.

Common questions

How long is a typical engagement?
Embedded engagements run three to six months minimum, long enough to own delivery and leave the team self-sufficient. Advisory sprints run two to six weeks with a defined deliverable.
Do you work remotely or on-site?
Berlin-based, embedded in your tools: Slack, Jira, incident calls. On-site for kickoffs and key milestones across the DACH region.
How does pricing work?
Three ways in, each credited into the next: a one-day workshop, an advisory sprint, or an embedded engagement. Workshop and sprint fees are credited in full if you continue — you never pay twice to get started. Tell me the scope on a call and you get a clear figure, not a vague range.
Can you work under our compliance requirements?
I've shipped under GDPR, BSI, KRITIS, and Gematik. Compliance shapes the architecture from the first design decision, not the week before launch.
Which model is right for me?
If you want to pressure-test where AI pays off before committing budget, start with a Workshop. If you have a defined problem and need a plan your team can execute, that's Advisory. If you have engineers but no one owning the product end-to-end, that's Embedded.
When do people usually bring you in?
Three moments come up most. An AI feature is live but unreliable and needs to be production-grade. A platform migration has stalled. Or growth has outrun the point where product decisions can stay ad-hoc. Earlier costs less than later.
What is a fractional AI Product Lead?
A senior product leader embedded in your team on a defined engagement — no agency overhead, no hiring risk. I own AI feature delivery end-to-end: from model selection and prompt engineering to production observability and team capability building. Typical engagements run three to six months.
How do you approach AI strategy for enterprise and scale-up companies?
I start with a one-day discovery session to map where AI creates real leverage in your product — not where it's interesting in theory. Then I identify the highest-ROI initiative, define the MVP, and lead delivery. Enterprise environments mean compliance (GDPR, BSI, KRITIS) is designed in from day one, not bolted on.
Why hire an independent consultant instead of an AI agency?
Agencies build what you spec. I help you work out what to build and why — then deliver it. You get C-level product thinking without the 3–6 month hiring cycle or the agency markup. Engagements are fixed-scope, and every tier credits toward the next.
What industries and company types do you work with?
Regulated industries are the core: energy (E.ON, 8M+ customers), insurance (Allianz), automotive (Volkswagen, 1M+ users), public sector (Bundesdruckerei, 11 ministries), telecoms (Telefónica), and B2B SaaS scale-ups. If your product touches KRITIS infrastructure, financial data, or health records, that's where I have the deepest pattern recognition.
How do you measure success in an AI product engagement?
Against the metric that matters to the business, not the model. Typically: reliability in production (uptime, accuracy, latency), adoption by real users, and whether the team can operate it independently after I leave. I set these targets in week one and track them openly throughout.
What makes AI product strategy in DACH different from the US?
Three things: compliance is load-bearing from day one (GDPR, BSI, KRITIS), not an afterthought. Procurement cycles are longer and require documented evidence of ROI before budget approval. And Mittelstand companies often need AI integrated into existing SAP or legacy infrastructure, not greenfield. US-style move-fast approaches fail here — careful architecture wins.

Not sure which fits? Let's talk it through.