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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. GDPR-compliant, BSI-experienced. Three engagement models, each shaped around your team.

Available for advisory
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AI Discovery Workshop

1 day · credited

A day with your leadership and engineers to map where AI delivers value and aligns with your regulator.

  • 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
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Advisory & Sprints

credited toward embedded

An outside perspective to frame the problem, pressure-test assumptions, and provide a clear execution plan.

  • 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
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Embedded Product Lead

Most common

Monthly retainer

I own the roadmap, discovery, and operating rhythm while your engineers focus on delivery.

  • 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
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Workshop and sprint fees are credited in full if you continue — you never pay twice to get started.

Which engagement fits?

  1. You need direction and a decision in weeks, not quarters.

    AI Discovery Workshop

  2. You have a team in motion — but decisions keep stalling.

    Advisory & Sprints

  3. You need someone to own the build, end to end.

    Embedded Product Lead

How an engagement unfolds

From first standup to clean hand-off

Every engagement follows the same arc: listen first, ship to production, then hand over a system your engineers understand inside out.

  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 get made — then frame the core problem. We surface security and compliance constraints (GDPR, BSI, KRITIS) immediately so they guide the architecture rather than blocking the 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. I leave behind a documented operating rhythm and playbooks, ensuring your engineers have full control of the platform.

Proof in production

“As project lead for the observability program, I valued that Tungi could align platform teams and business stakeholders around one playbook. He structured the rollout of OpenTelemetry and New Relic so that each new domain onboarded faster and with fewer surprises. Incident reviews are now grounded in shared journeys and SLOs, not just raw logs, which noticeably reduced detection and resolution times.”
F. M. · Lead Project Manager, E.ON
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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 firm figure to work with.
Can you work under our compliance requirements?
I’ve shipped under GDPR, BSI, KRITIS, and Gematik. We map the regulatory constraints during discovery and build them into the architecture, avoiding the cost of retrofitting right 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. Bringing in leadership early prevents expensive rewrites.
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. 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.
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, five 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). 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. Careful architecture wins over moving fast and breaking things.
Let’s find the fit.

A 30-minute call to map your situation to the right engagement. No pitch deck.

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

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