Do you work on pure equity?

I love working with ambitious founders, and I happily structure hybrid engagements — a reduced service fee combined with equity — when the fit is right. Pure-equity arrangements, however, aren't something I take on. A service fee is required on every project: it ensures focused delivery, protects both sides against misaligned timelines, and keeps the engagement sustainable the same way any senior technical hire is compensated. If you're an early-stage founder, let's talk — there's almost always a structure that works.

How much does a Fractional CTO cost in Europe?

Hourly consulting is €150/hour, day rate is €700/day. Ongoing Fractional CTO retainers start from about €2,100/month for a 2-3 days/month minimum and scale up with the cadence — a 1-day-a-week engagement typically lands between €5,000–€8,000 per month depending on scope. Partnership deals (reduced fee + equity) are available for aligned early-stage startups. All prices exclude VAT and French TVA applies where relevant; EU B2B clients with a valid intra-community VAT number benefit from reverse charge.

Fractional CTO vs full-time CTO — which should I hire?

Hire a full-time CTO when the role genuinely needs 40+ hours/week of executive tech leadership — usually post-Series A with 10+ engineers, a real product in market, and a 3-year roadmap. Hire a Fractional CTO when you need senior technical leadership but: you're pre-seed to Series A, your engineering team is under 10 people, you want to validate the product before committing to a cap-table-impacting hire, or you need a specific expertise (AI strategy, EU compliance, sovereign cloud) that a generalist full-time CTO wouldn't bring.

How does the Partnership model work?

It's a hybrid engagement: a reduced cash rate (typically 30-40% of my standard) combined with equity. The exact split is discussed case by case after we've talked through your stage, timeline, runway, and goals. The service-fee component is always present; it's what keeps the engagement focused and fair to both sides.

What's a typical engagement duration?

Consultations run 30 or 60 minutes. MVPs typically ship in 4-8 weeks. Fractional CTO engagements are usually 2-3 days per month minimum, with the cadence scaling up based on traction and fit. Shorter sprint engagements (1-2 weeks) are also possible for well-scoped prototypes.

Can you help with EU AI Act compliance?

Yes. I help companies classify their AI systems by risk tier (prohibited / high-risk / limited-risk / minimal-risk), implement the technical documentation and post-market monitoring required by Articles 9-15, align with GPAI obligations for foundation-model users, and produce the DPIA, Transfer Impact Assessments (Schrems II), and Article 28 DPAs needed. I also cross-walk with GDPR, NIS2, DORA (for financial services), and ISO/IEC 42001 / 23894. Sign-off always rests with your DPO or legal counsel; what I deliver is a defensible, documented compliance posture.

Can you help us migrate from OpenAI to sovereign infrastructure?

Yes — this is becoming a common request from European companies facing compliance pressure or customer procurement scrutiny. Typical path: audit current OpenAI/Anthropic usage, map workloads by quality/latency/cost sensitivity, select replacement targets (Mistral Large for most chat, self-hosted Llama-3 or Mixtral for sensitive data, Claude Sonnet via Bedrock EU where acceptable), build an eval harness that proves parity, migrate behind a feature flag, and cut over gradually. End state: zero prompt/response egress to non-EU jurisdictions, with an auditable trail.

Do you work with non-technical founders?

Yes — this is one of my most common engagements. Non-technical founders hire me to make the build vs buy decision, run the first hiring, set up the stack, ship the MVP, and represent the company technically with investors, partners, and early customers. I translate between product intent and engineering reality, and write everything in plain language so the founder stays in the loop without needing to code.

Do you work remote or on-site?

Remote-first, based in Paris. I'm open to on-site days in Paris and short on-site sprints across Europe for the right engagements. Most clients are 100% remote with weekly syncs and async daily updates.

How quickly can you start?

Usually within 1-2 weeks of signing. For urgent MVPs or discovery sprints I can often kick off within days, starting with a scoped 3-5 day discovery phase before we commit to the full build.

Yes. Mutual NDAs are standard. I can send you a clean one to start, or sign yours if the terms are reasonable. All client work, product ideas, and internal data stay strictly confidential — during and after the engagement.

Can you join as a technical co-founder?

Rarely, and only for startups I'm deeply aligned with — on mission, market, and long-term collaboration. Equity-heavy co-founder arrangements need mutual conviction on both sides and a realistic path to milestones. More often than not, the Partnership model (reduced service fee + equity) is a better fit for both of us: it gets you senior technical leadership without the co-founder commitment, and it keeps my focus across a handful of great projects rather than locked into one.

Can you work with US or Canadian clients?

Yes — North American clients are a regular part of the practice. Time-zone overlap with the US East Coast is 3-4 synchronous hours daily (your morning, my afternoon), and engagements typically run async-friendly with weekly syncs.

Baustellen-Abgleich — Computer Vision + kausale Inferenz

Problem

Ein Construction-Tech-Unternehmen musste 2D-Baustellen-Skizzen mit dem tatsächlichen 3D-Projektzustand abgleichen und dann identifizieren, welche der ~7.000 gemeldeten Features tatsächlich die Zeitplan-Varianz trieben. Ein Reporting-Tool produzierte Dashboards, auf die niemand handeln konnte — „der Zeitplan rutscht", ohne das Warum zu isolieren.

Ansatz

Zweiteiliger Build. Vision-Pipeline: YOLOv8 für Objekterkennung auf Baustellenbildern, Homographie-Matrizen zur Normalisierung von 2D-Skizzen in 3D-Koordinatenraum, sodass Plan-vs-Ist berechenbar wird statt per Augenmaß. Kausale Schicht: Causal Forest auf Ressourcenallokations-Entscheidungen angewendet, um kontrafaktischen Zeitplan-Impact zu schätzen. Aus ~7.000 verfolgten Features wurden 200 als zeitplankritisch isoliert.

Stack

PyTorch · OpenCV · YOLOv8 · scikit-learn / EconML for causal inference · Python service layer

Ergebnis

Das Reporting-Tool verwandelte sich von passiven Dashboards in eine „What-If"-Simulations-Engine, mit der Projektmanager tatsächlich steuern konnten. Die 200 / 7.000-Feature-Reduktion war der Durchbruch — das Team wusste endlich, welche 3 % der Inputs in Echtzeit zu überwachen waren und welche 97 % zu ignorieren.

Baustellen-Abgleich — Computer Vision + kausale Inferenz

Problem

Ansatz

Stack

Ergebnis

Möchten Sie ein ähnliches Engagement für Ihren Stack?

Mehr Arbeiten

Verkaufspreis-Prognose für Zwangsversteigerungen auf einem Marktplatz für notleidende Aktiva

Real-Estate-Entwicklungs-SaaS — Monte-Carlo-Simulations-Engine-MVP

CARAG — Compliance-bewusstes RAG über einen 1,2-Mio.-Dokument-Enterprise-Corpus