SERVICES
Fixed-scope services
Clear scope, defined timeline, and deliverables you can take to your board, partners, or engineering team. No hourly billing.
Decision-Grade Technical Assessment
A technical due diligence and vendor assessment that turns uncertainty into a defensible decision.
Investment
€9,000–€22,000 (typical €12,000)
Final scope depends on access (people, repos, infrastructure) and decision criticality.
Prices exclude VAT.
Who it's for
- Investors and acquirers evaluating a software or AI-heavy business
- CEOs selecting a strategic platform or vendor
- Founders preparing for fundraising or acquisition diligence
Problems I solve
- You need a go/no-go based on evidence, not demos
- You do not know whether the product can scale, be secured, or be maintained
- AI claims are unclear: what is real, what is commodity, and what it costs to run
What I do
- 1Stakeholder interviews and artifact review
- 2System map: architecture and key dependencies
- 3Risk register with severity and remediation options
- 4AI sanity check when relevant: evaluation, guardrails, monitoring, and unit economics
- 5Executive memo with recommendation and assumptions
What I need (typical)
- Read-only repository access (or architecture and code artifacts)
- CI/CD visibility and current incident or quality signals
- Infrastructure diagrams if available
- 2-4 stakeholder interviews across product and engineering
What you get
- Executive memo with decision summary, key risks, and unknowns
- Risk register with remediation options and rough ranges
- Questions-to-close list for next diligence round or negotiation
- Optional integration readiness notes in acquisition scenarios
Fractional CTO / Principal Architect
Senior technical leadership to stabilize delivery, reduce risk, and make build-vs-buy decisions that stick.
Investment
From €6,500/month (1 day/week), typical €6,500–€18,000/month
Final scope depends on involvement level, team shape, and criticality.
Prices exclude VAT.
Who it's for
- Founders and CEOs scaling a product team without a strong technical decision system
- Teams stuck in delivery slippage, incidents, or architecture debt
- Companies adopting AI and needing reliability, evaluation, and cost control
Problems I solve
- Releases slip and quality is inconsistent
- You do not know what to build vs buy
- AI is in production, but quality and cost are not controlled
- The team is strong, but senior technical decisions are missing
What I do
- 1First 10 business days: architecture map, delivery diagnosis, risk register, and 30/60/90-day plan
- 2Weekly execution cadence with engineering leads
- 3Delivery operating system: quality gates, release safety, and metrics
- 4Vendor selection scorecards, hiring bar, and technical coaching
- 5If AI is involved: eval harness, guardrails, monitoring, and cost controls
What I need (typical)
- Access to delivery boards, roadmap, and release calendar
- Repository and production observability read access
- Weekly leadership and engineering touchpoints
- A designated internal owner for decisions and follow-through
What you get
- Target architecture and phased plan
- Predictable delivery cadence with fewer surprises
- Decision log and vendor scorecards
- Playbooks and handover
AI Operating Model Upgrade
Make AI usage consistent, safe, and measurable across real workflows, not random prompting.
Investment
€5,000–€12,000 (depends on team size)
Final scope depends on tools, data sensitivity, and governance requirements.
Prices exclude VAT.
Who it's for
- Teams already using AI tools but with inconsistent quality
- Organizations that need clear rules for data handling and tool usage
- Leaders who need measurable outcomes, not workshops
Problems I solve
- Inconsistent outcomes because each person works differently
- Hidden risk from unclear data handling and tool approvals
- No measurement means no ROI story and no adoption signal
What I do
- 1Audit workflows to map where AI is used and where it should not be used
- 2Define role playbooks with quality criteria and review points
- 3Set governance: safe-use guidelines, data handling rules, and tool approvals
- 4Define measurement for adoption, time saved, and error reduction
- 5Optional internal assistant setup only when tied to a real bottleneck
What I need (typical)
- Current list of AI tools and access permissions
- Representative workflows and examples of current outputs
- Data classification constraints and policy requirements
- An internal owner for governance and adoption follow-through
What you get
- Role-specific AI playbooks
- Governance and safe-use guidelines
- Measurement plan and adoption checklist
- Handover package for the internal owner
Need a clear yes/no on the tech - or CTO-level leadership to fix it?
Book a 30-minute call. If it's a fit, I'll recommend the right starting point.
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