Live CV
Dennis Raisich
Full-stack AI leadership: strategy, governance & delivery
Neuhausen am Rheinfall, Schaffhausen (Switzerland) · born 1988 · Available from 1 September 2026
Full-stack AI lead with a business-first mindset: I identify where AI creates measurable value, then design, build, and ship the complete system, meaning data model, pipelines, agents and application. I also coach the teams that use it.
Institutional AI
SME to enterprise
systems for the company, not for individuals
Strategy
through to production
use-case definition, build, cost efficiency
Adoption
through change management
rollout, training, daily practice
Data first
foundation before model
readiness, governance, data residency
In production
Systems I built – the proof
Experience
Interim VP Growth (engagement through MySCALES) – Amnis Treasury Service AG
07/2025 to present · ZurichFintech (FX & international payments), interim mandate
- Built and shipped AI systems end-to-end: KYB automation (around 80% less manual work for the compliance team), voice call agents, custom MCP servers and multi-agent workflows with tool use, including data model, RAG and backend pipelines
- AI strategy & governance for the leadership team: prioritised the use-case portfolio, decided build-vs-buy, set the guardrails, GDPR-compliant EU data flows and LLM evaluation before anything ships
- Rolled out Anthropic tooling to 60 employees company-wide, with governance, training and coaching, until working with AI became daily practice
- Automated core business processes end-to-end; ran AI workshops for the investors and their portfolio companies; amnis was regarded there as the most AI-mature company
Owner – MySCALES / Scales GmbH
02/2023 to present · ZurichE2E AI: strategy, delivery, adoption
- Own product majiq, an AI due-diligence platform for M&A: from data room to shareable DD report in hours instead of months
- Voice AI implemented: customer support, reception, sales and customer onboarding
- AI strategy and use-case definition, compliance assessments (GDPR, Swiss FADP, EU AI Act) and implementation through to the running application
- Cost transparency as the precondition for ROI: AI spend attributed per use case and team, then agent running costs reduced through model choice, caching and token economics
- Custom AI applications, data and AI readiness, plus AI engineering implementation inside development teams
- AI workshops and team enablement; AI due diligence for investors
Board Member – Daita AG
2023 bis 06/2026 · Wollerau SZData & AI consultancy
- Board mandate at a consultancy for large corporates: data governance, data platforms and data products; banking and life sciences
Business Area Lead / Extended Management – Consulteer
02/2021 to 03/2023 · Zurichpromoted from Business Unit Manager (built one unit from scratch)
- P&L responsibility for two business areas and led 40+ people: consultants, tech experts, sales, recruiting; banking, pharma, manufacturing
- Offered the position of Chief Sales Officer before leaving
Key Account Manager – GULP Schweiz AG (Randstad)
07/2018 to 01/2021 · Zurich- B2B IT services for insurers, banks, pharma; C-level stakeholders, contract negotiations
Leadership & how I work
Use cases over tools
For every use case I look for the best AI solution, never in a silo, but scalable enough to serve other areas tomorrow.
Understand, then build
I am not a pure planner. I understand the business and deliver quickly, so the company sees first-hand what AI does when it is used properly.
Data first
Before touching AI, I get the data and the infrastructure in shape. That is the difference between a demo effect and a solution that holds.
Compliance built in
GDPR, Swiss FADP and the EU AI Act from day one instead of repaired afterwards. In the Swiss market that is the difference between a pilot and production.
Cross-functional, not siloed
I look at every part of the company and build solutions that work together. No AI islands.
Business first
AI projects start where they measurably pay off, not with the model. The business case comes before the technology.
Cost transparency first, then efficiency
Most companies don't know what their AI spend is actually for, so they cannot show an ROI. I make usage and cost visible per use case, then build the agents to run at a fraction of that cost.
Delegate the thinking, never the understanding.
Core skills
Strategy & governance
AI strategy · use-case definition & prioritisation · EU AI Act & Swiss FADP · build-vs-buy & vendor selection · sovereign AI & data residency · AI enablement & company-wide rollouts
Cost & ROI
AI cost transparency · usage and use-case attribution · ROI measurement · cost-efficient agent architecture · model and token economics
Building & running
Agentic AI · multi-agent systems · RAG & context engineering · MCP server development · voice AI · LLM evaluation & guardrails · full-stack AI engineering
Platforms & stack
AI platforms
AWS (Bedrock) · Microsoft Azure (AI Foundry) · Google Cloud (Vertex AI) · Anthropic · OpenAI
Data & backend
Supabase / Postgres · BigQuery · relational data modelling · Cloudflare · Vercel
Automation
n8n · Make · Zapier · custom agent workflows
Education · Languages
B.Sc. Industrial Engineering RWTH Aachen University
German (native) · Russian (native) · English (full professional)
Professional Scrum Master I (Scrum.org, 2020)
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