- 90%+
- AML/KYC & credit decisioning automated across retail and SME, in three countries
- ~1M CHF
- Annual value created through enterprise AI at Ringier
- 20,000+
- Manual hours recovered per year, roughly 12 full-time staff
Built and shipped inside
AI for regulated industries
AI that survives production and the auditor.
I help banks, lenders, insurers and asset managers build, advise on and lead AI that reaches production and holds up when risk, compliance and the regulator come asking.
Next call14 August
01 The problem
Most enterprise AI dies after the demo.
The demo is the easy 10%. The invisible 90% is where AI projects stall: integration, data governance, model risk, explainability, audit trails, sign-offs, change management. In a regulated business that 90% is bigger, slower, and the cost of getting it wrong is existential.
Size it for one of your own processes→01
Pilots that never ship
Impressive proofs-of-concept that quietly die in the gap between a notebook and production.
02
Risk & compliance as a wall
Promising automation blocked at the approval gate because risk was never designed in from day one.
03
Black boxes you can't defend
Models nobody can explain to an auditor, a regulator, or your own second line of defence.
02 Is this you?
You don't need another AI demo. You need one that ships.
Most of the people I work with arrive in one of these situations.
Your pilot stalled before production
An impressive proof-of-concept that cannot get past integration, model risk or sign-off.
Manual review is eating your margin
KYC, onboarding, credit, claims or document work done by hand, at a cost you can count in full-time staff.
Risk keeps blocking your AI
Every initiative dies at the approval gate because compliance was an afterthought, not a design input.
Where you sit
Start where you are.
The constraints differ by industry. Pick yours and see the relevant proof, then the next step.
Banking & lending
AI in credit, AML and onboarding that clears model risk.
- ·90%+ of AML/KYC and credit decisioning automated across three countries
- ·Compliance-first delivery model adopted bank-wide at Danske
- ·GDPR-ready data infrastructure, built before the regulation was in force
Asset management
Explainable AI your investment and risk teams can defend.
- ·Explainable AI for fixed income taken to product-market fit and acquisition
- ·Quant modelling, IFRS 9 and credit-loss provisioning
- ·Model risk and audit trails designed in, never bolted on
Founders & investors
AI product strategy, and diligence that reads the model.
- ·Groq advisory: 4.02x net MOIC, 117% net IRR post-carry
- ·$100k to $4.5M ARR as Head of Product at Genesis Cloud
- ·Still building: ProtocolEngine runs eleven agents in production
03 Why me
The rare overlap: someone who builds the AI and clears compliance.
Most people are one or the other: AI builders who have never survived a model-risk review, or compliance advisors who could not ship an agent to production. I have spent twelve years on both sides. The value lives in the overlap.
Hands-on AI builder
- ·LLM fine-tuning & evaluation
- ·RAG systems & agentic workflows
- ·MLOps on SageMaker, VertexAI & MLflow
- ·Production monitoring & cost tuning
Regulated operator
- ·AML/KYC, credit & onboarding automation
- ·Model risk, explainability & audit trails
- ·IFRS 9, EU AI Act, compliance-first delivery
- ·Sign-off with risk, compliance & second line
AI that ships to production and passes audit.
- 12+
- years across AI/ML and product
- 45x
- ARR scaled at Genesis Cloud
- 4
- companies' teams hired and coached
- 600+
- MLOps community members built
04 How we can work together
Three ways I help.
From building the systems, to advising the boardroom and the cap table, to leading the team that runs it all.
01
AI Leadership & Teams
I embed and build the capability.
Fractional AI leadership that sets up the team, operating model and culture, so the capability outlasts me.
- ·Fractional Head of AI / interim product & transformation lead
- ·Setting up an AI / Data unit from scratch
- ·Hiring, coaching and high-performing teams
- ·Governance and ways of working that pass audit
02
Advisory & Consulting
I help you decide.
Strategy and diligence for the leaders and investors making high-stakes AI calls.
- ·AI strategy, transformation roadmaps and operating model
- ·Build-vs-buy and vendor / model selection
- ·Investment due diligence on AI deals (Groq, Replit, Together AI)
- ·AI accelerator and hardware selection
03
AI Automation & Engineering
I build the AI.
Turning manual, compliance-bound processes into AI systems that reach production and survive audit.
- ·Process automation: AML/KYC, credit, onboarding, documents
- ·Agentic systems and RAG on your data
- ·Conversational Voice AI agents, outbound at scale
- ·Automated trading solutions & price prediction models (Mamba/Transformer custom architectures)
- ·LLM evaluation, fine-tuning and MLOps
How the Audit works
Week 1
Discovery
Interviews and a walk-through of your highest-cost manual processes and the constraints around them.
Week 2
Analysis
I map the automation opportunities, the ROI, and the compliance and risk that gates each one.
Week 3
Roadmap
A board-ready readout: prioritised, sequenced, costed and de-risked.
05 Proof
Shipped, measured, in production.
Not slideware: real systems inside real organisations, with the numbers attached. Two are broken down in full below, so you can forward them.
90%+
AML/KYC & credit decisioning automated
Danske Bank · Enterprise · Nordic retail & SME banking
Defined and delivered AML/KYC and credit-decision automation across retail and SME segments in Finland, Sweden and Denmark, replacing manual onboarding end to end. Pioneered the compliance-first delivery model the bank adopted organisation-wide. An earlier credit-automation design took SME automation from 5% to 50% and added ~€1M in annual profit.
- Banking
- AML/KYC
- Credit
- Compliance-first
→Read the full case · Skim in 60 secondsHide the full case
- Problem
- Customer onboarding and credit decisions were manual across three countries, capping growth and costing the business unit in full-time staff.
- Constraint
- A systemically important bank: every change had to clear AML regulation, model risk, audit and a second line of defence before it could ship.
- Approach
- Designed the credit-automation architecture and defined the product vision for AML/KYC and credit decisioning. Built compliance in as a design input rather than an approval gate, and made the data infrastructure GDPR-ready before the regulation was in force.
- Outcome
- 90%+ automation across retail and SME in Finland, Sweden and Denmark. SME automation went 5% to 50%, adding directly at least €1M annually. The compliance-first model was adopted bank-wide and cut compliance iterations at the approval stage.
- Governance
- Responsible for the product approval process covering every compliance aspect of the end-to-end financing products. The credit and AML/KYC onboarding automation sat inside that scope.
~1M CHF
Annual value from enterprise AI
Ringier · Enterprise · Media, 100+ publications
Led enterprise AI transformation recovering 20,000+ editorial hours a year, including the team working on AI Forge, the group's AI toolbox. Shipped a RAG assistant that lifted session duration 39% for 2M+ monthly users, across 380K+ AI-assisted content events a year.
- Enterprise AI
- RAG
- Agents
- MLOps
→Read the full case · Skim in 60 secondsHide the full case
- Problem
- Editorial work across more than a hundred publications was manual and repetitive, consuming hours that should have gone into journalism.
- Constraint
- Public-facing output for a major publisher: anything generative carried reputation and editorial-trust risk, on top of the usual production constraints.
- Approach
- Ran the AI transformation end to end. Led the team working on AI Forge across five content use cases, and shipped a RAG assistant for Blick.ch plus an agent that joins meetings and maintains JIRA tickets. Redesigned the MLOps setup on SageMaker underneath it.
- Outcome
- About 1M CHF of annual value and 20,000+ hours recovered, roughly twelve full-time staff. 380K+ AI-assisted content events a year. The RAG assistant lifted session duration 39% and pageviews per session 35% for 2M+ monthly users.
- Governance
- Reputation and content risk on a publicly exposed RAG system managed directly, with editorial guardrails around every generative surface.
Acquired
Drove product-market fit to a successful exit
Scorable · Start-up · Explainable AI for asset managers
Took an inherited MVP for explainable AI in fixed-income asset management to product-market fit, directly enabling acquisition by BondIT. Explainability treated as a first-class, regulator-ready feature, not an afterthought.
- Explainable AI
- Asset management
- Fixed income
$100K → $4.5M+
ARR as Head of Product
Genesis Cloud · Scale-up · AI cloud infrastructure
Owned the product vision and roadmap that scaled ARR 45x. Built the GPU-compute and MLOps offering, forged partnerships with Intel Habana and NVIDIA, and landed the largest enterprise contract at the time (€300K+).
- AI infrastructure
- GPU compute
- Enterprise contracts
06 Testimonials
In their words.
“Stefan is one of the brightest guys I've worked with. He went often beyond his role as product manager to also get his hands dirty in operations and architecture, and took on communicating complex and sometimes difficult messages to executives, and orchestrating complex operations, often dealing with compliance, with stakeholders across multiple organizations.”
“Stefan is an expert in Artificial Intelligence and High-Performance Computing, and a remarkable professional. His expertise spanned both software and hardware, going way beyond the level of depth I would typically expect from a Product Manager. He was always reliable, consistently meeting our milestones, and I genuinely enjoyed working with Stefan and his team.”
07 Toolbox
Take something useful with you.
Practical tools from the same work I do with clients. No charge, and no obligation to talk to me.
08 FAQ
The questions regulated buyers ask.
How do you handle our data and compliance during an engagement?
I work inside your environment and controls: your cloud, your data boundaries, your model-risk process. Nothing leaves your perimeter without sign-off, and governance is designed in from day one rather than bolted on.
Can you work within our model-risk and approval process?
Yes, that is the point. I have shipped AML/KYC and credit automation through a bank's second line of defence. I design for explainability, audit trails and sign-off so your risk and compliance teams can approve rather than block.
How long is the Audit, and what do we get?
Two to three weeks. You get a prioritised set of automation opportunities ranked by ROI and risk, the compliance constraints mapped per use case, and a business case you can take to the board.
Do you build, or only advise?
Both. I am hands-on: I fine-tune models, build RAG and agentic systems, and set up MLOps. I also lead strategy and embed as a fractional AI lead. Use me for any point on that spectrum.
Which industries do you work with?
Regulated, compliance-heavy businesses: banks, lenders, insurers, asset managers, and increasingly health and pharma. The common thread is high stakes and a low tolerance for getting AI wrong.
Get started
Find your highest-ROI AI automation, safely.
Tell me about the manual process that is costing you the most. If an Audit is a fit, I will come back within two business days with how it would work. If it is not, I will tell you that too.
Start with a call
Thirty minutes, no pitch. If an Audit is not the right next step I will say so.
Book an intro call→Next call 14 August
- sojanen@gmail.com
- in/stefanojanen
- GitHub
- StefanOjanen
Prefer to write?
- Length
- Two to three weeks
- Fee
- Fixed, agreed before we start
- You get
- Automation opportunities ranked by ROI and risk
- Plus
- Compliance constraints mapped per use case
- Outcome
- A business case you can take to the board