Go from technical risk to production code.
I help you validate high-stakes software decisions in 8 weeks through working prototypes and evidence-based analysis.
Innovation starts with a bet.
To stay competitive, you have to explore new technology. Integrating AI, modernizing your stack, adopting new platforms. These are the bets that create competitive edge.
Does this sound familiar:
Your board asks why you're not using AI yet. The vendor demo looks flawless. Your senior engineer is reluctantly confident it'll work. Your architect sees scaling risks.
You're about to commit €500k and six months based on a sales pitch, a controlled demo, and what, vibes? Will it work in your system? With your data? At your scale?
Noone really knows, but someone has to decide.
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Discovery Validation Implementation Release
When it fails, you own it.
The vendor moves on. Your team unwinds the mess. Your budget is gone, your credibility is damaged, and you're behind on features that actually make money.
You've seen this before. The ML project that failed in production. The cloud rewrite that became 18 months of technical debt. The architecture decision from way back that you're still paying for.
The cost of mistakes compounds over time. Every month on the wrong path is a month not exploring better options.
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I can de-risk it in 8 weeks.
I replace guesswork with evidence through structured R&D. We map the options, validate assumptions through prototyping, and build the production-ready foundation for you to extend.
Most companies gather opinions and debate. I test assumptions and deliver code.
Vendor demos in controlled environments
Working prototypes tested in your actual system.
PowerPoint recommendations
Production-ready code with documentation and tests.
Endless debates about what might work
Evidence-based go/no-go decisions from actual experiments.
Big commitments, up front
Short engagements with natural stopping points.
Past work: Testing a new hardware frontier.
When Apple announced the Vision Pro, a key question emerged: could this be the company's next platform? I built a native proof-of-concept in one month and stress-tested the development pipeline, uncovering critical tooling limitations.
The outcome: Avoided a costly strategic mistake with clear evidence for a "no-go" decision. The prototype became a valuable demo asset.
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◉ ○ ● ●───●───●───●The 8-week pilot.
My process breaks into three stages. Each delivers valuable insight, giving you the evidence to proceed, pivot, or stop.
Stage 1: Hypothesis
We transform ambiguity into a concrete plan. I map viable solutions, analyze trade-offs, and establish clear decision criteria.
You get a strategic brief that ranks your technical options against clear, pre-defined criteria, giving you a defensible plan for moving forward.
Stage 2: Experiment
We test the riskiest assumptions with targeted prototypes. Will it scale? Does it integrate with your systems? Does the vendor's API actually work as advertised?
You get working prototypes and performance data supporting evidence-based recommendations for your next step.
Stage 3: Model
We build the production-ready alpha. Not throwaway demos - well-architected core components with documentation, tests, and observability.
You get a well-architected, production-ready alpha that your team can confidently build upon, complete with tests, documentation, and clear hand-off instructions.
Past work: Escaping vendor lock-in.
When a critical vendor skyrocketed their price, a technology company faced existential risk. I mapped five alternative options, prototyped the top two, and validated the winner through a customer-facing pilot.
The outcome: Vendor dependency eliminated, massive licensing increases avoided, and production-ready architecture delivered in months. The first client project on the new platform went live shortly after.
Who I am.
I've seen how elegant lab solutions fail in production, and how the right technology, validated through structured R&D, transforms businesses.
Now I help technology leaders validate their technical bets before they commit.
"I highly recommend bringing Daniel in to help solve wicked, convoluted development problems. Especially when there is no easy solution and it requires input from several different domains."
Kristian Too Andreasen, CEO at Kanda
My background:
- Former CTO at Kanda, leading technical strategy and product development.
- Machine learning engineer at LEGO, building NLP systems for platform safety.
- Built SynthVR, a commercial VR product, from R&D to profitable release.
- Open source contributor and technical writer on software R&D.
Why 42?
Those who know "The Hitchhiker's Guide to the Galaxy" recognize 42 as the answer to the "Ultimate Question of Life, the Universe, and Everything."
The answer is meaningless without the right question. Many projects fail by solving the wrong problem well. My engagements begin with getting the question right.
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// //////// Past work: A high-volume hiring problem.
A major retailer needed to validate whether machine learning could filter high-volume applicants. I ran experiments comparing multiple approaches. Evidence showed a simple Random Forest outperformed complex alternatives.
The outcome: Delivered a practical solution from hypothesis to production API, with guidance on managing algorithmic bias at scale.
Transparent pricing.
I offer two engagement models. Choose based on your need for flexibility or speed.The core of my work is the 8-week pilot: a structured R&D cycle that takes your idea from ambiguity to production-ready foundation. You can commit to the full pilot upfront or engage one phase at a time.
Flexible
For complex approval processes or evolving requirements. Decide whether to continue after each phase based on evidence.
- Hypothesis: €5k
- Experiment: €12k
- Model: €25k
Integrated
For time-sensitive decisions or streamlined procurement. Complete technical validation with a guaranteed 8-week timeline.
- Full pilot: €30k
If you're not ready for a full pilot, we can start small. Options mapping, targeted prototypes, or decision audits - tailored to where you are.
Ready to validate your bet?
Book a call to discuss your technical decision. We'll explore how the 8-week pilot could strengthen and de-risk your next big thing.
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------' '-------Or maybe you prefer to DIY...
No problem. If you want to run your own pilot, I've documented my approach. The Software R&D Manual walks through structured validation: gathering the data, designing experiments and building production-ready prototypes.
Email me, and I'll send you the manual. No sign-up needed.
I also write about technical decision-making, R&D processes and emerging technology:
- Your data, their rules: The growing risks of hosting EU data in US clouds.
- Code economics: Why ROI fails to capture the value of platforms and R&D.
- And much more, on my blog.
Frequently asked questions.
Can't I just assign this to my own team?
You absolutely can. If you do, email me and I'll share my Software R&D Manual.
But if your engineering team is busy shipping and you want an unbiased perspective from seasoned R&D specialist, I can help.
What if the pilot fails? Have we just paid you to tell us it won't work?
Pilot failure is a good result! Consider the alternative: assign 3 engineers, work for 6 months and then fail - that'll cost much more and prevent you from pursuing other opportunities in the meanwhile.
If a pilot doesn't turn out as expected, my structured process helps maximise learning, gaining valuable insights about the domain, the use case and the technology. This brings you much closer to knowing what does work.
How much of my team's time will this consume?
The engagement is designed for high autonomy and low overhead. While every project is different, the time commitment from your team typically involves three key touchpoints:
- Kickoff: A single, in-depth workshop at the start where we align on the problem, success criteria, and any essential technical context.
- Check-ins: I provide regular asynchronous updates to keep you informed. We'll only need your team for brief, optional check-ins or if specific technical questions arise.
- Handoff: A final session at the end to walk your team through the deliverables, documentation, and recommended next steps.
Have you worked with our specific stack / domain / industry before?
My experience is broad, spanning cloud infrastructure, machine learning, and application development. But my core value isn't knowledge of a specific stack, it's expertise in the process of technical validation itself.
My role is to be a hands-on specialist who can de-risk new technology efficiently. In cases where a deep, niche challenge arises, I work with a private network of senior specialists to get the right answer quickly, ensuring the pilot maintains momentum.
What exactly does "production-ready alpha" mean?
It means we build the riskiest part of your project first, and we build it right. The goal isn't to deliver a full product in 8 weeks, but to deliver the most critical component to a production-level standard, because that's the best way to prove it works.
While the scope of the alpha will vary for each project, the quality standard doesn't. For me, "production-ready" means the code I deliver will always include:
- An extensive test suite.
- Clear, well-documented code and architecture.
- Observability hooks for logging and monitoring.
- A clear hand-off and instructions for your team to build upon.
Can we stop early if we don't like where things are going?
Yes, absolutely. The engagement is designed with clear "off-ramps" to ensure you always feel in control.
For the Flexible pilot, this is the entire point of the model. Each phase is a distinct commitment, giving you a natural stopping point to decide whether to proceed based on the evidence we've gathered.
For the Integrated pilot, you still have a safety net. Even with the upfront commitment, you can choose to stop after any completed phase. If you do, you receive a 50% refund for all remaining, unstarted work. This gives you a clear, pre-defined exit path while still retaining the benefits of momentum and cost savings.
My goal is to eliminate risk, and that includes the engagement model itself. You always have a way out.
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