The person
who answers “yes.”
Product builder, commercial strategist and AI systems architect. 20+ years at the intersection of business and technology — all of it applied to the question every founder eventually asks.
20 years of building.
All of it counts.
My background spans commercial strategy, digital product ownership and hands-on development. Before building products from scratch, I spent two decades at the intersection of business strategy and technology — advising organisations, leading digital transformations and owning products from brief to live launch.
I understand what makes a business work commercially. I also understand what makes software actually ship. Those two things together are rarer than most people think — and they're the combination that gets a startup from idea to live product without the wheels coming off.
I've been building with AI coding tools for nearly two years — earlier than most people took them seriously as a production option. I've watched the models evolve from genuinely impressive-but-architecturally-dangerous to production-ready. That experience means I know exactly where to trust them and where not to. It's earned perspective, not a trend I discovered last month.
Core expertise
“Anders came in when we had an idea and no way to execute. He answered the technical questions, built the product and kept us commercially grounded throughout. From first conversation to live platform was six months.”
Nearly two years
in the arena.
Not a recent convert. Someone who has built through the full evolution of AI coding tools — and learned which lessons matter.
Impressive. But dangerous if you didn't know what you were looking at.
The early models could produce working code fast. But the architecture was often a mess — technically functional, structurally fragile. If you didn't have enough engineering experience to audit what the model produced, you'd end up with code that worked until it didn't, and nobody could explain why.
Production-ready. With the right operator.
The models are now genuinely capable of producing clean, maintainable, production-grade code and architecture. The difference between a good outcome and a bad one is the human in the loop — someone with enough commercial and technical experience to direct, review and make the judgment calls the model can't.
The real advantage isn't using AI — it's knowing where the boundaries are. I've built four live products with these tools. I know which architectural decisions to make early, which shortcuts create technical debt, and which model outputs need a human rewrite. That judgment is what turns AI coding from a liability into a genuine competitive advantage.
Agentic AI that
reasons to outcomes.
Not prompts wrapped in a UI. Complete reasoning systems where every step has intent, every signal drives a decision, and every output creates measurable business value. All three are in production.
A complete compliance reasoning chain. The system doesn't just fill in form fields — it understands the regulation, maps it to your data, identifies what's missing and generates the evidence trail that makes the report auditable.
Every piece of content is informed by brand DNA, audience understanding and campaign intent — not just a prompt. The workflow ensures consistency, relevance and commercial purpose across every output at scale, without a dedicated content team.
From raw company registry signal to a personalised sales conversation — fully automated. Detects buying intent signals, qualifies against ideal customer profile, surfaces the right decision-maker and generates the opening line before your competitor even knows the company exists.