My name is Martin Koderisch. I advise payments and fintech businesses on significant commercial decisions, working with executive teams and boards to bring an independent, outside-in view.
Engagements typically run four to six weeks at a fixed price, or longer depending on what you need. I start on the analysis instead of spending the first month learning your market on your time and budget. Nothing is adapted from a previous client.
Usually the executive team already suspects the answer; what is missing is a case solid enough to act on. The five below run in the order the questions usually arrive, from testing whether the growth is real through to sequencing what gets built. Each one stands alone: most clients need one, not the sequence.
The same method gets pointed at pricing, market entry, capital allocation and go-to-market. If your question is not on this list, it is probably still the same work.
Built the strategic and valuation case for a pivot and replatforming decision, and presented it for board approval. Then designed the value creation plan and its roll-up to a C-suite dashboard, and the data and AI strategy for the replatforming.
B2B segmentation across 13 markets, connecting acquisition, retention and capability gaps to one investment view. Acquisition rates turned out to be broadly uniform, which meant retention was the real differentiator.
A private equity investor was looking at a pre-IPO round in an AI-native fraud platform, raised ahead of a NASDAQ listing. Led commercial due diligence on the unit economics, scalability and competitive durability, and modelled yield and margin over a seven-year hold. At Edgar, Dunn & Company.
When an engagement calls for a model or a prototype, I build it rather than commission it. Strafi and the prototypes below are the same kind of building, done on my own time.
Reads a business's accounting data and tells the owner what it means, in plain English, over WhatsApp (strafi.co). Python and Flask on PostgreSQL, with an LLM reasoning layer on the Anthropic API, and live integrations with FreeAgent, Xero, Stripe, HubSpot and Companies House.
Python and Next.js, on the questions the engagements keep raising: the economics of AI investment at scale, agent readiness scoring for payments APIs, diagnostics for growth-stage businesses.
All of it built for the known failure mode, which is that a model will produce a fluent answer from thin or inconsistent data. Most of the work goes into constraining what a system is permitted to conclude, and making the basis for each conclusion inspectable.
Where the thinking behind the engagements gets worked out in public.
Nearly a decade as a Principal at Edgar, Dunn & Company, the specialist payments consultancy, across more than 75 engagements for banks, fintechs, networks, private equity firms and enterprise merchants. Multiple pre-deal commercial due diligence mandates, and expert witness work on interchange and platform disputes.
Before that, a decade in senior operating roles at Mastercard, Citi and the fintech Luup. Co-author of Price Management in Financial Services (Springer).
I have worked on AI projects for most of the past decade, though until Strafi these were machine learning rather than LLM.
The practical effect is that I do not need a discovery phase to understand a payments business, or a platform whose economics are driven by payment flows. That is what makes short, fixed-price engagements possible.
Best fit is pre-deal work or a fast commercial read for a PE-backed payments, fintech or platform business.
martin@scalepointpartners.com