Fern Capital: Payments Advisory and Merchant Services Roll-Up
Twenty years building payments businesses. Now advising the sponsors who buy them, and assembling my own roll-up. I work with a small number of funds, founders and CEOs on commercial due diligence, payments product launches and the agentic systems that take cost out of finance operations. The roll-up is a software-led merchant services platform, built from the independent sales organizations that process payments for America’s small and medium businesses.
Advising the funds that back payments companies, and the teams that build them.
For private equity sponsors underwriting payments and finance-software targets, software companies adding payments, and fintech founders and CEOs preparing a raise or a sale.
Commercial Due Diligence
Independent commercial reads on targets: customer economics, retention and churn mechanics, pricing power, and the operational realities that do not surface in a data room. And the thesis work before it: which sub-sectors of payments and fintech are structurally attractive, which are value traps, and where consolidation math works. The output is a point of view you can commit capital behind and defend at an IC, plus personal introductions to the operators and experts who know the sector.
Payments Launches & Founder Advisory
Taking payments products and services to market: proof of concept, partner RFP and selection, then commercial launch. And work alongside founders and management teams: pressure-testing the business model, building unit economics that survive scrutiny, sequencing a raise, and preparing for institutional diligence before it arrives. I lead the work, with a team of payments consultants behind me, and I ran the P&L for eleven years.
Agentic Systems Inside Portfolio Companies
Dedicated engineering pods design, build and operate agentic AI systems inside operating companies, with the EBITDA impact modeled before anything gets built. Finance and payments operations are home field: AP and AR, reconciliation, exception handling, fraud review, month-end close. It starts with a fixed-fee diagnostic of one company or the whole portfolio, and the team that owns the company keeps what ships. I run my own firm this way today.
Agentic systems, built and operated inside your portfolio.
I run an agentic software factory: dedicated engineering pods that design, build, and operate agentic AI systems inside operating companies. For private equity funds, family offices, and their portfolio companies, that means production systems that take structural cost out of operations and add capabilities you could not hire for. They reach production within weeks, and the work is directed by someone who has run the P&L those systems touch.
I have been doing this work myself for years. Finexio ran machine learning in production for a decade, predicting the right payment method across a 500,000-supplier network, and I run my own firm the same way today: parallel coding agents, autonomous content and CRM workflows, a self-hosted stack I supervise from my phone. The Engine Room is that operating model, made available to a portfolio.
Private equity funds
Value creation you can underwrite. EBITDA impact is modeled per company before anything gets built, and shows up in the board pack after: margin, headcount efficiency, and payback.
Family offices
Direct holdings and operating companies where margin is trapped in labor-heavy workflows. One diagnostic across the holdings, then transformation where the math is best, reported the way a principal reads.
Portfolio companies
An embedded engineering pod that ships production systems rather than strategy memos. Management keeps its team, and the team gains engineering capacity it could not hire in a year.
What gets built.
Production systems from day oneFinance & payments operations Home field
AP and AR automation, reconciliation, exception handling, payment operations, fraud review, month-end close. These are the workflows where money moves, and I have run every one of them myself at scale.
Revenue & go-to-market
Outbound and inbox agents, pipeline orchestration, pricing intelligence, and content produced at portfolio scale in the company’s own voice. Same team, more revenue per head.
Back office, legal & support
Support deflection, document intelligence, contract drafting and pre-signature risk review, compliance operations. The recurring labor every operating company pays for and none enjoys.
Custom platforms & data infrastructure
When the answer is a new system rather than a bolt-on: full-stack product engineering, data pipelines and warehousing, ML infrastructure and evaluation harnesses, and where the thesis calls for it, tokenization and crypto rails.
How an engagement runs.
Same sequence every timeDiagnose
A two-to-three week scan of the company or the full portfolio: workflows, headcount, vendor spend, friction. The output is a board-ready opportunity map, ranked by EBITDA impact.
Design
The target operating model: which functions go agentic, which people become supervisors of agents rather than doers of tasks, which tools and vendors get retired.
Build
Engineering pods ship the systems inside the company. First systems reach production within weeks, because most of the underlying infrastructure already exists.
Sustain
Delivered systems ship with clean handover and a maintenance retainer that keeps agents tuned and models current. How much further the relationship goes is your choice, not a requirement of the model.
Every build makes the next one cheaper.
Systems are assembled from a hardened library of working components: ingestion, retrieval, evaluation, observability, integrations, and whole agent workflows proven in prior deployments. A system built for one company becomes the starting point for the next. Over time the fund holds an internal library of working AI assets no consultancy can sell it, and each engagement starts further ahead than the last. The full effect arrives when the same standardized systems run across the entire portfolio.
Reusable assets, compounding per engagement
Three ways in.
Project-based or fully embeddedPortfolio Diagnostic
Fixed fee · 2–3 weeks
A scan of the holdings. I identify the companies with the highest transformation ROI and deliver a ranked, board-ready plan with modeled EBITDA ranges and recommended sequencing.
Build & Deliver
Fixed + gain-share on delivered value
We come into one company, build the systems, and get paid on what ships and what it saves. Your team owns what ships and keeps the keys. Maintenance runs on a light retainer, and how far the relationship goes from there is your call.
Embedded AI Partner
Retainer · portfolio-wide
Fern becomes the AI transformation and value-creation function for the fund: the operating-partner and lead-engineering capability, at a fraction of the cost of hiring it full time. Scope can stay concentrated on a few holdings or run across the entire portfolio, with quarterly reporting fit for LPs.
Why run it through Fern.
Run by an operatorOperator judgment
I know which workflows survive automation and which quietly break, because I ran them at $7B+ of annual payment volume: enrollment, reconciliation, risk, support. Most AI shops learn this at your portfolio’s expense.
Payments, finance & regulated depth
Money movement, fraud, bank-grade compliance, healthcare, and the full office of the CFO: AP, AR, close, treasury, collections. The domains where generic dev shops stall are the domains where I have spent twenty years. If your holdings touch money, that is the difference.
Delivery at scale
Delivery runs through dedicated engineering pods under my direction, staffed with senior AI/ML, data, and full-stack engineers. Every engagement stays direct. I run the diagnostic and I own the outcome.
Start with one company.
Tell me about your portfolio and the one holding you would transform first. I will come back with a candidate workflow, what it costs you today, and what it is worth transformed.
Assembling a software-led merchant services platform.
Independent sales organizations, the ISOs, sign up and serve the small and medium businesses that take card payments in America. Most are owner-run, decades old, profitable, and too small to build the software their merchants now expect. The thesis is to bring a number of them together under one platform, with an AI-run back office, and keep the people who built them close. I am leading it as executive chairman, setting the technology and M&A strategy, with capital partners who know the sector.
Why now
- The founders who built the US ISO base are reaching the end of their chapter, and most have never engaged a banker or a buyer.
- Small-merchant volume is moving to software platforms, and a standalone ISO cannot fund a software build on its own.
- The same book of merchants is worth materially more inside a scaled, software-led acquirer than inside a small ISO.
- AI has changed what a back office costs. A merchant portal, reconciliation and reporting that once took years to build now take months.
How it works
- Start with one wholesale platform ISO that already holds its own bank sponsorship, then add smaller ISOs, agent offices and residual streams around it.
- Rent what is commodity, build what runs on our own data, and own what matters: the merchant contracts, the residuals, and the pricing.
- Run every deal through an AI-built automation engine: diligence tools, marketing and sales automation, and strict criteria applied across the board.
- Keep the owners and their teams close. Every deal is a direct conversation with the founder, never an auction.
The first acquisition
A wholesale ISO with its own bank sponsorship and BIN, a stable owner-led team, and merchant attrition that says the book is cared for. Southeast preferred, not required. A founder who wants a partner for the next chapter, not an exit from it.
Everything that follows
Smaller ISOs, agent offices and residual streams that fit the platform’s processors and sponsor banks. Vertical software businesses with embedded payments where the merchants are already ours to serve better.
The roll-up’s AI back office, built in public.
The back office is being built before the first acquisition closes, and the first AI tools are free for any ISO to use today. Both are open source, and both run on the residual reports every ISO already has.
OpenResidual
Residuals are an ISO’s revenue, and most owners cannot verify what their processor pays them. OpenResidual checks the processor’s file against your Schedule A, finds every short merchant and drafts the dispute. The residual analyst most ISOs cannot afford to hire.
OpenRetain
Merchant attrition is one of the biggest problems an ISO faces, and every merchant lost takes its residual with it. OpenRetain finds the merchants at risk of leaving while there is still time to keep them, and tells you who to call first.
Three payments businesses built from zero.
The full arc of a payments company, from origination to institutional scale to transition, is one most founders never complete. This is the ground I have covered, as an operator and as the person the banks backed.
Finexio
Founder and CEO for eleven years. Built from an idea to more than $7 billion in annual payment volume, $17.5 million in ARR and a 500,000-supplier network, with J.P. Morgan as largest investor and roughly $100 million raised from five publicly traded banks. Left on my own terms with the business still growing.
Change Healthcare
Vice president, strategic partnerships and corporate development, focused on payments and fintech. Grew the payments business to $80 million in annual revenue and $3.5 billion in annual payments through virtual card adoption, inside a $3 billion healthcare technology company.
Mastercard
Vice president. Led fintech partnership initiatives for what would now be called embedded payments, and ran a $60 million revenue healthcare payments portfolio for the network.
EY
Founding member of the West Coast working-capital practice, advising the office of the CFO on cash, payables and receivables.
Eleven years in the hardest part of payments.
I founded Finexio in 2015 and led it as CEO for eleven years, building it from an idea into a category leader in embedded B2B payments.
B2B payments looks like a software problem because the volumes are enormous, but the volumes are not the hard part. The hard part is the middle: enrolling hundreds of thousands of suppliers, executing across every payment rail, carrying the fraud and risk burden, and passing bank-grade compliance, all while keeping the economics intact. Most platforms underestimate it and stall.
We built Finexio to more than $7 billion in annual payment volume, $17.5 million in ARR, and a network of over 500,000 suppliers, with net revenue retention around 140% and payment contribution margins roughly twice the industry average, because we solved the operational grind, not just the interface.
Fewer than 2% of U.S. fintech companies raised the capital Finexio did over the past decade. I led the company for all eleven of those years and left it growing.
Founder-CEO
Concept to category leader, eleven years leading it.
Capital Markets
~$100M raised, from bank strategics, not momentum money.
Full Cycle
Built, financed, and transitioned on my own terms.
Why Fern
One of the oldest plants on earth, resilient, self-sufficient, and quietly enduring.
A symbol of new beginnings and quiet confidence.
Start the conversation.
Every engagement, and every acquisition, begins with a private conversation.
Thank you. Your note is with me.
I read every one myself and reply by email. If it is easier to talk, take a time below.
Book a 30‑minute conversationStill checking that you are a person. Press Send again in a moment.
That did not go through. Please email er@ferncap.com directly. It reaches the same place.
Or reach me directly.