Fern Capital
I built and scaled a category-leading B2B payments company over eleven years, then financed it through five publicly traded banks. I now work with sponsors on theses, diligence, and introductions I can actually make, and with their portfolio companies on the agent systems that take structural cost out of finance and back-office operations. Payments and the office of the CFO are where I go deepest. The build capability goes anywhere.
Begin a conversationDeep in one place. Useful across the portfolio.
I work directly with a small number of sponsors and the companies they own. My depth is payments and the office of the CFO: the finance, treasury, and money-movement functions every company runs, and the ones I ran myself at scale for eleven years. That work travels. The diligence, the operating judgment, and the agent systems apply wherever a portfolio company's margin is trapped in manual process.
I lead every engagement personally, and I bring in senior specialists in AI/ML, data, and full-stack when the build calls for them. Judgment from someone who has run the P&L, with the bench to actually ship.
Eleven years as founder-CEO. $7B+ in annual payment volume, 500,000 suppliers.
~$100M raised across eleven years, from five publicly traded banks.
Advisory through build. Principal-led, with engineering behind it.
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.
Concept to category leader, eleven years leading it.
~$100M raised, from bank strategics, not momentum money.
Built, financed, and transitioned on my own terms.
Built, financed, and led.
The full arc of a payments company, origination to institutional scale to transition, is one most founders never complete. This is the ground I have actually covered.
Build
Finexio, from concept to $7B+ in annual payment volume and a 500,000-supplier network, with rails embedded inside the world’s largest accounts payable and procurement platforms, with an AI/ML engine predicting the right payment method for each supplier.
Raise
Roughly $100M in equity and debt across eleven years, from five publicly traded banks, with J.P. Morgan as largest investor and strategic network partner. When the market turned in 2022, I engineered equity and debt to carry us through without a down round.
Lead
Eleven years leading the company through hypergrowth, through a market that turned in 2022, and through a planned leadership transition rather than a forced one. I left on my own terms with the business still growing.
Where I’m spending my time.
Advisory work follows conviction. These are the four areas I am actively researching, building networks around, and working in, with sponsors, founders, and operators already engaged in each. These are investment theses, not the limits of where I work. Engagements run wherever a sponsor needs operator judgment or a portfolio company needs systems built.
The agentic factory
Two things are happening at once. Agent fleets are absorbing operational work inside financial services businesses: enrollment, exception handling, reconciliation, diligence, support. And agents are starting to build and service software products autonomously, not just assist the people doing it. I run both inside my own firm, on a self-hosted stack with parallel coding agents supervised across persistent workspaces. For a sponsor these are separate underwriting questions. The first changes what a portfolio company costs to operate. The second changes what a software business is worth, because it moves the floor on what it costs to build one.
Agentic payments
Software agents are beginning to initiate and authorize purchases. That breaks assumptions the payment rails were built on: identity, authorization, dispute rights, and who carries liability when no human clicked. The infrastructure to make agent-initiated payments safe and settleable is being built right now, and it does not yet have obvious winners.
TradFi and crypto, both directions
The value is at the bridge, not at either end. Payment routing across multiple chains and networks, bank deposits moving to tokenized deposits, and stablecoin reserve yield: who earns it, who shares it, and how it prices into a platform’s economics. I have done the work on both sides, including tokenized money-market integration and reserve revenue-share design for a platform holding roughly $2 billion in customer deposits.
Merchant services consolidation
The acquiring and payment-platform landscape is fragmented, sub-scale, and priced accordingly. The thesis is consolidation: assembling independent processors, ISOs, and vertical software payments businesses into one better-capitalized platform with unified economics. I am working this as a director and advisor who stays close to the deal.
Agent systems, built and operated inside your portfolio.
I run an agentic software factory: dedicated engineering pods that design, build, and operate AI agent 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 Factory 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.
Sponsor-side and company-side.
Most engagements start with a fund and end up inside a company, or the reverse.
Investment Strategy & Thesis
Helping funds decide where to spend attention, with payments and fintech as the areas I underwrite deepest: which sub-sectors are structurally attractive, which are value traps, where consolidation math actually works, and what the next two years of market structure look like. The output is a point of view you can commit capital behind and defend at an IC.
Agentic Transformation
Agentic AI transformation for funds, family offices, and their portfolio companies. Dedicated engineering pods design, build, and operate AI agent systems inside operating companies, with EBITDA impact modeled before anything gets built. The full offering is The Factory, above.
Commercial Diligence
Independent commercial reads on targets: customer economics, retention and churn mechanics, competitive position, pricing power, and the operational realities that do not surface in a data room. Strongest in payments and fintech, and in any target with a heavy finance-operations component, where the cost structure lives in process rather than product. I built and ran the operations in this sector, which means I know which claims survive contact with the operations and which are easy to present well.
Network & Introductions
Building the map: who the acquirable assets are, who the operators worth backing are, and which industry experts are worth an hour. Then customer and partnership introductions on the growth side. I make the introductions personally, from relationships I have held for years.
Founder & Operator Advisory
Company-side work for founders and management teams: pressure-testing the business model, building unit economics that survive scrutiny, positioning and sequencing a raise, and preparing for institutional diligence before it shows up. Private, direct, and written down.
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 begins with a private conversation. We would be glad to hear what you are working toward.