Abacum Tripled Revenue in One Year Without Adding a Single Employee

The Round That Closed in June

A financial planning startup tripled revenue in twelve months without adding a single employee, Cathay Capital found. Then it raised $60 million, Cathay Capital reported.

Abacum announced the Series B on June 11, 2025. Scale Venture Partners led. Cathay Innovation joined as a new backer. Existing investors Y Combinator, Creandum, Kfund, and Atomico all doubled down. Total capital raised now exceeds $100 million, Fortune's data shows, the founders say.

Scale's portfolio includes DocuSign, HubSpot, Bill.com, and Box. Partner Stacey Bishop said the industry needs an FP&A platform built for finance but designed for the entire business. She called Abacum's approach innovative, collaborative, and intuitive, qualities legacy platforms have never nailed.

Julio Martínez, Abacum's co-founder and CEO, framed the raise as a response to urgency. Finance teams are demanding AI-powered platforms that let them act as strategic drivers, he said. The company raised this round to meet that demand.

Martínez and Jorge Lluch founded Abacum in 2020. Both are former CFOs. The company is headquartered in New York with offices in London and Barcelona. Over the past year it expanded across 31 countries while keeping headcount flat. More than half of revenue now comes from the U.S., its fastest-growing market.

The fresh capital will fund product innovation and accelerate U.S. growth. The company plans to deepen its AI capabilities, strengthen its data layer, and expand collaborative workflows that put finance in the driver's seat across the organization.

Why AI-Native Means Building the Stack First

Most vendors treat AI as a chatbot bolted onto a legacy stack. Abacum built the stack first.

"Finance is a trust business," Martínez said in a 2025 interview. "You cannot put intelligence on top of chaos and expect credibility. If the data is messy, definitions are inconsistent, and the model is fragile, AI will not fix it. It will simply scale the confusion faster."

That conviction shaped the product from day one. Before the company shipped a single AI feature, it invested in a deterministic foundation: a data layer that normalizes inputs from NetSuite, Sage, Workday, Salesforce, and hundreds of other sources; modeling primitives that reflect how businesses actually operate; and an integration framework that keeps actuals, plans, and assumptions in continuous sync. Only then did the team apply AI, and only where it creates measurable leverage.

The target is high-volume, low-judgment work that consumes disproportionate time and generates errors. Cleaning and normalizing incoming data. Reconciling mismatches across systems. Classifying and tagging transactions at scale. Surfacing anomalies in days, not at month-end. These tasks live inside the workflow, not in a separate conversational interface. "Intelligence lives therein separate chat interface," Martínez said. "We start at the beginning. We apply AI where humans add the least value and make the most errors."

The payoff appears when the foundation holds. Scenario exploration becomes economical. A finance leader can test a hiring freeze, a pricing change, or a new GTM investment in the meeting where the decision is being made — not in a follow-up scheduled for next week. "That is when AI becomes foundational," Martínez said. "Not when it can generate a nice chart or a summary, but when it allows finance to apply rigor fast enough to influence a decision while it is still open."

This approach contrasts with what Martínez calls the industry's default: "Most AI in finance today starts at the end of the workflow. It assumes your data is already clean and governed, then adds a chatbot to query it or summarize insights. That can be helpful, but it skips the hardest part of FP&A."

Abacum also uses AI to lower the complexity tax that keeps finance teams dependent on specialized consultants or "system owners." The platform lets a finance analyst express intent, such as "model revenue by product line with seasonal adjustments," and helps construct the logic correctly, without requiring a dedicated administrator. "Finance teams should be able to express intent and have the system help construct the logic correctly," Martínez said.

The design philosophy rejects two false trade-offs. Legacy tools forced a choice between flexible but fragile spreadsheets and powerful but heavy platforms that demanded months of implementation. The new wave of AI copilots recreates the same trap: easy but shallow assistants on one side, orchestration systems that require learning a new paradigm on the other. "We believe the right answer is AI that disappears into the workflow, improving planning without changing how teams operate," Martínez said.

The boundary for judgment is explicit. AI accelerates analysis and expands the option set. It does not allocate capital, set hiring strategy, determine pricing, or own the board narrative. "As for judgment, the boundary is clear," Martínez said. "AI can accelerate analysis and exploration, but decisions involving capital allocation, hiring trade-offs, pricing, and strategic prioritization still require human context and accountability. The CFO owns the call."

Trust in this model depends on explainability. A CFO must be able to answer, in seconds: what changed, why it changed, which drivers moved, and what assumptions produced the variance. Governance cannot live in static controls; it must be embedded so every scenario leaves a traceable record. "In finance, 'directionally correct' is not good enough," Martínez said. "Finance leaders are accountable for the numbers they present. If you cannot explain a forecast, you cannot use it in a decision conversation."

The result is a platform that deploys in two to six weeks — not quarters — and automates the layers that should run continuously: data consolidation, cleaning, normalization, reconciliation, anomaly detection, and baseline reporting. Forecasting and scenario generation accelerate dramatically but remain under human authority. "AI will make it cheap to explore options and stress-test assumptions, but context, risk, and accountability still matter," Martínez said. "AI changes whether finance can keep up with the pace of decisions. It does not change who is responsible for the outcome."

The Founder Problem and the Market's False Choice

Martínez and Lluch didn't set out to build software. They set out to survive the job. Both had served as CFOs at high-growth companies (Martínez across banking, fintech, and startups; Lluch in similar environments) and both kept hitting the same wall. In executive meetings, someone would ask a reasonable question: "How many months of runway do we really have if we slow hiring?" or "What happens if revenue slips next quarter?" The answer never came in real time.

The math wasn't hard. The structure was. Cash lived in one system. Headcount in another. Revenue somewhere else. Expenses in spreadsheets. To answer confidently, you had to pull everything together, rebuild the model, reconcile discrepancies, and hope nothing broke. By the time the answer arrived, the decision window had closed. "Finance earns its seat at the table through rigor but keeps its seat through timing," he said. "If you cannot show up with confident decision support in minutes or hours, you lose influence, even if your analysis is perfect a week later."

The tooling offered a false choice. Spreadsheets were flexible and fast but fragile and ungoverned. Legacy platforms were powerful but assumed a static business and required heavy administration just to function. Modern companies operate in sprints. Plans shift constantly. Decisions stack up. Finance cannot afford to be the team that is always "coming back with the answer."

Martínez called Lluch, the smartest finance person he knew, expecting to be told there was a better way he had missed. Instead, they compared notes for hours and realized they had lived the same pattern in different environments. Finance teams were drowning in reconciliation, constantly rebuilding models, always one step behind the business. This was not a personal failure or a process issue. It was a structural problem shared across companies.

They validated it the hard way. Martínez personally interviewed over 100 finance leaders and business partners across company sizes and industries, using scripted, recorded conversations. The language changed. The story did not. "We are always rebuilding instead of advising." Y Combinator admission later reinforced that this was a global problem, not a niche one.

COVID hit while they were preparing to jump. The pandemic accelerated the very chaos they had diagnosed (distributed teams, volatile forecasts, weekly plan changes) and confirmed the urgency. They founded Abacum in 2020 to build a planning system that delivered speed and trust simultaneously, so finance could apply rigor early enough to shape direction while choices were still negotiable.

The spreadsheet killer wasn't a metaphor. It was the only way to stop the rebuild cycle.

That same false choice defines the FP&A market today. On one side sit spreadsheets — flexible, fast, familiar, but fragile at scale, ungoverned, and prone to error. On the other sit legacy platforms — powerful in theory, but built for enterprise stability, not the weekly planning cadences of high-growth companies. Newer AI-first rivals tend to optimize for demo-ready copilots that produce impressive outputs but lack traceability. "What made it worse was the false choice finance teams were given," Martínez said. He restated the false choice he had described earlier.

Abacum positions itself as the middle path. The platform centralizes real-time data across hundreds of integrations, supports multi-dimensional modeling, and automates the repetitive consolidation and reporting work that consumes half a finance team's hours. But its competitive wedge is not feature parity — it is architecture. Where most AI-native entrants bolt a chatbot onto clean data, Abacum applies AI at the front of the workflow: cleaning, reconciliation, classification, anomaly detection, and model logic assistance. He repeated his earlier observation.

That distinction matters in the mid-market, where companies operate across multiple geographies and revenue streams but lack the IT resources to babysit a heavy platform. Its bet remained the same. He reiterated his earlier belief.

The competitive moat is not any single feature. It is the combination of speed-to-value, self-service implementation, and SaaS optimization that lets a mid-market finance team go live in weeks, not quarters. When spreadsheet limitations start slowing decision-making and eroding confidence in the numbers, the shift to a platform like Abacum stops being optional and becomes essential. The next wave of FP&A winners will be the ones that make rigor fast enough to shape decisions while they are still negotiable.

Customers Who Outgrew Spreadsheets Before Series B

Abacum's customer roster reads like a roll call of companies that outgrew spreadsheets before they outgrew their Series B. Strava, Aiven, JG Wentworth, Mastercam, Kajabi, RapidSOS — these are not early adopters experimenting with a pilot. They are mid-market operators running complex, multi-entity finance functions that need answers in hours, not weeks. As of June 2025, the platform serves hundreds of companies across 31 countries, with over half of revenue now coming from the U.S. market.

The traction metrics tell a sharper story than any logo slide. Over the 12 months leading into the Series B, Abacum tripled revenue while holding headcount flat, spread across New York, Barcelona, and London. That efficiency metric matters because it mirrors the value proposition Abacum sells: finance teams that scale without linearly scaling headcount.

Customer testimony backs the numbers with operational detail. Richard Harem, head of finance at RapidSOS, said the platform cut his team's headcount needs three-fold and accelerated the reporting cycle by 73 percent. Nico Serventi, director of finance at Kajabi, described a shift from reactive data wrangling to strategic planning: business review meetings now happen within the first five days of the month, and 80 percent of finance time goes to forward-looking work instead of reconciliation. Both quotes appeared in the Series B announcement materials distributed by Cathay Capital.

The partnership with AccountsIQ adds a second validation layer. Investment bank GP Bullhound now runs on the combined stack: AccountsIQ handling multi-entity consolidation with built-in FX across GBP, USD, and EUR, while Abacum sits above as an intelligence layer linking financial information across systems in real time. AccountsIQ's integrations team built a dedicated Abacum connector to replace what had been a manual upload process. The firms frame the tie-up as part of a broader European shift from historical reporting toward AI-supported forward planning — a shift Abacum aims to lead globally.

Abacum's Built-for-NetSuite certification (earned in July 2026) signals that the platform meets mid-market buyers where their data already lives. Scott Derksen, Oracle NetSuite's vice president of partnerships and business development, said the integration reduces manual data work and unlocks real-time insights for joint customers. The certification matters because NetSuite remains the ERP backbone for a vast slice of the target segment; a native connector removes a common adoption blocker.

The geographic spread reflects a deliberate go-to-market motion rather than organic drift. Martínez has said U.S. companies move faster and demand more operational CFOs, which makes the pain of slow planning more acute. That market now drives the majority of revenue.

Where the $60 Million Goes

The capital breaks down along two axes: geographic expansion and product depth. Abacum has earmarked the Series B to drive U.S. growth (a market that already delivers roughly half of revenue) while doubling down on four product pillars the company says will move FP&A from reactive reporting to proactive intelligence.

Scale Venture Partners led the round with that split in mind. "Finance teams are eager for change," Martínez said in June 2025. "They're demanding AI-powered platforms that enable them to be strategic drivers within their business. We did so very demand with urgency." The U.S. push is not speculative; Abacum's existing customer base (Strava, Kajabi) skews heavily American, and the company maintains a New York headquarters alongside offices in Barcelona and London.

On the product side, the roadmap is specific. Multi-agentic AI sits at the top: systems that anticipate business needs, surface insights without prompting, and help teams navigate complexity rather than just execute predefined workflows. A rebuilt modeling engine targets faster scenario planning, critical when CFOs are re-forecasting quarterly or monthly instead of annually. A "robust data layer that becomes a single source of truth" aims to eliminate the integration fragility that keeps finance teams tethered to spreadsheets. And collaborative workflows are designed to do so, letting the department share live insights across the organization instead of emailing static decks.

The hiring plan reflects that product ambition. Abacum added six roles in a single week in early 2026: Strategic Initiatives Lead in New York, Strategy & Operations Lead split between Barcelona and New York, Lifecycle Marketing Manager, IT Support Specialist in Buenos Aires, Finance Solutions Engineer for EPM in Toronto, and a Senior Back End Engineer focused on integrations. Board data shows a salary band across salaried roles (detailed in the table below).

Notably, Abacum has tripled revenue over the past 12 months without increasing headcount, a discipline Martínez has defended publicly. "When we reached that moment at Abacum, we paused," he wrote in February 2026. "Not because we were trying to run lean at all costs... but because hiring felt like the safe answer. And we wanted to understand whether we were building a company to scale or if we were about to mask deeper problems with headcount." The Series B does not abandon that philosophy; it funds the product leverage that makes the philosophy viable. The new capital buys the AI architecture that lets a flat team serve a growing customer base across 31 countries and the U.S. go-to-market motion to convert that leverage into market share.

The Shift Reshaping Tech Finance

Abacum's $60 million Series B is not an isolated financing event. It is a data point in a structural shift that Deloitte's 2026 financial services outlook describes as a rebuilding of the industry's operational infrastructure — legacy systems and manual processes giving way to technology upgrades with artificial intelligence as the execution engine. The firm projects 30 to 100 percent productivity gains by 2032, potentially freeing 25 to 50 percent of adviser time currently spent on operational tasks.

The shift is already visible in hiring patterns. The same Deloitte research that forecasts productivity gains also finds that 7 in 10 business leaders say their primary competitive strategy over the next three years is to be fast and nimble. Speed now outpaces scale. Finance teams that still consolidate data manually across disconnected systems cannot move at that speed.

The trap, Deloitte warns, is treating AI as a technology deployment rather than a work redesign. Organizations taking a tech-focused approach to AI are 1.6 times more likely to miss return expectations than those taking a human-centric approach. The distinction matters for FP&A. A platform that automates variance analysis but leaves the CFO manually checking formulas has not solved the problem. It has only moved the bottleneck. Abacum's founders, both former CFOs, built the product because they lived that bottleneck. Their platform keeps data, plans, and outputs aligned so teams focus on decisions instead of maintenance. That design philosophy mirrors Deloitte's finding that organizations intentionally redesigning roles, workflows, and decision-making for human-AI collaboration are more likely to exceed investment returns and deliver meaningful work.

The implications extend beyond the finance function. Retail executives offer a preview: 68 percent expect to deploy agentic AI for key operational and enterprise activities within 12 to 24 months. By 2030, AI agents could handle 25 percent of global e-commerce sales. Tech finance teams will face the same pressure: board members asking why forecasting still takes two weeks when competitors run scenarios in hours. Deloitte notes that 44 percent of retail respondents say legacy systems are slowing innovation. The same dynamic plays out in mid-market tech companies running on ERP implementations that predate their current product lines.

Capital follows capability. Scale Venture Partners led this round because AI-native FP&A represents a new category of enterprise software — one where AI operates as the core execution engine rather than a supporting layer. Deloitte predicts that by 2030, such AI-native products could account for up to 25 percent of institutional banking revenues among the top 50 U.S. banks, representing incremental revenue in the range tabled above. The same logic applies to the companies Abacum serves: Strava, Kajabi. Their finance teams are not cost centers. They are decision engines. The tools they use determine how fast the company can pivot, price, hire, or acquire.

The next phase is not automation. It is orchestration. Deloitte's human capital research argues that advantage is shifting from allocating talent in static structures to orchestrating people, skills, data, and technology in real time. For a CFO, that means the FP&A platform becomes the control surface — not the spreadsheet. Abacum's raise bets on that transition. The companies that make it first will not just report results faster. They will decide faster — while the spreadsheet is still recalculating.

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