Fintower Raises €1.5M to Replace Excel FP&A

Swedish AI startup Fintower closes €1.5M seed as finance teams push beyond spreadsheets

A Gothenburg-based AI company, Fintower, has secured €1.5 million in an oversubscribed seed round, signaling strong investor appetite for tools that modernize how companies plan, forecast, and make decisions. The round brought together a mix of new and returning backers, including Chalmers Ventures, Akka, and the Stena family (via William Olsson), alongside entrepreneurs and angel investors. Prior supporters also participated, including Almi, Daniel Jonsson (Inet), and Alexander Hars, reinforcing confidence from those who have already been close to the company’s early journey.

Behind the headline number is a familiar story playing out across finance departments worldwide: leaders are under pressure to produce faster insights, explain performance drivers in plain language, and connect financial planning to operational reality—all while markets shift quickly and board expectations rise. Many teams still rely on patchworks of spreadsheets and disconnected systems, which can work in stable environments but often break down when business models evolve, product lines multiply, or headcount changes accelerate. In those moments, “planning” becomes less about strategy and more about chasing versions of the truth.

Fintower is positioning itself as a response to that friction: a platform designed to consolidate budgets, forecasts, and reporting in one place, with AI features built to support analysis and scenario simulation without forcing teams to stitch together multiple tools. In practical terms, that means reducing the time spent reconciling numbers and increasing the time spent answering the questions leadership actually cares about—what’s moving revenue, what’s driving costs, and what happens if assumptions change next quarter.

From the lens of the ai world organisation, this kind of funding moment matters because it shows where applied AI is delivering immediate business value: not only in customer-facing features, but in the internal “operating system” of companies—how they plan, measure, and decide. That’s a theme we regularly explore across ai world organisation events and the ai world summit, where practitioners and builders compare what works in real deployments, not just in demos. The conversation also fits naturally into the agenda of ai conferences by ai world, where finance leaders, founders, and operators can align on the next wave of AI-enabled decision support.

Why this round is a signal: the market is demanding planning that moves at business speed

Oversubscription is more than a vanity metric; it often indicates that multiple investors see the same pain point and believe timing is right for a solution that can scale. In this case, Fintower’s co-founder Salman Eskandari has pointed to unusually strong demand, saying the company had to expand the round and still couldn’t accommodate everyone who wanted in—an indicator, in his view, that the product is meeting a real need. He also framed the broader shift succinctly: companies are increasingly ready to move away from Excel-based planning and are asking for modern tools that improve analysis and decision support.

That shift has been brewing for years, but several forces have made it feel urgent. First, the cadence of business has changed. Planning cycles that used to be quarterly or annual are increasingly “always on,” because pricing, demand, hiring plans, and capital costs can change fast. Second, leadership teams want planning to reflect the business as it actually operates—by product, customer segments, channels, sales motions, and teams—rather than purely by ledger categories. Third, cross-functional decision-making is now normal: finance needs to partner with revenue, product, operations, and people teams in a shared model, not as a downstream reporting function.

Spreadsheets are incredibly flexible, which is precisely why they persist. But flexibility comes with trade-offs: version control headaches, fragile formulas, lack of auditability, and knowledge trapped inside individuals’ files. When finance teams scale, they often discover that “everyone has the spreadsheet” doesn’t mean “everyone agrees on the numbers.” The cost isn’t only wasted time—it’s delayed decisions, missed opportunities, and the risk of steering based on outdated assumptions.

What is changing in 2026 is the expectation that planning should behave more like a living system than a document. Leaders want to test scenarios quickly, see drivers clearly, and align operational choices with financial outcomes. Investors, meanwhile, want to back platforms that can become infrastructure—software a company keeps for years, not a point tool they churn after a budget season. In that environment, a focused, well-designed FP&A platform can be a strategic wedge into the CFO stack.

From an ecosystem standpoint, this is exactly the kind of real-world adoption story that resonates at the ai world summit 2025 and ai world summit 2026: not AI in the abstract, but AI that shortens cycles, improves accuracy, and makes teams more resilient. For founders and operators attending ai world organisation events, it’s also a reminder that some of the most scalable AI businesses emerge where the pain is persistent and measurable—like forecasting accuracy, reporting latency, and planning agility.

What Fintower is building: one hub for budgets, forecasts, reports—and AI-driven analysis

Fintower was founded by Salman Eskandari and Ehsan Yazdani with the goal of changing how companies work with finance, particularly in a world where planning is still often done manually in spreadsheets. Their argument is simple: when data is spread across multiple systems and files, analysis gets slower, updates become brittle, and teams struggle to keep models in sync with reality. Instead, Fintower aims to bring budgets, forecasts, and reporting into a single environment, reducing fragmentation and helping teams work from a shared source of truth.

The AI layer is designed to make the model more usable, not just more complex. The company describes capabilities such as helping users understand what drives revenue and simulate the impact of different changes—without forcing them to jump between disconnected systems. That emphasis matters because many finance tools “store” data well but still leave users doing the hardest work manually: interpreting relationships, explaining variances, and turning a list of numbers into a decision.

Yazdani has also highlighted an important design choice: many financial systems are organized around accounting charts, while businesses often need to plan around operational realities like products, sales, and personnel; Fintower has focused on connecting finance and operations in the same system. In practice, this is the difference between a model that explains what happened (accounting) and a model that helps decide what to do next (planning). When product strategy changes, when a sales org restructures, when hiring pauses or accelerates, leaders want the planning model to reflect those levers directly.

For growth companies, this connection is especially critical. Funding plans, runway, headcount timing, and revenue assumptions are tightly linked. A small change in conversion rates or sales cycle length can ripple through hiring plans, cash requirements, and strategic priorities. If planning systems can’t capture those relationships cleanly, finance becomes reactive—constantly patching and explaining—rather than proactive.

This is why the “replace Excel” narrative resonates: it isn’t a critique of spreadsheets as a tool; it’s a critique of spreadsheets as a platform. Spreadsheets don’t enforce governance by default. They don’t naturally support multi-user workflows, audit trails, consistent business logic, and controlled access across teams. A purpose-built platform can—if it’s designed with real finance workflows in mind.

At the ai world organisation, we see the same pattern across industries: applied AI wins when it complements how teams actually work and reduces the coordination cost inside organizations. That’s why stories like Fintower’s fit naturally into programming at the ai world summit, where the most valuable sessions often revolve around operational deployments—how teams structure data, build trust in models, and integrate AI into day-to-day decision-making without adding risk.

Who backed the seed round—and why investors see a scaling opportunity

In this seed round, Fintower attracted both new and existing investors, including Chalmers Ventures, Akka, and the Stena family (through William Olsson), alongside additional entrepreneurs and angel investors. Returning participation also included Almi, Daniel Jonsson (Inet), and Alexander Hars. The total raised was €1.5 million.

Chalmers Ventures, one of the investors, framed the opportunity as helping growth companies move from Excel to intelligent financial decision support, emphasizing the company’s AI-based platform and a team combining technical depth with business understanding. That perspective is worth unpacking, because it reveals what many early-stage investors look for in B2B AI: a clear customer pain, a product that can become central to workflows, and a team capable of shipping quickly while understanding business constraints.

FP&A is a market with strong expansion dynamics. A tool may start in finance, but planning touches every department: sales leadership cares about pipeline and quota capacity; product teams care about roadmaps and resource allocation; operations teams care about demand and supply constraints; people teams care about hiring, retention, and compensation planning. If a platform becomes the shared planning layer across functions, it becomes harder to replace—and easier to expand.

There’s also a defensibility angle. The best planning tools accumulate context: driver trees, scenario histories, approval workflows, and the “why” behind decisions. As organizations grow, those artifacts become institutional memory. When a platform captures this well, it doesn’t just store numbers—it stores decision logic. That creates long-term value for customers and, by extension, long-term retention for the vendor.

At the same time, investors are realistic about what it takes to win. Finance leaders expect reliability, governance, and clarity. They want a product that is intuitive for everyday use but rigorous enough for board conversations. They want integrations that reduce manual reconciliation and reporting that can be trusted. They also want AI features that feel safe—explainable enough for stakeholders and grounded in the business model, not just pattern-matched outputs.

This is where seed funding becomes meaningful: it gives a company the room to build for durability—better product depth, stronger customer onboarding, and a repeatable go-to-market motion—rather than chasing shallow growth. Fintower has indicated it will use the new capital to further develop the product and build a sustainable business around the solution it already has in market.

For communities like ours at the ai world organisation, investor rationales like these are gold: they help operators understand what “credible AI” looks like in 2026—AI that integrates with workflows, improves decisions, and can be adopted without creating operational chaos. It’s the kind of case study that fits well into panels and roundtables at ai world organisation events, especially when the conversation moves from “what is possible” to “what actually delivers ROI.”

Early traction, use cases, and what this funding suggests for AI in finance

Since its first funding round, Fintower has attracted customers across industries including tech and software, credit institutions, retail, and energy. The company notes these customers share a common pattern: high expectations for control across financial and operational management, often in environments shaped by external funding and growth goals. That mix is telling, because it suggests the platform is not confined to one niche; rather, it targets a universal workflow problem that manifests differently across sectors.

In tech and software, finance teams often live inside recurring revenue metrics, cohort behavior, and complex pricing models. Planning isn’t just “last year plus 10%”; it’s a constantly updated view of retention, expansion, acquisition costs, and product-led growth dynamics. In credit institutions, planning intersects with risk, capital requirements, and macro conditions. In retail, seasonality and inventory dynamics can dominate forecasts, while energy organizations face volatility and long-term investment horizons. Across all of these, the shared requirement is a planning model that can adapt quickly and remain understandable.

This is where AI can add leverage—when it helps teams interpret the model and stress-test assumptions faster than manual workflows allow. A finance leader doesn’t only need a forecast; they need the narrative of the forecast. They need to explain what changed, why it changed, and what choices are available next. If AI features can reduce the time to insight and improve consistency in analysis, finance teams can shift from “reporting what happened” to “shaping what happens next.”

But the shift isn’t only technological; it’s cultural. Finance departments are changing roles. Repetitive work is increasingly automated, and the future of finance looks more like business partnering than spreadsheet maintenance—an idea Fintower’s leadership has emphasized as part of their direction. In that world, the best tools are those that make finance more strategic without sacrificing rigor.

Fintower has also shared an ambition to become the leading platform in the Nordics, aligning product development with a clear regional leadership goal. That matters because regional dominance can create momentum: local networks, referrals, partner ecosystems, and credibility with mid-market and enterprise buyers. If a platform becomes “the standard” in one geography, it can later expand outward with stronger proof points.

For readers following AI business news, this funding round sits at an interesting intersection. It’s not the loudest category in AI—planning platforms aren’t as flashy as consumer AI or headline-grabbing agent demos—but it can be more durable because it attaches to mission-critical workflows. CFOs don’t switch planning systems casually. When a platform becomes embedded, it can become the backbone of how leadership steers the company.

From the ai world organisation perspective, this is precisely why finance transformation deserves more spotlight within major AI conversations. At the ai world summit, we see leaders asking practical questions: How do we introduce AI into planning without damaging trust in numbers? How do we ensure governance and auditability? How do we connect operational drivers to financial outcomes? And how do we upskill teams so AI becomes a collaborator rather than a black box?

If you’re building, investing, or operating in this space, consider the bigger signal: the “Excel-to-platform” migration is accelerating, and AI is becoming a core feature of planning systems—not a bolt-on. That’s why we encourage stakeholders to follow ai conferences by ai world and participate in ai world organisation events, where these shifts are discussed with the people actually implementing them.