Una Software Raises $13M for AI FP&A Platform

Una Software reaches $13M total funding with fresh Seed round

Una Software, a Toronto-based company building an AI-powered financial planning and analysis (FP&A) platform for midmarket and enterprise teams, has closed a Seed round that brings its total funding to $13 million. In today’s finance landscape—where boards expect faster forecasts, operators want tighter spend control, and leaders need a clearer line of sight from plan to performance—this milestone is another signal that “AI-native” finance tooling is moving from experimentation into real deployment, a theme we track closely at the ai world organisation through ai conferences by ai world and ai world organisation events.

From the perspective of the ai world organisation, funding announcements like this are not just about the number; they’re about what the capital unlocks next and what it implies for how modern finance teams will work. The ai world summit routinely features this exact shift: the finance function evolving from a reporting hub into a decision engine that helps the business respond in near real time, especially as AI agents and automation start showing up inside core systems. This is also why “ai world summit 2025 / 2026” programming themes have increasingly centered on practical agentic workflows, governance, and measurable ROI—because transformation stories only matter when they ship into day-to-day operations.

What the Seed round means—and who led it

The Seed round was led by Staircase Ventures, described as a Toronto-based venture capital firm focused on seed-stage investments in Canadian B2B technology companies and founded in 2023. In plain terms, a lead investor typically helps set the round’s direction and signals confidence to the rest of the market, and in this case the message is clear: there is strong conviction that AI-forward FP&A can be a foundational layer for modern performance management rather than “just another analytics add-on.”

This matters because FP&A sits at the crossroads of every department’s plans: revenue forecasts, hiring, marketing spend, inventory decisions, and operating budgets all converge here. When that function becomes faster and more adaptive, leadership teams can react to volatility with less friction—something many organizations learned the hard way over the past few years. For the ai world organisation, these are the kinds of operator stories that translate directly into high-value on-stage conversations at the ai world summit, where leaders compare what worked, what failed, and what they’d implement differently if they had to roll it out again.

What Una Software is building for modern finance teams

Una Software was founded in 2023 and positions itself as an AI-powered FP&A platform built for modern midmarket and enterprise organizations. The company describes its product as AI-native and adaptive, aiming to help finance teams deliver intelligent forecasting, adaptive planning, and connected corporate performance management (CPM). It is headquartered in Toronto, Canada.

Those product claims map to a common set of pain points that finance leaders voice repeatedly: forecasting cycles that take too long, planning models that break when the business changes, and data that lives in too many places to create a single source of truth. While spreadsheets and legacy CPM tools can still do the job in stable environments, they often struggle when teams need to re-forecast quickly, simulate multiple scenarios, or reconcile planning assumptions across departments. AI’s promise in FP&A is not “magic predictions”; it’s reducing manual work, improving consistency, and making iteration cheaper—so teams can run more scenarios, more often, with clearer audit trails.

At the ai world organisation, we also see a second-order effect: when planning becomes more continuous, leadership expectations change. Stakeholders begin asking for tighter turnaround times, more frequent updates, and deeper explanations for variance. Platforms that can support that pace—without burning out the finance team—tend to become strategically important.

Where the funding goes: agents, templates, and go-to-market scale

Una says the new capital will be used to deepen its AI agent framework, expand its Global Template Library, and scale sales, marketing, and partnership efforts to meet growing demand from midmarket and enterprise customers. That allocation is worth unpacking because it hints at how the company thinks about product-led differentiation and distribution.

First, “AI agents” in finance software usually imply more than basic automation. In many modern stacks, agent-like capabilities can help users navigate complex workflows, pull the right data, draft narratives, flag anomalies, or suggest next actions based on context. When done responsibly, this reduces the cognitive load on finance teams and shortens the distance between a question (“Why did margin move?”) and a defensible answer (“Here are the drivers, assumptions, and source systems.”). The difference between a novelty assistant and a durable finance agent often comes down to trust: governance, permissions, traceability, and consistent outputs over time.

Second, expanding a Global Template Library implies the company is investing in repeatable planning models and frameworks that customers can adapt faster. Templates may sound simple, but in FP&A they can be a serious moat. Most finance teams do not want to reinvent driver trees, budget structures, or rolling forecast models from scratch. They want “a strong default” that reflects best practices—then they want the flexibility to tailor it to their business realities. If Una can build templates that speed time-to-value while still supporting the messy variability of real organizations, it becomes easier to land and expand accounts.

Third, scaling sales, marketing, and partnerships suggests Una is preparing for the operational side of growth. For B2B platforms in finance, partnerships can be particularly powerful: consultants, systems integrators, and adjacent SaaS providers can help companies implement planning solutions faster and integrate them into broader data and reporting ecosystems. This also aligns with what we emphasize at the ai world summit: the best AI outcomes often happen when tooling, process redesign, and change management move together—not when software ships alone.

Why AI-native FP&A is becoming a board-level topic

Una’s stated mission is to shift finance from a back-office requirement into a strategic growth engine. That’s an ambitious statement, but it reflects a real trend. Organizations increasingly expect finance to do more than report historical numbers; they want finance to steer the business with forward-looking insights, scenario planning, and rapid sensitivity analysis.

Several forces are pushing this change. Data volume and business complexity have grown, especially for companies operating across multiple geographies, products, and channels. Competitive cycles are faster, and planning assumptions can become stale quickly. Meanwhile, finance talent is expensive and often stretched thin, which makes automation and workflow acceleration more than a “nice to have.”

In that context, AI-powered FP&A can create leverage in a few practical ways—without overpromising. It can help teams consolidate inputs across stakeholders more efficiently, reduce repetitive modeling work, and turn raw variance into a clearer narrative for leadership. It can also help enforce standard definitions and reduce the “multiple versions of the truth” problem that emerges when teams operate in disconnected sheets and siloed tools.

From the ai world organisation viewpoint, this is exactly the kind of real-world AI adoption story that resonates at ai conferences by ai world: it’s applied, measurable, and tied to business outcomes. When finance leaders share how they shortened planning cycles, improved forecast accuracy, or increased scenario coverage, the audience learns what “AI impact” looks like beyond demos.