The modern finance department runs on more software than ever before. Yet for many organizations, that expansion has created as many operational problems as it has solved. Teams often juggle separate systems for accounting, payroll, planning, reporting, compliance, and reconciliation, while still relying heavily on spreadsheets and manual oversight to connect the dots between them.
That growing complexity is the opportunityViewz believes it can address. The company has emerged from stealth with $7 million in seed funding led by Ibex Investors and Flint Capital, alongside a broader argument that finance software has reached the limits of incremental improvement. Instead of building another application for finance teams to manage, Viewz is attempting to consolidate the function into a single operational system.
Why Finance Still Feels Fragmented
Over the last decade, enterprises invested heavily in digital finance transformation. But despite those investments, many CFO organizations still struggle with delayed reporting cycles, inconsistent data visibility, and operational inefficiencies tied to disconnected systems.
According to Viewz’s founders, the issue stems from the architecture itself. The company was founded by Moti Cohen, Omer Aviad, and Liran Kessel, whose combined backgrounds span audit, financial leadership, and operational finance. Their view is that finance departments have spent years trying to optimize fragmented workflows instead of redesigning them.
“I started Viewz because I spent 20 years watching finance fail in the same way, not from a lack of data, but from a lack of structure. We are not a better tool. We are a different answer to the same question every finance leader has been asking for years: why does this still feel so hard?” Cohen said.
That philosophy arrives at a time when AI adoption is accelerating across enterprise operations. While automation promises faster decision-making and reduced manual work, the quality of those outcomes still depends on the integrity of the systems underneath. When data is fragmented across multiple tools, even sophisticated AI models can produce inconsistent or unreliable results.
Replacing The Stack Instead Of Extending It
Most finance platforms position themselves as integrations within an existing ecosystem. Viewz is taking a more aggressive approach by trying to replace much of that ecosystem entirely.
The platform combines a native general ledger with AI agents and embedded finance operations capabilities. Instead of separating bookkeeping, payroll, FP&A, reporting, and compliance across different vendors or teams, the company brings those functions into one governed environment.
The goal is to create a continuously updated financial system rather than one that revolves around monthly reconciliation cycles. By structuring and reconciling financial data daily, the company says organizations can move closer to a “continuous close” model that reduces the operational burden traditionally associated with month-end reporting.
“Finance was never meant to feel this heavy. But it does. More tools. More people. Less clarity. That’s the problem we set out to fix – not by improving the model, but by replacing it,” Cohen added.
The broader concept mirrors a wider trend happening across enterprise infrastructure. As companies adopt AI-driven workflows, the systems responsible for maintaining clean, unified data increasingly become strategic assets rather than back-office utilities.
Investors Bet On Operational Consolidation
While many early-stage software companies emphasize growth metrics, Viewz’s investors appear particularly focused on customer retention and replacement behavior.
The company says it reached multi-million-dollar ARR within roughly a year of launching quietly and experienced strong growth during the fourth quarter. More notably, it reports zero voluntary churn, suggesting customers are not simply experimenting with the platform alongside existing systems.
“Moti, Omer, and Liran have spent twenty years inside the problem they’re now solving. You can feel it in how they talk to CFOs. Most finance-oriented startups are layering intelligence on top of broken plumbing. Viewz rebuilt the plumbing. That’s a much harder thing to do, and it’s the only version of automated finance that scales,” said Aaron Rinberg, Partner at Ibex Investors.
For investors, that distinction between augmentation and replacement appears significant. Rather than selling point solutions, Viewz is attempting to become the operational core of the finance department itself.
Sergey Gribov, General Partner at Flint Capital, pointed to customer behavior as a signal of that shift. “What stood out wasn’t the growth; it was the retention. Zero voluntary churn tells you customers aren’t using Viewz alongside their existing tools. They’re using it instead. One thing that really caught my attention was feedback from one of my CFOs: if he were using this platform, he believes he could run his team with roughly 30% fewer people.”
That replacement dynamic also appears in customer accounts. “Viewz is my finance department from A to Z; everything I need in one place. When I moved companies, I brought Viewz in from day one,” said Erez Fisher, VP of Finance at Dig Security.
The Infrastructure Layer Behind AI Finance
As AI becomes more deeply embedded into enterprise finance, infrastructure may become the defining competitive layer. Automation tools can generate forecasts, reconcile transactions, and surface insights, but their effectiveness still depends on structured and trustworthy data.
Viewz is positioning itself around that premise. Instead of focusing solely on workflow automation, the company is building around the idea that finance itself should function as an always-on operational system.
With fresh funding now secured, the company plans to continue expanding what it calls a “fully agentic finance team.” Whether that vision reshapes enterprise finance at scale remains to be seen, but the company’s thesis reflects a growing belief across the software industry: the next wave of operational transformation may come less from adding intelligence on top of existing systems and more from rebuilding the systems themselves.
