Zalos Raises $3.6M to Bring “Computer Agents” Into Finance Operations

Zalos, a startup focused on automating finance workflows through AI-driven “Computer Agents,” has raised $3.6 million in seed funding. The round was led by 14 Peaks, with participation from Cohen Circle, 20VC, and a group of notable angel investors.

The raise comes at a time when finance teams are under pressure to modernize operations, yet remain constrained by legacy systems that are deeply embedded and difficult to replace.

A Different Approach to Finance Automation

Modern finance teams operate across a fragmented stack of ERPs, CRMs, spreadsheets, and banking platforms that were never designed to work together. Even in well-resourced organizations, critical workflows still depend on people manually moving data between systems, reconciling discrepancies, and ensuring accuracy across disconnected tools.

Zalos is taking a different approach. Rather than replacing these systems or attempting to connect them through fragile integrations, the company is building software that operates themectly. Its Computer Agents log into platforms, navigate interfaces, input data, and complete workflows the same way a human would. The idea is simple, but the implication is significant: automation that works within the reality of existing infrastructure instead of trying to rebuild it.

Turning Workflows Into Autonomous Execution

At the core of Zalos’ platform is a method of converting recorded workflows into repeatable automation. Finance teams can capture a process once, such as closing the books or reconciling accounts, and the system transforms that recording into an agent capable of executing the same steps continuously.

What makes this approach compelling is not just the automation itself, but the context awareness behind it. These agents are designed to understand the sequence of actions, the checks and balances required, and the business logic that underpins financial processes. Every action is logged, creating a detailed and auditable trail that aligns with the strict requirements of finance teams.

Instead of relying on brittle scripts or limited rule-based automation, the system mirrors how experienced operators actually work inside their tools, bringing a level of flexibility that traditional automation has struggled to achieve.

Why Finance Demands a Higher Standard

Automation in finance has always faced a unique challenge. Unlike other domains, where a margin of error might be acceptable, finance operations demand precision, accountability, and transparency. A missed entry or incorrect reconciliation is not just an inconvenience; it can have regulatory and financial consequences.

This is where Zalos is positioning itself differently from general-purpose AI agents. While broader AI systems are becoming capable of interacting with software, finance requires more than capability. It requires reliability, traceability, and alignment with audit standards. Zalos is building its infrastructure specifically around these constraints, ensuring that every automated action can be reviewed, verified, and trusted.

Founders Focused on the Reality of Enterprise Systems

The company was founded by William Fairbairn and Hung Hoang, who both arrived at the same conclusion from different perspectives. Years of experience with finance teams revealed a consistent pattern: organizations invest heavily in ERP systems, yet still rely on manual work to keep them functioning effectively.

Replacing these systems is rarely an attractive option. The cost, time, and risk involved often outweigh the potential benefits, especially when entire processes have been built around them over many years. Zalos is built around the idea that transformation does not require replacement. Instead, it can come from enabling those systems to be used more effectively through automation.

The Broader Implications of Computer Agents in Finance

If Zalos and similar technologies succeed, the impact on finance operations could be substantial. One of the most immediate shifts would be in how work is distributed within teams. Instead of spending time on repetitive, process-driven tasks, finance professionals could focus more on analysis, strategy, and decision-making. The of the finance team would evolve from executing workflows to overseeing and optimizing them.

Over time, this could also change how organizations think about scaling. Traditionally, growth in transaction volume requires a corresponding increase in headcount. With reliable Computer Agents, that relationship begins to break down. Companies may be able to handle significantly more operational complexity without expanding their teams at the same rate.

There are also implications for how software itself is designed. If agents can operate interfacesectly, the importance of APIs and deep integrations may diminish in certain contexts. Instead of building tightly coupled systems, companies might prioritize flexibility, knowing that intelligent agents can bridge gaps between tools.

At a broader level, this technology introduces a new layer of abstraction in enterprise software. Just as cloud computing abstracted infrastructure and SaaS abstracted application management, Computer Agents have the potential to abstract execution itself. Workflows become something that can be recorded, replicated, and continuously improved, rather than manually performed.

The challenge, however, will be trust. Finance teams will need to be confident that these systems can operate with near-perfect accuracy and that every action can be audited and explained. If that trust is established, the shift could redefine not just how finance teams operate, but how organizations approach automation across the enterprise.