The hardest part of automating office work often happens before an AI agent takes its first action: figuring out what employees actually do. A workflow might cross an inbox, a spreadsheet and several business applications, with its exceptions understood by the team but never captured in a formal specification. Marble Cognition has raised $3.75 million in pre-seed funding to turn that operational knowledge into AI coworkers.
Heartcore and Seedcamp co-led the round. Angel participants include a Datadog board member, a Personio founder and executives from McKinsey, Mistral AI and Lovable, according to the announcement. The Munich company will use the financing to scale inference, expand its engineering team and launch in the United States.
Marble’s approach starts with observing work. It captures how processes unfold, turns those observations into structured maps and deploys Marble Coworkers to carry out the resulting workflows in the tools employees already use. People remain involved where judgment or approval is required.
Turning Unwritten Workflows Into an Automation Blueprint
An agent that can navigate software still needs to know which records to consult, what counts as a completed task and when an exception requires escalation. Those instructions are difficult to supply when a company’s real operating procedures exist as habits, informal handoffs and knowledge scattered across its systems.
Marble is betting that this context becomes more valuable as software development and agent-building become easier. Its proposed advantage is an accurate understanding of the work being automated: the sequence of actions, the information used and the decisions that connect one step to another.
The company says its desktop application records screens, clicks and keystrokes within business applications. It uses those observations to build structured process maps that AI systems can interpret, then creates coworkers that execute the process end to end. This connects workflow discovery with deployment, instead of leaving the customer to translate a process analysis into a separate automation project.
“Every company we talk to wants AI to take over real work. But nobody can tell the AI how that work is actually done,” said Felix Rösner, Marble’s co-founder and CEO, in the announcement. “So we solve that first.”
AI Coworkers That Operate Across Existing Business Tools
Marble’s existing product website makes the execution model more concrete. It describes coworkers with native computer and browser use, allowing teams to delegate work in the environments they already use. Its refund-triage illustration combines a customer request, a refund policy and business records: the coworker classifies the request, checks the policy, drafts a response and sends exceptions back to a person.
That example illustrates why context matters. Producing a plausible reply is only one step. A useful coworker must connect the request with the relevant rules and customer information, then distinguish routine work from a case that should not proceed automatically.
In operational terms, Marble is seeking to close the gap between understanding a workflow and running it. The process map supplies a specification for the work; the coworker carries it into the business applications; and human review preserves a decision point where the task requires it. Successful deployment depends on how well that specification captures exceptions as well as the common path.
Early Customers and a Financial-Services Deployment
Marble says it works with technology unicorns, private-equity-backed companies and AI roll-ups, naming EGYM, Mutares and Enam among its customers. These organisations offer a natural testing ground for a product aimed at repetitive, tool-heavy administrative operations.
In one deployment at an unnamed, private-equity-owned US financial-services company, Marble says a coworker reduced handling time per case by 90% and helped the team handle 50% more cases during its first month without additional hires. The coworker prepares settlement offers for a specialist to approve, having learned the workflow by observing the team.
Those figures are company-reported results from a specific deployment, rather than a general performance benchmark. They describe two different outcomes—time spent on an individual case and the team’s total throughput—and the specialist’s approval remains part of the process.
The example also points to the company’s commercial focus. Work with a repeatable structure and a clear approval boundary can provide an initial place to deploy an agent, while more ambiguous decisions remain with the team. Scaling that approach requires the system to recognise when an apparently familiar case falls outside the learned workflow.
Privacy Controls Are Central to Observing Work
A product that learns from employee activity needs clear boundaries around collection and access. Marble says its desktop application excludes private applications, private tabs and activity outside working hours. Employees can pause recording, and analysis is performed in aggregate rather than used to assess individuals.
The company also says captured material is anonymised before a person or AI model sees it, including in deployments within a customer’s own cloud. These are the company’s stated safeguards; they are material to how the system is meant to operate, rather than incidental features of the product.
For an enterprise deployment, the practical questions include which applications are in scope, how sensitive information is removed and who can access the resulting process information. Learning enough to reproduce a workflow while limiting unnecessary collection is a core engineering and governance challenge for this model.
Pre-Seed Capital for Inference, Engineering and US Expansion
Rösner and co-founder Christophe Schmit, Marble’s CTO, met at Munich’s Center for Digital Technology and Management and founded the company in late 2025. Rösner previously worked at AI sales-agent company 11x. Schmit, a former MIT researcher, previously worked at Enam, according to the release.
The financing will support the infrastructure and engineering needed to turn observed workflows into deployable coworkers, alongside Marble’s planned US launch. Seedcamp’s participation also connects the company with an investor that backed UiPath early in its development, placing Marble in a broader lineage of businesses trying to reduce repetitive office work through software.
Marble’s proposition is that the next constraint on enterprise automation is increasingly the quality of the process knowledge an agent receives. Its AI coworkers will be judged on how reliably they turn that knowledge into completed work—and how well they preserve human judgment when the workflow calls for it.