Marble Cognition Raises €3.35M for Watch-First AI | Zeniteq
Loading
Loading article
Zeniteq is now available on mobile for iPhone, iPad and Apple Watch.Download nowLearn more
Marble Cognition Raises €3.35M for Watch-First AI
Marble’s AI coworkers learn workflows from business-app activity, putting process discovery, privacy controls and human approval at the center of its automation pitch.
Marble Cognition has raised €3.35 million in pre-seed funding to build AI coworkers that observe employee activity before automating their tasks. Heartcore Capital and Seedcamp co-led the round, announced November 2, 2026.
Founded in late 2025 by Felix Rösner and Christophe Schmit, the Munich startup's desktop application captures screen activity, clicks, and keystrokes across business software to generate structured process maps. A claimed pilot at an unnamed US financial services firm reportedly cut handling time by 90% and increased case volume by 50%, though these figures are company-supplied and unaudited.
Munich-based Marble Cognition has raised €3.35 million ($3.75 million) in pre-seed funding to build AI coworkers that learn how employees work before automating their tasks. Heartcore Capital and Seedcamp co-led the round, announced on November 2, 2026. The approach targets a practical obstacle for enterprise AI: companies cannot easily automate processes they have never properly documented. EU-Startups reports the funding and company announcement.
Founded in late 2025 by Felix Rösner and Christophe Schmit, Marble plans to spend the money on scaling model inference, expanding its Munich engineering team and supporting a US launch. Its product starts with a desktop application that observes activity across business software, then turns that activity into structured process maps.
For companies evaluating agentic workflow tools, the interesting question is what that observation can teach an AI system, and where employees still need to supply context or approval.
Why Marble Starts With Observation
Marble’s premise is that employees leave important details out when explaining their work. A written procedure might describe the normal sequence while missing the exceptions, workarounds and judgment calls that determine what actually happens.
In Seedcamp’s November 2 announcement, Rösner estimates that around 90% of core processes at companies he has worked with exist in inboxes and people’s heads. That is a founder’s assessment of his engagements, not a representative survey of businesses.
The announcement coverage also cites a McKinsey estimate that current technology could theoretically automate about 57% of US working hours. These figures describe different things: one concerns documentation within Marble’s experience; the other concerns theoretical technical potential across working hours. They cannot be combined into a market forecast or a prediction of Marble’s results.
The useful connection is narrower. Even when technology can perform individual tasks, a company still needs to specify their sequence, inputs and exceptions. Marble is trying to make that discovery work part of the product.
As Rösner puts it in Seedcamp’s announcement:
“Our Coworkers watch how the work happens before they take it over.”
My view is that process discovery is the more consequential part of this pitch. Generating an action is only useful once the system understands when that action belongs in a workflow.
Marble says its desktop application captures three types of activity inside business applications:
Screen activity, providing visible context.
Clicks, recording interactions with application controls.
Keystrokes, capturing keyboard input.
The company says it converts observed activity into structured maps that AI systems can interpret. It then builds Marble Coworkers to execute those processes using the tools teams already use, with humans involved where judgment or approval is needed. Tech.eu describes this observation-to-execution sequence.
The distinction from a workflow that starts with written instructions is where the specification comes from. Marble proposes deriving it from actual activity. That could reduce the initial burden on employees who would otherwise have to explain every step.
However, capturing a click does not automatically explain its purpose. An employee might open a spreadsheet because it is an official source of truth, because another system is temporarily broken, or simply out of habit. Those possibilities imply different automation choices.
That makes the process map an important evaluation artifact. Buyers should check whether it captures meaningful branches and exceptions, and whether employees can correct its interpretation. The published descriptions do not detail the map format, editing controls or underlying model architecture.
I’d want to inspect a map before approving its resulting automation. A convincing replay of a common sequence is weaker evidence than a clear account of what happens when required information is missing.
Privacy Controls Cover Different Stages
Marble’s observation model requires close attention to what its desktop application can see. Screens and keyboard input can contain sensitive information even inside legitimate business applications.
According to Seedcamp’s announcement, Marble says captured information is anonymized before any person or AI model accesses it, including in a customer’s own cloud. It also describes exclusions, employee controls and aggregate analysis. These privacy and security claims have not been independently verified in the opened coverage.
Company-described control
What buyers should establish
No recording of private apps, private browser tabs or activity outside working hours
How those boundaries are identified, configured and tested
Employees can pause recording at any time
What the pause stops and how employees confirm its status
Anonymization before human or model access
What information is removed, where processing happens and how failures are handled
Analysis in aggregate, not by individual
What administrators can retrieve, export or associate with an employee
These controls address different stages. Excluding an application limits collection. Anonymization changes information after capture. Aggregate analysis limits how information is interpreted or presented. A company needs to examine each stage separately.
The anonymization claim makes the boundary around initial capture especially important. Buyers should ask whether raw captures are stored temporarily, how long records remain available and who can access them. The published material does not answer those questions.
A sensible deployment review would also test mixed-content situations, such as a personal message appearing inside a work application. An app-level exclusion policy needs to be assessed against what appears on screen.
Human Approval Matters More Than the “Coworker” Label
Marble describes humans staying involved where judgment matters. Its clearest published example is a private-equity-owned US financial services company, where a Marble Coworker prepares settlement offers for a specialist to approve.
That is a meaningful division of responsibility: the AI prepares the work, while a person authorizes the consequential decision. The example supports an approval-based workflow, although the public descriptions do not specify a universal approval requirement for every action or automation.
The team handled 50% more cases in the first month, without additional hiring.
The system learned the process through observation.
These are company-supplied results, also promoted by investor Seedcamp, not an independently audited benchmark. The reporting provides no sample size, baseline timing, error rate or detailed accounting of review work.
The two percentages also measure different outcomes. A large reduction in handling time for one part of a workflow need not produce an equally large increase in total throughput. Specialist approvals, incoming demand and other stages can remain constraints.
For procurement, I’d prioritize the approval interface alongside the time-saving claim. Reviewers need enough context to spot an incorrect offer without reconstructing the whole case themselves. Otherwise, preparation gets faster while verification remains expensive.
What Companies Should Test Before Buying
Marble’s watch-first design suggests a specific evaluation plan. The following criteria follow from its published mechanism; they are not results from testing the product.
Check process coverage before execution. Select a bounded workflow containing both routine cases and known exceptions. Ask employees to compare the generated map with how the work actually proceeds. Include a rare but important branch.
Separate observed behavior from approved policy. A repeated workaround can be common without being correct. Establish who signs off on the process map and how obsolete or unauthorized steps are removed before automation.
Verify collection boundaries. Test exclusions, pauses and working-hours settings in the intended deployment environment. Request documentation covering retention, access permissions, anonymization and available security assessments.
Define approval boundaries explicitly. Identify which actions require authorization, who can grant it and what happens when a reviewer rejects a proposed action. The financial-services example demonstrates one arrangement, not every possible deployment.
Measure the complete workflow. Track case volume, errors, rework and reviewer effort alongside handling time. Faster preparation is valuable, but its operational benefit depends on what happens downstream.
Competition in enterprise AI will partly turn on how reliably vendors translate messy business activity into a usable specification. Marble’s approach makes that translation explicit. For buyers, the decisive evidence will be a correct, reviewable process map and controlled execution, not the amount of desktop activity captured.
Frequently Asked Questions
3 questions
1
Does Marble Need Written Workflow Documentation?
Marble says it learns workflows by observing business-app activity. Employees still need to validate whether the resulting process map accurately reflects the work.
2
Are Marble Coworkers Fully Autonomous?
The company describes human involvement where judgment or approval matters. Its financial-services example requires a specialist to approve AI-prepared settlement offers.
3
When Will Marble Launch in the US?
The funding is intended to support a US launch. The cited announcements do not provide a launch date.
