How a PE Roll-Up Surfaced 40% of Redundant Work

A 500-person PE-backed roll-up's ops team layered screen-capture telemetry across a 50-person pilot — surfacing 40% redundant work, reclaiming 200–250 hours a month, and cutting integration time 80%.

40%

Found redundant across siloed teams

2–4 months

Implementation Time

Felix Rösner

Co-founder

Marble

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The Challenge

A private equity-backed roll-up had completed its acquisitions but had no operational visibility into what its 500 employees were actually doing. Legacy systems across portfolio companies didn't talk to each other. Quarterly reporting was delayed. And buried beneath the surface, 40 percent of the work being performed was redundant — duplicated across teams that didn't know each other existed. Integration planning was built on org charts and assumptions, not observable reality.

What They Built

Marble deployed lightweight screen and keystroke capture across a 50-person pilot team, with PII redacted before data left the environment on the client's own AWS infrastructure. The captured activity was processed into process models — maps of how work actually happened, not how it was supposed to happen. Living standard operating procedures were generated automatically and shared across teams, allowing best practices to spread before a single automation was built. Results came faster than expected: 80 percent faster post-acquisition integration, 200 to 250 hours per month returned to the business, and the 40 percent redundancy surfaced and handed to leadership for action.

The engagement began with a 50-person pilot before expanding across the broader organization — an intentional starting scope that allowed the team to validate the data model and demonstrate results before asking the client to trust the system at scale. Lightweight screen and keystroke capture infrastructure was deployed across pilot team endpoints. Before any captured data left the device, PII was redacted on-premises — a privacy architecture built specifically for the sensitivity requirements of the engagement. The client's own AWS environment hosted the data pipelines, ensuring no sensitive process data crossed organizational boundaries. Captured activity was processed by Marble's proprietary process modeling system into structured maps of how work actually occurred — distinct from how org charts or documentation claimed it was supposed to occur. From these models, living SOPs were generated automatically and shared across teams, allowing best practices to propagate without requiring manual knowledge transfer. The 40% redundancy finding was surfaced through this process modeling work and handed directly to leadership for action. Results arrived faster than expected, with 80% faster post-acquisition integration and 200–250 hours per month returned to the business within the engagement timeline.

AI Role

The AI system processes screen capture and keystroke data — with PII redacted before leaving the endpoint — to generate structured process models of how work actually occurs across the organization. From these models, the system automatically produces living standard operating procedures that are shared across teams, surfacing best practices and revealing redundancies without requiring interviews or manual process mapping.

AI Model

Custom / proprietary

Infrastructure

Client's own AWS environment (on-premises data pipeline and storage) • Existing employee endpoints (for lightweight screen and keystroke capture deployment) • Legacy operational systems across acquired portfolio companies

Integration Points

Marble platform connecting to employee endpoints via lightweight screen and keystroke capture agents • On-premises PII redaction layer processing data before it leaves each endpoint • AWS data pipelines ingesting sanitized activity data into Marble's process modeling system • SOP output shared across teams in acquired portfolio companies

Impact

40% Redundant Effort Uncovered

Process mapping revealed that 40% of work being performed across the roll-up was redundant — duplicated by teams operating in silos with no visibility into each other's work.

80% Faster Integration

Post-acquisition integration timelines shortened by 80%, driven by having actual process maps rather than relying on org charts and interviews.

200–250 Hours/Month Returned

200 to 250 hours of employee time were recovered monthly through redundancy elimination and process standardization — before any formal automation was built.

Technology Utilized

Implementation Complexity

Cost Reduction

Time Savings

Headcount Avoidance

The solution requires deploying lightweight screen and keystroke capture infrastructure across employee endpoints on the client's own AWS environment, building ML-based process modeling pipelines, and generating living SOPs from unstructured activity data. The data sensitivity, privacy requirements (PII redaction), and custom process modeling infrastructure represent significant engineering and compliance effort.

Best Fit For

PE firms managing post-acquisition integration for roll-ups, particularly those inheriting legacy systems and needing operational visibility before automating anything.

Felix Rösner

Co-founder

Marble

Co-founder of Marble, enabling PE firms and enterprises to capture real workflows, surface inefficiencies, and accelerate transformation through instrumentation-grade operational visibility.

Connect with the Expert

Industry:

Financial Services

Business Function:

Executive & Strategy

Operations

Company Size:

251-1,000

Project Cost:

$25K – $100K

Ownership:

Private Equity-Backed

Organization Type:

Private Company

AI Pattern:

Process Automation (RPA + AI)

Data Synthesis & Reporting

Value Type:

Cost Reduction

Time Savings

Headcount Avoidance

AI Model:

Frequently Asked Questions

How did a mid-market financial services roll-up surface 40% redundant work with process-mapping AI?

The experts deployed lightweight screen and keystroke capture across a 50-person pilot — redacting PII on-device and hosting pipelines in the client's own AWS — then processed the activity into models of how work actually happened and auto-generated living SOPs. This surfaced that 40% of the work was redundant and reclaimed 200–250 hours a month.

What AI tools were used in this process-mapping project?

The work used the Marble Platform's proprietary, custom process-modeling system on the client's own AWS infrastructure, combining process automation with data synthesis and reporting, with PII redacted on-premises before any data left the device.

What results did the roll-up achieve?

Three outcomes: post-acquisition integration timelines shortened by 80% using actual process maps instead of org charts; 200–250 hours per month returned through redundancy elimination and standardization before any automation was built; and the discovery that 40% of work across the roll-up was redundant, handed to leadership for action.

How long did the process-mapping project take?

About four to six months, starting with the 50-person pilot to validate the data model before expanding across the broader organization, with results arriving faster than expected.

Who is this process-mapping AI approach best for?

PE firms managing post-acquisition integration for roll-ups, particularly those inheriting legacy systems that need operational visibility before automating anything.