A clearer path from data room to diligence.

The work before
the work.

An illustrative mid-market private equity firm receives financial statements, operating reports, and management materials across a sprawling data room. Analysts repeatedly extract the same figures and assemble initial diligence packs by hand.

A workflow built
around the team.

Classify source documents, extract operating metrics with page references, identify missing information, and assemble a draft diligence pack. Every material claim leads back to its source.

The embedded engineering team would map the current process with users, agree on an evaluation set, connect approved sources, and test a reviewable prototype before a staged deployment.

Make the assumptions
visible.

Assume 12 items per month and a change from 18 to 7 hours of staff preparation and review per item.

This represents a modeled 61% reduction in time spent on this task, rounded to the nearest whole percent. It is potential staff capacity, not a claim of cash savings or additional revenue.

Implementation effort, ongoing review, maintenance, and model or infrastructure costs would need to be measured separately. Actual outcomes depend on document quality, case complexity, and adoption.

Useful in a demo.
Proven in the workflow.

• Staff time per item, including corrections and review
• Accuracy and completeness on representative cases
• Exception rates, rework, and turnaround time
• User adoption and ongoing operating cost

Compare a documented baseline with a bounded pilot using comparable cases. Agree on quality and approval criteria before expanding the workflow.