The diligence bottleneck
Analysts still spend hours combing PDFs, spreadsheets, and data rooms to assemble memos and flag risks. Quality varies by deal team and individual—creating inconsistency and burnout at peak volume.
The goal of AI is not to replace the analyst. It is to standardize first-pass coverage and surface what needs human attention.
What works in practice
Effective systems ingest deal documents, extract structured fields, draft summaries against your templates, and flag material signals for review. Outputs must cite sources so analysts can verify quickly.
In one mid-market private equity engagement, FocusKPI reduced manual document review by about 50% and shortened deal evaluation cycles by about 35%, while analysts kept ownership of every recommendation.
How to scope a diligence AI pilot
Pick one document family (for example, financials or customer contracts) and one memo template. Define precision/recall expectations with your team—not a vendor demo score. Run on a closed set of past deals before live deals.
Security and access control matter as much as model quality. Data rooms and deal materials require private deployment options and clear retention policies.
Where this sits in FocusKPI’s practice
Document intelligence spans Automate Work and Activate Data: extraction and routing on one side, insight and reporting on the other. We typically prove the approach in a focused pilot, then harden integrations for production deal flow.