Eliza

The fund needed to deploy ChatGPT Enterprise across nine portfolio companies simultaneously as a coordinated program, not a pilot, affecting 10,000+ users.

The insurer's 30 person engineering org was slowed by legacy tooling, inconsistent developer environments and workflows across teams, and AI that was adopted ad hoc.

The PE sponsor required an 8 to 10 percent cut in SG&A within 180 days while holding revenue.

Developers had AI coding tools and external support, but adoption was inconsistent with no governance model for AI-generated artifacts.

The firm navigated FASB's Accounting Standards Codification for complex PE-backed transactions, revenue recognition, and lease modifications, taking 30+ minutes per.

Sourcing was manual and time-intensive, with analysts assembling company profiles individually and consuming thousands of hours annually.

Rootstrap's engineers already used Cursor and Claude Code for autocomplete and pair programming, but few worked with sub agents or reusable skills, and little AI was.

Leadership, including the COO, head of AI, head of due diligence, CFO, and deal partners, was not aligned on AI priorities and was weighing competing vertical tools.

The company processed thousands of receipts each week from field operations and vendors, with a 10 person offshore team keying required fields into the ERP.

The company ran thousands of manual, repetitive workflows every day across six geographic zones, with inconsistent handling from one region to the next.

BD teams manually triaged thousands of brief teasers in slow, error-prone work; analysts spent hours extracting tables from CIMs and rebuilding models; sourcing teams.