A mid-market PE fund with 15 vertical SaaS portfolio companies — healthcare, compliance, field services, workforce management — had significant operational inefficiency baked into the back offices of every portco. Manual AP processing, compliance reporting, invoice reconciliation, and internal ops workflows were consuming FTE capacity that should have been going toward growth. Each company was dealing with the same problems in isolation, with no shared playbook.

We ran a portfolio-wide workflow diagnostic to map the highest-friction back-office processes across all 15 companies and rank them by automation potential and ROI. Then we deployed in waves — building and shipping AI-powered automation for AP intake and routing, compliance report generation, contract data extraction, and internal ops, including agents that handle exception routing end to end. Each build was designed as a reusable component so subsequent portcos got to production faster than the first. Governance framework and an AI operating model stood up across the full portfolio in Month 2.

A 40-location specialty care group was processing 8,000+ invoices per month manually across a fragmented AP function. Compliance reporting for payer contracts required 3 FTEs working full-time on data reconciliation. Leadership had identified the problem but had no internal AI capacity to address it.

We mapped the full AP workflow, identified the four highest-friction steps, and built an AI-assisted invoice intake, classification, and exceptions-routing system integrated directly into their existing ERP — with an agent handling exception triage autonomously. Separately, built an automated payer compliance report generator pulling from their existing data warehouse. Trained staff in two days. Total build time: 7 weeks.

A B2B SaaS company with 60 engineers was watching competitors ship faster. Leadership had budget for AI tools but no structured rollout — previous attempts at Copilot adoption had stalled at 20% active use after 90 days. Feature cycle time was too slow and the team was skeptical.

We ran a two-week diagnostic to understand where the team spent time and where the real friction was. Built a per-team onboarding curriculum anchored to actual workflows, not generic prompts. Deployed Copilot and Cursor with governance guardrails, stood up an internal AI Champion program, and built a code review agent that cut PR review time. Active adoption hit 80% within 8 weeks.