FOR PRIVATE EQUITY FUNDS

AI that moves at the speed of a portfolio company.

PE funds face a specific challenge: 10 or 20 companies, each at a different stage of AI readiness, all with pressure to show P&L impact. We've built a playbook for exactly this. From portfolio-wide assessment to company-by-company deployment — in weeks, not quarters.

/01 · The PE problem

Why AI is harder
inside a PE portfolio.

/01

Decentralized teams, no shared standard.

Each portco is largely independent — different tech stacks, different engineering talent, different leadership. There's no central team to drive adoption. What works at one company may not translate to the next.

/02

The urgency is real.

Hold periods don't wait. The window to show operational improvement is 18-24 months. That leaves no time for multi-year AI transformation programs or pilots that never graduate to production.

/03

Shadow AI is already happening.

Engineers across the portfolio are already using Copilot, ChatGPT, and Cursor — on personal accounts, without data policies, without enterprise contracts. The risk is real. The upside is being left on the table.

/02 · The Deployly approach

How we work
across a portfolio.

Step 01

Portfolio-wide diagnostic

Weeks 1–4

We score each portfolio company across five dimensions: current AI adoption, data infrastructure readiness, highest-friction workflows, leadership appetite, and path to value. Output: a ranked list of where to start, why, and what each company needs to get there.

Step 02

Wave 1 deployment

Months 1–4

We start with the 2–3 highest-readiness companies. Full build — use case selection, development, change management, and production deployment. We move fast because we've done this before.

Step 03

Portfolio-wide rollout

Months 4–12

What we build for Wave 1 becomes the playbook for the rest. Shared governance, standardized tooling, trained champions at each company. The program compounds — each new company benefits from everything the previous ones learned.

/03 · What we build

What we typically build
for PE portfolios.

/01

Developer productivity programs (AI coding tools, training, adoption infrastructure)

/02

Back-office automation (AP, invoice processing, reporting, compliance workflows)

/03

AI-assisted code review and testing agents

/04

Internal document and knowledge base AI

/05

Customer-facing feature pilots (within existing products)

/06

Portfolio-wide governance and AI operating model

/04 · In practice

What this looks like
in practice.

For a mid-market PE fund with 15 vertical SaaS portfolio companies — healthcare, compliance, field services, workforce management — we built a full 12-month AI implementation program. Starting with a portfolio-wide diagnostic, we scored every company on AI readiness, data infrastructure, and workflow friction, identified Wave 1 companies, and stood up a developer productivity program across 120 engineers in the first 90 days. A governance framework and AI operating model were live portfolio-wide by Month 2. The program is currently in Month 6 execution.

See full program overview →
/05 · Engagement

Two ways
to work with us.

/01

Full Deployment

Hand us the mission. We run a rapid assessment of your full opportunity landscape, build the roadmap, prioritize ruthlessly, and execute. You get strategy and delivery in one team — no handoffs, no gaps.

Best for: companies without an AI roadmap yet.
/02

Point Builds

Already know what you want to build? We come in fast, validate the use case, and ship a production-ready solution in weeks. No retainer, no strategy tax.

Best for: companies with a specific use case ready to build.
GET STARTED

Tell us
about your
portfolio.

Thanks for your interest in deployly.ai. Tell us about your portfolio and what you're trying to build, and we'll get back to you within one business day.

Message received.

We'll be in touch within one business day.