PE firms · Portfolio companies

AI for Private Equity
that moves EBITDA, not slides.

Your LPs are asking about AI. Your portcos are winging it. We run AI for private equity like an operator: fix one company in the room, then roll the same playbook across the portfolio — measured in the KPIs your boards already track.

Diligence to exitOne toolkit, rolled forwardSmaller funds contract it fractionally

Every portco is running its own AI experiment. None of them compound.

The thesis said AI value creation. The reality across the portfolio is one company with a champion, three with idle licenses, and the rest waiting for someone to tell them where to start. Each CEO is fielding the same board question alone, and each one is about to buy a different answer.

That is expensive twice. You pay for twenty uncoordinated experiments, and you lose the thing a portfolio is for: leverage. What works at one company should get cheaper and faster at the next. It only does if somebody builds it once, writes it down, and carries it forward.

  • That is the engagement: one operator, one toolkit, rolled across the portfolio.
  • Built at the anchor company, reported on the metrics they already run, in language an IC meeting recognizes. Smaller funds ($50–200M) usually contract this fractionally — they do not have AUM to staff a full-time Head of AI.

Two ways in

For the firm. For the portfolio company.

PE buyers do not shop by service name — they shop by where the deal is. Start at either level; the toolkit is shared. Smaller funds usually start at one portco.

For the Firm

Portfolio AI Diagnostic

A comparable readiness read across your companies — who has leverage waiting, who has risk, and where the first dollars come from. Written for operating partners, not engineers.

Executive Sessions Across Portcos

Working sessions with each leadership team on their own P&L and their own workflows. Executives leave with systems running, not a vendor evaluation to schedule.

One Toolkit, Rolled Forward

Everything built at the anchor company is documented and reused — fluency rubric, role tracks, the metrics that moved. The firm owns it; every subsequent rollout starts warmer and lands faster.

For the Portfolio Company

Readiness Assessment

Ten minutes, seven dimensions, and a straight answer on where to start. The free version qualifies the conversation; the paid audit goes function by function.

Executive Session + Team Rollout

Leadership first, then each department on its real work — sales, finance, ops, support. Adoption you can count, not licenses that sit idle.

90-Day Acceleration + Advisory

Baseline the KPIs they already track, ship weekly, report at day 30 and day 90 on those same numbers. Then an operator stays in the corner as the ongoing rung.

The hold period, mapped

Diligence to exit, with AI earning its keep at every stage

Stage 1

Diligence

A plain-English read on the target before you wire the money: where AI moves the P&L in year one, what the data reality is, and what the first 100 days should look like.

Stage 2

First 100 Days

The window when change is expected. Executive session, company-wide training on real work, and the first shipped systems — while the mandate is fresh.

Stage 3

Hold Period

Where EBITDA is made. Opex programs with a number attached, agents doing real work in production, and a senior operator on call for the executive team.

Stage 4

Exit

The AI story a buyer will believe: documented systems, the metrics the company already ran, moving, and a toolkit that transfers with the company instead of leaving with a person.

Portfolio economics

Built once. Reused across the portfolio.

The toolkit compounds

The first rollout does the heavy lifting: the fluency rubric, the role tracks, the policy, the metrics baseline. Company two starts from a working toolkit, not a blank page — so per-company cost drops with every rollout.

Less than a third of one hire

An internal Head of AI runs well north of $300K a year per company, before they have shipped anything. One senior operator across the platform costs less than a third of one of those hires — and starts shipping in week one.

Defensible to the board and LPs

Every engagement ends in writing: what was built, and which of their existing KPIs moved. Numbers that survive an IC meeting — not “AI transformation underway” on a slide.

The evidence lives on this site, not in a deck: a PE-backed vertical SaaS company booked a $25k two-day executive session. A RevOps consulting firm hired us to build a custom AI staffing engine. A founder we coached 5–6x’d her output — case study published, with her name on it.

Fit check

We'd rather tell you no on the first call

Right fit if

  • You are an operating partner whose portfolio companies are each running their own AI experiment — and none of them compound.
  • A portco COO, CFO, or CEO has an efficiency mandate with a number attached and no plan that survives contact with the P&L.
  • You run a smaller fund ($50–200M) without a full-time Head of AI — you contract this the same way you contract recruiting.
  • Your companies are field services, vertical SaaS, manufacturing, logistics — real-work businesses, not tech companies.
  • The board asked what the AI plan is, and “we bought licenses” is not going to hold.

Wrong fit if

  • You want a 40-page strategy document and no working systems. Large firms do that well; this is the other lane.
  • You are looking for offshore build capacity by the hour. Engagements here are fixed-scope and operator-led.
  • Nobody at the firm or the portco owns the outcome. The toolkit compounds only when someone is accountable for it.

In their words

Operators who came in skeptical and left fluent.

Founders, CEOs, and executives — the same rooms your portfolio companies are sitting in.

I thought I was a power user. Then I started working with Mark and realized I had no idea what was actually possible.

Read the case study

Working with Mark turned my AI curiosity into actual leverage. I show up to enterprise calls with answers, not questions.

If you’re a CEO and you’re not building with AI yet, Mark is the guy. He doesn’t teach theory - he hands you the keys.

Last month I was just asking ChatGPT to write emails. Now I’ve built a client onboarding tool, and my team probably thinks I hired a developer.

VP of Operations

Series B SaaS

Research that used to take me half a day now takes 20 minutes. Competitor analysis, market research, customer interviews - all transformed.

Strategy Consultant

Management Consulting

I finally understand what my engineering team is talking about. More importantly, I can spec out AI features without needing them in every conversation.

Product Leader

Fintech

The prompt engineering framework alone saved me. I was getting mediocre AI outputs for months. Now I get usable first drafts 90% of the time.

Marketing Director

E-commerce Brand

One toolkit. Every portco.

Thirty minutes. Bring one portfolio company you want moving — you'll leave with a written read on where its first AI dollars are. If we're not the right fit, we'll say so.

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FAQ

AI for Private Equity FAQs

How portfolio engagements run, who buys, and what ships when.