AI and custom software for private equity portfolios.

For operating partners and portfolio CEOs: find where AI moves EBITDA in each company, build it on a fixed budget, and turn what works into a playbook the rest of the portfolio can reuse.

01why now

AI has become a line in most value creation plans, and investment committees now ask about it during diligence and at exit. The upside is real for mid-market portfolio companies, which tend to carry exactly the manual processes AI removes well: intake, document handling, reporting, customer service, and sales support. Margin gained there flows straight to EBITDA, which is then multiplied at exit.

The difficulty is execution. Portfolio companies rarely have anyone to own AI, the operating team is stretched across many companies, and the large consultancies are priced for one big program, not ten mid-sized ones. What works is a partner that can assess quickly, build on a fixed budget, and reuse what it learns.

02what we build

• Portfolio AI assessments. A short, standard review of each company: where time and money go, which processes AI can take on, what each is worth, and what it costs. Comparable across the portfolio so the operating team can prioritize.
• Value creation builds. The highest return opportunities built and shipped, usually workflow automation, AI agents in operations and service, or internal tools that replace manual reporting.
• A cross-portfolio playbook. When an automation works in one company, it is documented and adapted for the next, which lowers cost and risk every time it repeats.
• Fractional AI leadership. A fractional chief AI officer for portfolio companies that need an owner for AI but not a full-time executive.
• Diligence support. A technical read on a target's AI claims, data, and systems, and a realistic estimate of what AI could add post-close.

where AI tends to move EBITDA in the mid-market

• Back office: invoice processing, reconciliation, collections follow-up, and month-end reporting.
• Customer operations: inbound requests, order status, scheduling, and first-line support.
• Sales: lead response time, proposal drafting, and CRM hygiene.
• Engineering, in software companies: AI coding agents that raise output from the existing team. See AI coding orchestration.

what operating partners should ask of any AI vendor

• Is the business case written against the company's own baseline, in hours, cost, or revenue, before the build starts?
• Is the price fixed, so the investment case does not drift?
• Will the portfolio company own the code, data, and accounts, so the work survives an exit or a change of vendor?
• Can the work be documented well enough to repeat in the next company?

03who this is for

Operating partners and value creation teams at lower and middle market firms, and the CEOs and CFOs of portfolio companies in roughly the $10M to $500M revenue range. See AI consulting for mid-sized companies for how we think about that segment.

Our public case studies are in healthtech, dental, and consumer apps rather than PE portfolios specifically. The relevant proof is the method: Pinned Golf reduced its engineering need from five engineers to one through AI-augmented development, the kind of cost structure change a value creation plan looks for.

04how it runs

1. Assess. A standard assessment per company, with opportunities ranked by EBITDA impact and cost.
2. Fix the budget. A fixed number per build, agreed before work starts, so the investment case is clear. Change requests are priced before they start.
3. Build in the open. Working software every two weeks, with the portfolio company's operators in the reviews.
4. Measure and repeat. Results measured against the baseline, written up for the board, and reused across the portfolio.

05common questions about AI for private equity

How are private equity firms using AI in portfolio companies?

Mostly to remove manual work in back office, customer operations, and sales support, which raises margin and EBITDA. Some firms also use AI-augmented engineering to raise output in software portfolio companies, and to assess AI claims and potential during diligence.

What does a portfolio AI assessment include?

A short, standard review of each company covering where time and money go, which processes AI could take on, what each opportunity is worth, and what it would cost, in a format that is comparable across the portfolio.

How do you make AI work repeatable across a portfolio?

By documenting each working automation and adapting it for the next company with similar processes. Each repeat costs less and carries less risk than the first build.

Can you support AI diligence on a target?

Yes. We give a technical read on a target's AI claims, data, and systems, and a realistic estimate of what AI could add after close.

How is pricing structured for portfolio work?

Each assessment and build has a fixed number agreed before work starts, so the investment case is clear for each company. Change requests are priced before they are started, never after.

Have you worked with private equity portfolios before?

Our public case studies are in healthtech, dental, and consumer apps rather than PE portfolios specifically. We are glad to walk through how that work translates to your portfolio on a call.