Private Equity

Attribution you can underwrite

Sponsors are being asked to prove operational value, not narrate it. We build the automation inside portfolio companies and the measurement that makes the improvement defensible at exit.

100 days

to a measured baseline, not an estimate

1 decision

per portco before any platform spend

Exit-ready

attribution evidence, captured continuously

The decisions that matter

We start with the decision, not the technology

Is the AI claim in this deal real?

Diligence needs to separate a model from a moat. We test whether the automation is durable, documented, and dependent on data the company actually owns.

Where is the operating leverage in this portco?

The same three or four back-office decisions carry the margin in most middle-market companies. Finding them is a repeatable exercise.

What can we attribute at exit?

Improvement without a measured baseline is a story. Baseline capture has to start in the first 100 days.

Why now

  • Extended hold periods and constrained exit paths
  • LP pressure for operational, not multiple, attribution
  • Continuation vehicles requiring defensible valuation narratives
  • Roll-up integration cost that erodes the thesis

Where we start

AI diligence module

A structured assessment of the target's data, decisions, and automation claims inside your existing diligence timeline.

First-100-days automation

One decision per portco, instrumented with a measured baseline from day one.

Cross-portfolio playbook

What worked in one company packaged so the next deployment costs a fraction of the first.

Common questions

What operators ask before the first call

How long before AI value-creation work pays for itself?
If you're measuring at the portfolio company level, the first automated decision usually shows a measurable baseline shift within the first 100 days — that's the point of starting the measurement early rather than retrofitting it before exit. If you're measuring at the fund level, the honest answer is it depends on hold period and how many portfolio companies you deploy across, which is why we start with one decision in one company before scaling the playbook.
What data do we need before we can start?
For diligence, you need whatever data room access is already standard, plus direct conversations with the target's operations team about how their claimed automation actually works day to day. For portfolio value creation, you need the same operational access an ops partner already has — no special data infrastructure required, since the point is to instrument the baseline as the automation goes live, not analyze historical data that doesn't exist yet.
How do we know an AI claim in a target company is real versus a pitch deck story?
Ask whether the automation is durable without the specific people who built it, whether it's documented well enough for a new hire to maintain, and whether it depends on data the company actually owns versus a vendor relationship that could disappear post-close. A model with no documentation, no fallback process, and no defined owner is a fragility, not an asset, regardless of what it's saved so far.
How do we attribute AI-driven improvement at exit without it looking like a story?
Capture the baseline before the automation goes live, define the metric with the portfolio company's finance function so it survives scrutiny, and keep measuring continuously rather than doing a before/after snapshot near exit. Buyers and their diligence teams are increasingly skeptical of AI attribution claims that appear only in the final data room, so the credibility comes from the paper trail existing from day one, not from the final number.
Should this go through the deal team or the operating partners?
Operating partners, with the deal team involved only at diligence and exit narrative stages — this is portfolio company execution work, not a fund-level technology bet. The firms getting real attribution value treat it as an operations discipline applied consistently across portfolio companies, not a one-off initiative championed by a single deal partner for a single asset.
Tailored engagement

Portfolio AI value-creation scan

We assess two or three portfolio companies for agent-ready decisions and return a ranked, costed plan with the attribution method defined up front.