AI due diligence for PE deal teams.
A pre-close assessment of the target’s AI opportunity: which workflows to automate, what the builds cost, how long they take, what they save.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
A million dollars of paper, and the AI question still open.
From deal partners and operating leads at mid-market funds: a hundred-plus deals a year through the CRM, two or three closed.
A million dollars of third-party paper
An IT firm assesses the systems, a financial firm evaluates the books, lawyers review the contracts — a million dollars of third-party stuff that the deal team synthesizes to put on its own paper.
Every CIM now carries an AI story
Every target arrives with AI claims in the deck. Nobody around the table builds these systems, so the claims go untested — and the opportunity goes unpriced.
The memo gets typed from scratch
The investment memo runs forty to fifty pages, and entire sections get typed up from scratch — the synthesis rebuilt by hand on every deal.
Diligence dies at the handoff
The first board meeting starts with totally different people than did the diligence. The knowledge transfers by anecdote, and half of what everyone thought was true turns out not to be.
The firm’s own history stays locked up
Years of refinement across business models, sectors, and geographies sit in people’s minds — when a lookalike deal lands, there is no way to ask what the firm has seen.
The spend is measured. The AI opportunity is not.
What one mid-market deal team puts into a single deal — before anyone prices what the target’s workflows are worth.
From anonymized fund calls — a mid-market PE deal partner, 100–150 deals a year through the CRM
From data room to priced opportunity map in two weeks.
No new access requests, no target-side project — the assessment runs on the diligence materials already in hand: CIM, data room, management sessions.
- 01
Map the workflows that matter
From the CIM, the data room, and management sessions: where the target’s people spend the hours, which rules drive the work, what volume runs through each flow.
- 02
Price each build
Per workflow: what the build costs, how long it takes to production, and what it saves — estimated by the people who would build it, against systems we have shipped.
- 03
Rank the plan
A ranked opportunity map — which workflows, in what order, with what payback — written for the IC memo and the hundred-day plan, not the shelf.
Technology due diligence consulting from the people who build the systems — not diligence software.
Diligence platforms sell AI that reviews documents faster. That is not this. We assess the target’s workflows and price what AI saves after you own it — a build plan, not a license.
The strategy firms attach AI chapters to diligences at strategy prices. We build these systems for a living — the estimate comes from the people who would ship the work.
We get to learn alongside the deal team — and then there’s good momentum into execution.
Led by Peter Enestrom.
Asked by deal partners mid-diligence.
The straight answers, before you book anything.
AI due diligence for private equity is a pre-close assessment of an acquisition target’s AI and automation opportunity: which workflows can be automated, what the builds cost, how long they take, and how much money they save. It is not diligence software, and it is not a technology audit of what the target has — it is an AI opportunity assessment, priced for the investment committee memo.
Two weeks, fixed fee, inside the diligence window. The first week maps the target’s workflows from the materials the deal already has — the CIM, the data room, management sessions. The second prices each build: cost, time to production, and savings. The ranked opportunity map lands before close, in time to shape the investment committee memo.
A ranked opportunity map, written to go on your own paper: each automatable workflow, what the build costs, how long it takes to production, and what it saves, with the assumptions stated. Deal teams drop it into the IC memo; operating partners pick it up after close as the hundred-day plan. It is a build plan, not a platform demo.
From the same materials the deal runs on, plus what we have measured building these systems elsewhere. Every number is a range with its assumptions attached: volume through the workflow, loaded cost per hour, the exception rate the rules must absorb. If the deal closes, the first build replaces the ranges with the target’s actuals.
The assessment becomes the build plan. The diligence fee credits toward the first build, the top-ranked workflow goes into production first, and what the deal team learned carries into execution instead of dying at the handoff. One large-cap fund’s AI lead describes the shape: learn alongside the deal team, then momentum into execution.
Technology due diligence assesses what the target has: systems health, security, technical debt, the team’s ability to scale. AI diligence assesses what the target could save: which workflows AI can take over, at what build cost, and with what payback. Most deals need the first; the second is where the value creation plan’s AI chapter comes from.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.