For one portfolio company

AI Value Sprint

Readiness assessment, workflow mapping and a signed business case, then working software built with the company’s own team and taken live in about a quarter, with the return measured against the case.

Engagement length · about one quarter, with an MVP in about 7 weeks and go-live in about 13

Book the sessionDiscuss a company
Pick a time on the calendar (opens in a new tab)

Outcomes

What the engagement creates.

Each engagement is built around a measured result, and the artifacts and habits your team keeps after it ends.

A readiness assessment and time study of the workflows that eat the most hours.

A business case with a target return and a payback date, signed by partners and management.

Working software in production, built with the company’s team and run in shadow mode first.

A value dashboard: adoption, hours saved, margin impact and payback, tracked against the case.

Recent work

What the work looks like in practice.

AI Value SprintIn progress

Automating the data work at a compliance services company

PE-backed HR compliance and analytics services business, with most of the work done by analysts in spreadsheets.

Situation
A regulatory change removed a core revenue line. The business needed to protect margin fast without losing service quality.
What we did
Mapped and timed three workflows: chasing clients for missing data, verifying data, and standard data prep. Wrote the business case, then built the automation with the company’s own team, running in shadow mode before go-live.

Outcome

In progress. Results are published here once they are measured.

MVP in about 7 weeks, live in about 13

Engagement Model

How it runs.

This engagement runs about one quarter, with an MVP in about 7 weeks and go-live in about 13. Pricing is by scope, not by hours.

1

Weeks 0-2: prove the case

Map and time the work, check the data and the systems, and agree the target and the measure with partners and management.

2

Weeks 3-7: build the MVP

PRDs, an implementation plan, and a first version built with the company’s own engineers and reviewers, running alongside the current process.

3

Weeks 8-13: go live and measure

Harden security and data handling, take it live, train the people who use it, and report the return against the case.

The first step

Ninety minutes on one company, or one decision.

Most advisory relationships start with a discovery call that discovers nothing. This one starts with work. Send the context beforehand: the company, the workflows that eat the most hours, the AI pilots already running. We spend ninety minutes on where AI pays back first and what it would take to prove it. You leave with a written point of view: the play to start with, the business case to test, and what the first ninety days look like.

Format

90 minutes, one decision

Deliverable

Written point of view within 48 hours

What follows

Fixed scope, quoted in writing after a first conversation. Never billed by the hour.

If ninety minutes shows this is not a fit, Kevin will say so and point you somewhere better. Confidential by default. Happy to sign your NDA before the first call.

Book the sessionPick a time on the calendar (opens in a new tab)

Fit

Best fit scenarios.

Companies where most of the cost is people and paperwork: data intake, verification, reports, scheduling, outreach.

Legacy systems and no in-house AI team, where the build has to work with what is already there.

Regulated data (pay, health, financial records) that needs security and governance agreed up front.

Owners who want capacity and margin first, and a people plan agreed before staff hear about it.

Start

Send the context.

Share a little context. Kevin replies within one business day. Confidential by default. Happy to sign your NDA before the first call.

A few quick details

One tap each. They let Kevin come to the first conversation prepared.

Kevin reads every message himself and replies within one business day. Confidential by default; happy to sign your NDA before the first call.