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Value creation plans
A sequenced AI and data roadmap for the hold period, tied to revenue, margin, and hours reclaimed.
The gap
Every portfolio company can list AI use cases. Few can show one in the P&L.
The gap is rarely the model. It is the distance between a value creation plan and a system people use on Monday morning. We close it by doing both jobs at once: setting the strategy and writing the code, with success criteria agreed before the first line of code.
Portfolios span every sector. We learn each business fast and hold every build to one technical standard, so a win in one company becomes the playbook for the next.
Where we help
From diligence to production.
01
AI due diligence
An operator’s read on a target’s data and AI position: what is real, what it would take, where the value is, and where AI threatens the model.
See the diligence read →03
Portfolio programs
One technical standard across companies: orchestration, evaluation, and vector store choices, build-vs-buy, and vendor selection.
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Data monetization
Turning a portfolio company’s data into products customers pay for. Our leadership built data-driven revenue from zero to $300M+.
Portfolio and company
Across the portfolio, and inside each company.
We work with the operating team across the portfolio and with the leaders inside each company. The people who scope the work are the people who build it.
01
For the sponsor
- Diagnose with the operating team. Where generative AI and data products create value across the portfolio, sequenced by dollar impact and risk.
- One technical standard. Orchestration, evaluation, and vector store choices, build-vs-buy, and vendor selection, held across every build.
- Playbooks that travel. Every win becomes a reusable playbook and reference implementation for the next company.
02
Inside the company
- Forward deployed engineers build. Next to the people who own the problem: RAG pipelines, evaluation harnesses, agentic and MCP workflows.
- Data monetization. The company’s data, turned into products customers pay for.
- Success criteria and adoption. Agreed with the business sponsor up front, then held through rollout and hand-off.
The deal lifecycle
From before close to the next company.
One accountable team, from the first read on a target to the playbook the next company inherits. The four moves, placed on the deal clock.
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01 · Diagnose
Before close
AI due diligence. What is real in the target’s data and AI, what it would take to deliver, where the value is, and where AI threatens the model.
AI due diligence → -
02 · Diagnose
After close
Where generative AI, agents, and data products create value, and where they don’t. A roadmap for the value creation plan, sequenced by dollar impact and risk.
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03 · Build and prove
The hold period
Forward deployed engineers build inside the company: a working system in a one to six week window, and we stay through rollout until it is used.
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04 · Compound
The next company
The playbook carries over, with a reference implementation for every win, so the second portfolio company costs a fraction of the first.
What the board sees
The value creation plan makes the promise. The P&L carries the proof.
Success criteria are agreed before the first line of code, in numbers an operating partner can take to the board. Results are measured, not asserted.
- 01 Revenue
- Data products and AI that customers pay for.
- 02 Margin
- Cost out of the workflows that cost the most.
- 03 Hours reclaimed
- Manual hours returned to the business.
- 04 Adoption
- A system in use, not just shipped.
The ledger
1,000
About 1,000 manual hours a month returned to the business.
$300M+
Data-driven revenue built from zero to $300M+.
40+
40+ generative-AI use cases evaluated, sequenced, and governed in six months.
Outcomes delivered by our leadership across operating roles and client engagements.
Questions
Questions, answered.
Do you work with sponsors or with portfolio companies?
Both. We can diagnose across a portfolio with the operating team, and embed forward deployed engineers inside an individual company to build.
What does AI due diligence cover?
An operator’s read on a target’s data and AI position before close: what is real, where AI and data products can create value, what it would take to deliver, where AI threatens the model, and the risks. See AI due diligence.
How quickly can a portfolio company see something in production?
After intake and a discovery workshop, we write a scoped, build-ready brief. The build runs in a one to six week window, and success criteria are agreed up front, so results are measured, not asserted.
How does the work carry across a portfolio?
Every win becomes a reusable playbook and reference implementation, and one technical standard (orchestration, evaluation, and vector store choices) is set across builds, so the second deployment costs a fraction of the first.
Which sectors do you work in?
Any sector. Our strength is adapting fast to a new business: the four moves work wherever there is data and a P&L to move, which is why private equity portfolios, spanning every sector, are a natural fit. Our case studies happen to include media, streaming, sport, and ad tech. See industries.
Next
Send us the value creation plan. We will show where AI moves EBITDA, and what it takes to ship.