AI for private equity
Confident investment decisions and measurable portfolio value
From AI technology experts and builders with investing experience.
The firm
Rostra works with private equity investors and their portfolio companies on AI across the deal lifecycle, from early research through post-close.
The firm evaluates a target's technology differentiation, data position, and durability against proprietary scoring methods it maintains across engagements, then sets the strategy and builds the products and commercial programs the investment case depends on.
Rostra's clients include investors with more than $200 billion in assets under management.
Services
Across the deal lifecycle
Rostra tests an AI claim the way an engineer would and reports it the way an investor needs to read it. It does that at three points in a deal. Each is scoped on its own, and a deal team can use any one without the others.
A screen and a diligence assessment score the same eight dimensions, so an early read and a final answer sit on one scale. The post-close work starts from the diligence findings.
Before diligence
Deal screen
A deal team has to decide how hard to lean in before it starts making calls. Rostra builds a technical read ahead of the first one, from whatever evidence is available.
The read covers how the product's architecture drives its economics and how defensible the technology and data are, scored on eight dimensions. The team leaves with the questions to ask on each call and a guide to what strong and evasive answers sound like.
For a team asking whether an AI-native or the platform next door takes this company's customers.
In diligence
Diligence assessment
Commercial and technical diligence rarely go deep enough on AI to settle the question. Rostra tests the model, data, and product choices the thesis rests on, against management and the data room.
The work separates what ships today from what is still a prototype, and it establishes who holds the rights to the data and what a challenger could get from a customer export. It ends in a committee-ready memo that ranks the risks, names the findings that would change the answer, and estimates what execution costs after close.
For a committee asking whether the data is worth what the seller says and whether the lock-in holds once competitors ship AI.
After close
Value creation
After close, the question is what to build first and who owns it. Rostra works with management to set the priorities, the roadmap, and the pricing, packaging, and go-to-market plans behind them.
Then it builds alongside the company's team and brings in engineering talent where the build needs it. The work is done when software is in production and the commercial program has an owner inside the company.
For an owner asking what to build first and which companies get AI money first.
Point of view
Every competitor can buy the same AI.
Growth goes to the company that already has the customers, the trust, the data, and a founder who did the job.
The AI in most plans is available to every competitor, including the platform already installed next door. Engineering matters, and the assessment tests it. What cannot be hired is what the company had before AI showed up.
Growth comes from one place. Customers pay people to do work around the product, and that payroll is many times the software bill. AI lets the company do some of that work and charge for it. The company that holds those four gets that budget first. The company without them ships the same feature and cuts price to keep up. Growth shows up first as current customers paying more.
Whether a company holds those four, and whether they survive the competitor next door, is what the assessment tests. It reads the company and its market, and ends in three things a deal team can act on.
The company
Defensibility to AI
Whether the customers, the trust, and the founder's knowledge of the work still hold once every competitor has the same models, and which advantages get repriced at renewal.
Maturity with AI
What in the plan ships today, what is still a pilot, and whether this team can ship the rest on the plan's timeline.
AI product engineering
How the product is built and served, which models it depends on, and whether the margin in the plan survives the cost to run it at volume.
Data durability
What the product records as it runs, what leaves when a customer does, and what the contracts let the company keep and learn from.
The market
Trends
What customers in this market still pay people to do, and how soon AI does it instead.
Competitors
Who else can sell AI into the same accounts, and how long each would need to ship the same thing.
Displacement risks
Which parts of the product a customer could rebuild with a general tool it already pays for, and what that does to the renewal price.
The outputs
Investment assessment
Where the thesis holds, where it does not, and what the difference is worth in the price.
Value creation
What to build first and what it is worth, with a named owner.
Risk mitigations
Each risk ranked, with whether it gets fixed after close or belongs in the price.
Example work products are available on request, including a sample deal screen, a diligence report, and the assessment methodology.
About Rostra
Background and team
John Larson founded Rostra in 2024 after seven years at McKinsey, where he led its AI for private equity practice in North America. An electrical engineer by training, he has worked in machine learning for twenty years, building systems, running the teams that ship them, and advising the investors who back them.
He watched the same thing happen in deal after deal. The AI question got asked, and the answer came back as a list of technical risks and nothing on what they meant for the investment. Rostra was built to supply that second half. It looks at what runs in production, the data, and the team the way an engineer would, then says what they mean for the deal in the words a committee uses. The upside, what it costs, which risks get fixed after close and which belong in the price. A deal team holds one assessment it can argue from. Because the firm also builds what it recommends, its findings come from people who expect to be held to them.
John leads each engagement and staffs it from a bench of senior advisors, including machine learning practitioners, former software company chief technology officers, and operating partners from private equity advisory firms. The firm is based in Los Angeles.
Contact
Every company in your portfolio can buy the same AI. What each one already holds decides which of them grows.
contact@rostratechnology.comReplies within one business day.