Diligence, portfolio rollout, reporting, exit readiness and public-market communication. Hands-on training for funds and their portfolio companies, with the cost and data controls built in, from the founder who ran the original public-markets playbook by hand.
Analysts working through folders page by page, with findings assembled by hand.
A queryable workspace over deal materials, filings and models, so the team interrogates the data room. A professional still confirms every finding.
Each portfolio company buying and experimenting alone, with no shared controls.
A governed rollout with per-user limits and permissions, piloted on a small group first, then repeated across the portfolio.
Monthly and LP reporting pulled together from spreadsheets and emails.
Reporting drafted from source data and reviewed, not built. Your own AI capability becomes a question you can answer, not one you dodge.
Most companies treat a listing as an event: get to the bell, get the pop, move on. The approach I ran treated it as an education campaign that started before the listing and continued after it, on the view that a shareholder base that understands what it owns trades it more rationally than one that doesn't. Public-market investors, retail and institutional, were briefed on the business in plain terms before the deal closed and kept briefed once trading began, rather than left to work it out from a filing.
I took a company from seed funding to a NASDAQ listing, including the investor education programme before and after the deal.
The cost of skipping investor education shows up hardest at exactly the moment a listing needs it most. Across the 2021 to 2022 SPAC cycle, average monthly redemption rates, the share of investors pulling their money out rather than staying in for the merger, rose from a 7 to 43% range in the first half of 2021 to 43 to 67% in the second half, and above 81% through 20221. A deal that loses most of its cash at closing has little left to fund the business it just took public. That is what happens when a market has not been told a coherent story before being asked to hold the stock.
The playbook that avoids this has not changed. What has changed is the cost of running it. Reading a shareholder register, briefing a roadshow, drafting registration statements and staying on top of ongoing disclosure used to take an IR team, external counsel and weeks of calendar time. Much of that can now be done in a fraction of the time, without changing what the strategy is trying to achieve. I teach teams how to do it.
Weeks of due diligence prep, a growth story built by hand from spreadsheets and comparables, AI capability assessed informally if at all.
Due diligence assistants that consolidate deal materials, filings and financial models into one queryable workspace, so a team can interrogate a data room instead of reading it page by page. Your own AI capability becomes a question you can answer, not one you dodge.
An IR team and counsel hand-building the S-1 or S-3, manually identifying investors worth targeting, and coordinating roadshow logistics meeting by meeting.
AI drafts and cross-checks registration statement language against comparable filings, flags inconsistencies, and speeds first-pass review. Investor targeting uses portfolio and engagement data to rank who to see, and briefing-pack assembly becomes a first draft, not a blank page. Every disclosure decision still goes through legal counsel before it ships.
Quarterly 13F aggregation done by hand to see who actually owns the stock, 8-K drafting on a deadline, and earnings narratives written from scratch each quarter.
AI aggregates 13F filings automatically to track new institutional holders, ownership concentration and free-float risk every quarter, flags material-event disclosures for faster 8-K turnaround, and and drafts earnings narratives and Q&A prep from the underlying numbers. Portfolio reporting that used to be assembled by hand becomes a review task.
Founder and CEO from seed funding to a NASDAQ listing in 2021, including the investor education programme before and after the deal.
I have built and deployed large language models across legal and public markets, and I run agentic systems in production today.
I have lived the disclosure, materiality and selective disclosure risks first hand, so I teach where AI helps and exactly where it must stop.
What AI does not do here. Disclosure decisions, materiality assessments and anything that touches selective disclosure risk stay with legal counsel and the board. AI drafts, checks and flags; it does not decide what a public company is obliged to tell the market, and treating it as though it could is the fastest way to turn an efficiency gain into a compliance problem.
Every engagement is scoped and quoted after the free discovery call. How each one runs is on the main page.
30 minutes, no deck, just what this would look like for your fund or your portfolio company's next raise.
Book a call1SPAC redemption rate data, 2021 to 2022 cycle: monthly average redemption rates rising from 7 to 43% (H1 2021) to 43 to 67% (H2 2021) to above 81% (2022).
Peak valuation and deal figures: closing 8-K share count and public market pricing data. Full citations and workings available on request.