Dakota Marketplace is the global private markets intelligence platform used by thousands of investment professionals to research LPs, GPs, and private companies — and the data source powering every prompt in this post. Built by fundraisers for fundraisers, Dakota Marketplace delivers complete, accurate, and daily-updated intelligence across every allocator channel — from family offices and RIAs to sovereign wealth funds and public pensions. Learn More | Book a Demo
AI tools like Claude and ChatGPT can now connect directly to databases… and for anyone working in private markets, that's a major unlock.
But only if you're actually using it.
The professionals pulling ahead right now aren't waiting for AI to become part of their firm's official process. They're building it into their daily workflow today whether for meeting prep, prospect research, outreach, or competitive intelligence.
The difference between a generic AI tool and the Claude App connected to Dakota Marketplace is the difference between a guess and a grounded answer.
Generic AI has no access to 30 years of verified LP, GP, fund, and transaction data. It hallucinates. It generalizes.
Dakota Marketplace’s Claude App doesn't return rows. It returns intelligence, built on the only dataset built exclusively for the private markets community.
Here's what that looks like in practice, five things we learned today.
1. The Sponsor-Backed Healthcare Services Deal Sourcing Map
For: Senior Associates on private equity deal teams sourcing proprietary buy-side opportunities in healthcare services before assets are formally marketed. The Job: Identifying PE-sponsored healthcare services companies approaching the end of a typical hold period, with C-suite contacts and sponsor harvest period flags for early relationship building
The prompt
Search Dakota Marketplace for PE-sponsored healthcare services companies that received their initial investment between 2018 and 2021 and have revenue between $10M and $100M. I am looking for potential buy-side targets before they are formally brought to market. For each company, return: company name, headquarters city and state, PE sponsor name, year of initial investment, estimated revenue or EBITDA if available, business description, and known C-suite contacts — CEO, CFO, COO — with LinkedIn profiles or email addresses. Flag any companies whose sponsor fund is known to be in its harvest period.
2. The APAC Institutional Investor Roadshow List
For: Directors of Capital Formation at global mid-market private equity firms preparing first-time APAC fundraising roadshows. The Job: Building a prioritized list of sovereign wealth funds, government pension plans, and insurance companies across Japan, South Korea, Australia, and Singapore with verified U.S. or global PE allocation history and identified decision-makers
The prompt
Using Dakota Marketplace, build me a list of institutional allocators in Asia-Pacific — specifically sovereign wealth funds, government pension plans, and large insurance companies in Japan, South Korea, Australia, and Singapore — that have demonstrated allocations to U.S. or global private equity in the last three years. For each, provide: AUM, alternatives target allocation percentage, existing PE manager relationships, key investment decision-makers with direct contact information, whether they work with a consultant or OCIO, and any recent public mandate or RFP activity. I'm preparing for a roadshow to APAC and want to prioritize first meetings with the 10 allocators most likely to be receptive to a mid-market buyout strategy.
These prompts are only as good as the data behind them. Every prompt above runs on Dakota Marketplace data: the verified contacts, AUM, investment preferences, and transaction activity that turn a generic AI answer into a real prospect list. Whichever AI app you use, the facts come from the same place. Book a demo of Dakota Marketplace to get connected.
3. The Digital Health Follow-On Equity Offering Target List
For: Directors in healthcare investment banking advising on follow-on equity offerings for publicly traded digital health companies. The Job: Building a tiered institutional investor target list segmented by existing healthcare holders vs. new-to-name opportunities, flagged by prior participation in comparable digital health equity raises
The prompt
I'm a Director in healthcare investment banking advising on a $300M follow-on equity offering for a publicly-traded digital health company focused on ambulatory care software. Using Dakota Marketplace, identify institutional investors — large asset managers, healthcare-focused endowments, long-only equity funds, and healthcare system investment offices — with AUM over $500M that have an active allocation to healthcare or life sciences equities. Surface accounts where the portfolio manager or healthcare analyst name and direct contact are on file. Flag investors that have participated in comparable digital health or health IT equity raises in the last 24 months. Return a ranked target list of 40 priority accounts as a PDF, organized by tier: existing healthcare holders vs. new-to-name opportunities.
4. The PE-Backed Healthcare CFO Candidate Map
For: Partners at private markets-focused executive search firms building candidate slates for CFO searches at healthcare and healthcare IT portfolio companies. The Job: Mapping sitting CFOs across comparable PE-backed healthcare companies, flagging long-tenured executives and those with Big Four or bulge bracket backgrounds as highest-priority transition candidates
The prompt
Using Dakota Marketplace portfolio company and career history data, identify individuals currently serving as CFO at PE-backed healthcare or healthcare IT companies with revenues between $50M and $300M in the United States. For each person, show their full name, current company, PE sponsor, years in current role, educational background, and prior employer. Flag candidates who have been in their current role for 5 or more years — suggesting potential openness to transition — and those who previously served at Big Four accounting firms or bulge bracket investment banks. Export as a formatted candidate map I can bring to an initial client kickoff meeting.
5. The Emerging Private Credit Manager Screening Report
For: Investment Officers at public pension funds evaluating emerging manager program candidates in private credit. The Job: Screening private credit managers launched between 2018 and 2023 with verified performance data, filtered to exclude existing portfolio relationships and ranked by strategy fit for an emerging manager allocation
The prompt
Using Dakota Marketplace, identify private credit fund managers that launched their flagship strategy between 2018 and 2023, have raised between $200M and $1.5B in total AUM, and have verified performance data on file — IRR, TVPI, DPI. I am evaluating candidates for our emerging manager program. For each manager, return: fund name, firm name, strategy description — senior secured, mezzanine, distressed, etc. — fund vintage year, total AUM raised, verified net IRR and TVPI where available, key portfolio managers and their backgrounds, location of firm headquarters, and current LP roster if known. Exclude managers already in our existing portfolio.
Start Prompting With Real Data
Here's the thing that makes these prompts work… on its own, AI is brilliant at structure and terrible at facts it doesn't have. Ask any chatbot for a pension fund's current allocation, a CIO's contact, or who actually owns a target company, and it will confidently make something up.
That's the whole reason these prompts run on Dakota Marketplace data, no matter which AI app you prefer: you get the speed and structure of AI with contacts, AUM, allocations, and transactions that are actually verified.
AI is the engine. Dakota Marketplace is the fuel.
Connect the two, in Claude, ChatGPT, or whatever you already use, and the work that used to eat your morning takes minutes, with data you can actually act on.
Book a demo of Dakota Marketplace to get started.
