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 OCIO-Advised Endowment Target List
For: Directors of Fundraising at growth equity funds building a systematic endowment and foundation coverage strategy The Job: Identifying OCIO-advised endowments and foundations with active or recently approved growth equity mandates, ranked by actionability
The prompt
I'm leading fundraising for a $2B growth equity fund focused on B2B software. Build me a list of endowments and foundations between $500M and $5B AUM that currently allocate to growth equity or venture capital through an OCIO or consultant. Show the OCIO firm name, consultant of record, investment contact at each institution, their strategy preferences, and any recent manager presentations. Flag accounts where the OCIO has approved growth equity mandates in the last 18 months and prioritize the top 20 most actionable targets.
2. The Pre-IPO CFO Candidate Map
For: Principals at executive search firms running CFO mandates for PE-backed financial services companies approaching a liquidity event The Job: Building a ranked candidate universe of senior finance leaders with IPO or capital raise experience at sponsor-backed specialty finance companies
The prompt
I'm conducting a CFO search for a $350M ARR PE-backed specialty finance company preparing for an IPO in 18-24 months. Using Dakota Marketplace, identify senior finance leaders — CFOs and SVPs of Finance — currently at PE-sponsored financial services or specialty finance companies with revenue between $200M and $750M in the Northeast and Mid-Atlantic. Prioritize candidates who have navigated a company through an IPO or significant capital raise, have been in their current role for at least 2 years, and have direct contact information on file. Return a ranked PDF with name, current employer, sponsor, tenure, revenue or AUM context, and direct contact details.
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 Public Pension Real Estate Manager Review
For: Senior Investment Officers at large state public pension funds conducting periodic private real estate manager reviews The Job: Surfacing actively fundraising value-add and core-plus real estate managers, benchmarking fees, and identifying managers appearing across multiple peer pension datasets
The prompt
I'm a Senior Investment Officer at a $45B state public pension fund reviewing the private real estate manager landscape — value-add and core-plus strategies, US-focused. Using Dakota Marketplace, surface managers currently fundraising, what peer public pensions have allocated to in the last 24 months, and any managers appearing in multiple datasets as priority candidates. Include fee benchmarks with separate-account and large-LP terms and flag outliers. Datasets: Form D, Public Pension Investments, Manager Presentations, Fee Studies.
4. The Healthcare Equity Roadshow Target List
For: ECM and Equity Capital Markets Directors managing follow-on equity offerings for publicly traded healthcare technology companies The Job: Building a targeted institutional roadshow list of allocators who have recently increased healthcare technology and digital health equity exposure
The prompt
I'm managing a follow-on equity offering for a $2B publicly traded healthcare technology company. Using Dakota Marketplace, find all institutional allocators — large asset managers, healthcare-focused endowments, and long-only equity funds — with AUM over $500M that have recently increased their healthcare technology or digital health equity exposure. Include the portfolio manager or analyst name, AUM, and direct contact info so I can build a targeted roadshow list.
5. The Global Flagship Fund LP Roster
For: Capital Formation and Investor Relations leaders at large global private equity firms pre-marketing a new flagship fund The Job: Building a complete LP target roster combining re-up probability scoring for existing LPs with a prioritized new-money prospect list across all major allocator channels
The prompt
I lead Capital Formation and Investor Relations at Warburg Pincus and we're pre-marketing our next global flagship fund. Using Dakota Marketplace, build a PDF LP target roster: existing LPs likely to re-up with check-size context and relationship status, plus 40 prioritized new-money prospects — public pensions, corporate pensions, sovereign wealth, endowments, large single-family offices — that have made new global PE commitments in the last 24 months.
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.
