Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. 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 looked like in practice this week, five things we learned.
1. The Digital Infrastructure Allocator Tearsheet Pack
For: Partners and Heads of Investor Relations at digital infrastructure managers raising a Fund II. The Job: Building a tearsheet pack of endowments, foundations, and public pensions that have backed data-center or digital-infrastructure funds, so the IR team can prioritize Fund II meetings.
The prompt
Using Dakota Marketplace, build a tearsheet pack of 6-8 endowments, foundations, and public pensions that have committed to at least one data-center, fiber, tower, or broader digital-infrastructure fund since January 2023 and still list real assets or infrastructure as an active allocation. I’m a Partner and Head of Investor Relations at a $1.4B Digital Infrastructure Fund II (Data Centers & Fiber). For each institution give me: type and location, total AUM, infrastructure/real assets target allocation %, the most recent digital-infrastructure commitment (manager, fund, amount, date), the CIO and the senior infrastructure or real assets decision-maker with titles, emails, and phone numbers, consultant of record, and any power, energy, or ESG constraints noted in their investment policy. Then write a short APPROACH (how to open the conversation given their existing exposure and pacing) and an EDGE (the specific reason our power-secured, mid-market data-center strategy is differentiated for them). Prioritize institutions with upcoming investment committee meetings in Q1 2027.
2. The Consultant-Transition Allocator Tearsheet Pack
For: Heads of Capital Formation at lower middle-market buyout managers raising a successor fund. The Job: Building a tearsheet pack of endowments, foundations, and public pensions that are switching or just switched investment consultants, so outreach can be timed to the consultant-transition window.
The prompt
Build a tearsheet pack of endowments, foundations and public pensions that issued an investment consultant RFP or hired a new general or private markets consultant in the last 12 months. I’m the Head of Capital Formation at a $1.8B Lower Middle-Market Industrials Buyout Fund IV. For each, show AUM, current private equity allocation vs. target, the outgoing and incoming consultant, the date of their last private equity commitment, and two key contacts (CIO and the staff lead on private markets) with email and phone. Add a recommended approach that explains how to get on the new consultant’s radar and what the board is likely to prioritize in the first year of the new relationship. Focus on institutions with 1B-15B in AUM that have historically backed lower middle-market buyout.
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 Latin America Private Credit Allocator Tearsheet Pack
For: Managing Partners and Heads of IR at emerging-markets private credit managers raising a Fund III. The Job: Building a tearsheet pack of US endowments, foundations, and public pensions with emerging-markets or Latin America private markets exposure to target for a Fund III first close.
The prompt
Build a tearsheet pack of 6-8 US endowments, foundations and public pensions in Dakota Marketplace that have committed to an emerging-markets or Latin America-focused private credit or private equity fund in the last 36 months. I’m the Managing Partner and Head of IR at a $650M Latin America Mid-Market Private Credit Manager. For each institution, show AUM, current private credit or EM allocation %, most recent relevant commitment (fund, size, date), and two key contacts: the CIO and the investment staff member covering private credit or international. Add an APPROACH note on how to open the conversation given their mandate and portfolio gaps, and an EDGE note on why a USD-denominated, senior-secured Latin America lender fits their book right now. We’re targeting a $400M Fund III first close in Q1 2027.
4. The Cybersecurity Growth Equity First-Meeting Tearsheet Pack
For: Partners and Heads of Capital Formation at technology growth equity firms raising a Fund II. The Job: Building a tearsheet pack of endowments, foundations, and public pensions with technology growth-equity exposure, to prioritize first meetings for a $400M cybersecurity and critical-infrastructure software fund.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of 6 US endowments, foundations and public pensions that are the best first-meeting targets for our $400M Fund II, a growth equity fund investing $20-60M in cybersecurity, identity and OT/critical-infrastructure security software companies at $15-75M ARR. I’m a Partner and Head of Capital Formation at the firm. Prioritize allocators that (1) have committed to at least one technology-focused growth equity or venture fund in the last 24 months, (2) have AUM between $1B and $30B, and (3) back Fund II or emerging managers. For each institution, include: type and location, AUM, private equity / growth allocation %, most recent relevant commitment (manager, fund, amount, date), two key contacts (title, email, phone), a recommended approach angle for the first meeting, and our edge: why our strategy fills a specific gap in their current portfolio.
5. The Southeastern Private Credit Pacing-Gap Tearsheet Pack
For: Heads of Capital Formation at private credit managers building their 2027 pipeline. The Job: Building a tearsheet pack of Southeastern US endowments, foundations, and public pensions with rising private credit targets and open pacing capacity for 2027.
The prompt
Build a tearsheet pack of Southeastern US endowments, foundations and public pensions ($2B+ AUM) that have increased or formally raised their private-credit target allocation in the last 12 months and are still below target. I’m the Head of Capital Formation at a $4B Private Credit Manager. For each, show AUM, current private-credit allocation vs. target, last private-credit commitment (size and date), two key contacts with titles and tenure, the recommended approach and the edge Dakota data gives us. Order by size of the gap to target, flag any institution that works through an OCIO or consultant gate, and exclude any LP currently in our fund’s pipeline or existing LP base.
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.
