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 Southeastern Private Credit Gap Tearsheet Pack
For: Heads of Capital Formation at private credit managers raising their next fund. The Job: Building a tearsheet pack of Southeastern endowments, foundations and public pensions that have raised their private credit target and are still below it, ranked by the size of the gap and screened for consultant gates and existing relationships.
The prompt
Using Dakota Marketplace, I’m the Head of Capital Formation at a USD 4B private credit manager. Build me a tearsheet pack of Southeastern US endowments, foundations and public pensions with USD 2B+ in AUM that have increased or formally raised their private credit target allocation in the last 12 months and are still below target, with open pacing capacity for 2027. For each institution, show AUM, current private credit allocation vs. target, last private credit commitment (size and date), and two key contacts with titles and tenure. Add the recommended approach and the edge Dakota data gives us. Order the pack 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.
2. The Mid-Market Direct Lending Mandate Gap Pack
For: Heads of Fundraising at mid-market private credit managers. The Job: Building a one-page-per-institution tear sheet pack of foundations, endowments and public pensions that have recently committed to private credit but have no disclosed lower-middle-market or mid-market direct lending manager.
The prompt
Using Dakota Marketplace, I’m the Head of Fundraising at a USD 2.4B mid-market private credit manager. Build me a tear sheet pack of 6 US institutions (foundations, endowments, and public pensions with USD 2B+ in AUM) that made a private credit commitment in the last 18 months and have no disclosed lower-middle-market or mid-market direct lending manager. For each, include AUM, private credit allocation %, last commitment, two investment-staff contacts with titles, how to approach them, and what gives a USD 2.4B mid-market direct lender an edge. Give me a mix of institution types, one page per institution.
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 Energy Transition Allocator Tearsheet Pack
For: Heads of Fundraising at energy transition growth equity managers. The Job: Building institution tear sheets on endowments, foundations and public pensions with recent climate, energy transition or sustainable growth commitments, each paired with an approach tailored to the manager’s current fund.
The prompt
Using Dakota Marketplace, I’m the Head of Fundraising at a USD 1.6B energy transition growth equity manager raising Fund II. Build me a tear sheet pack for six US endowments, foundations and public pensions that have made climate, energy transition or sustainable growth commitments in the last 24 months. For each, show AUM, private markets allocation %, most recent relevant commitment, and the two most senior contacts with titles. Close each tear sheet with a short approach and edge note tailored to our USD 1.6B growth equity Fund II.
4. The Infrastructure Fund IV First Close Pack
For: Heads of Distribution at core-plus infrastructure managers preparing a first close. The Job: Building institution-level tear sheets on the best-fit endowments, foundations and public pensions with infrastructure or real assets targets, grounded in recent board minutes and consultant relationships.
The prompt
Using Dakota Marketplace, I run distribution for a USD 4B core-plus infrastructure manager preparing a Fund IV first close in Q2 2027 (target USD 2.5B, 8-9% net IRR). Build me tear sheets on the six best-fit endowments, foundations, and public pensions in North America with USD 3B+ in AUM and a stated infrastructure or real assets target of 5%+. For each, include AUM, current infrastructure allocation %, last infrastructure commitment (manager, size, date), and two key contacts with title and email. Finish with a short approach and edge section based on their recent board minutes and consultant relationships.
5. The Infrastructure Debt Fund III Tearsheet Pack
For: Heads of Fundraising at infrastructure debt managers launching a new fund. The Job: Building a tearsheet pack of endowments, foundations and public pensions with recent infrastructure or private credit commitments, each with the right internal contacts and an approach angle.
The prompt
Using Dakota Marketplace, I’m the Head of Fundraising at a USD 9B infrastructure debt manager launching our third fund with a USD 2B target. Build me a tearsheet pack of five endowments, foundations and public pensions that have made infrastructure or private credit commitments in the last 18 months. For each, show AUM, infrastructure and credit allocation %, and last commitment. Include the CIO and the best contact for real assets, plus an approach angle and what gives us an edge.
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.
