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 Healthcare Royalty Allocator Tearsheet Pack
For: Heads of Capital Formation at life sciences royalty and structured healthcare funds raising a successor vehicle. The Job: Building a tearsheet pack of endowments, foundations, and public pensions with healthcare royalty, pharma credit, or structured life sciences exposure ahead of a Fund III first close, with a tailored approach note and a timing edge for each allocator.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of endowments, foundations, and public pensions that have committed to a healthcare royalty, pharmaceutical credit, or structured life sciences fund since 2021, or that list royalties or specialty finance in their private credit or real assets allocation. I’m the Head of Capital Formation at a $750M life sciences royalty fund preparing for a Fund III first close. Limit to institutions with 1B-40B in AUM. For each one, give me AUM, the relevant target allocation percentage, their most recent commitment in the space (manager, fund, amount, date), and the two contacts most likely to own the decision with titles. Then write a short approach note on how to frame a royalty strategy for that specific allocator (uncorrelated yield, inflation linkage, or diversification away from biotech venture), and an edge note on anything that gives us a timing or relationship advantage, such as a recent board meeting, a manager they just exited, or a consultant we already know.
2. The Asset-Based Finance Allocator Tearsheet Pack
For: Partners and Heads of Capital Formation at asset-based finance managers raising a Fund II. The Job: Building a tearsheet pack of endowments, foundations, and public pensions that have created or expanded an asset-based finance or specialty finance sleeve, so the team can prioritize LPs already underwritten to the asset class and leave Fund I investors out.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of 6-8 endowments, foundations, and public pensions that have committed to an asset-based finance, specialty finance, or consumer/commercial receivables fund in the last 36 months, or that have created a dedicated ABF sleeve within private credit. I’m a Partner and Head of Capital Formation at an asset-based finance manager raising Fund II. Exclude any institution already in our Fund I LP roster. For each institution, give me: type and location, AUM, private credit allocation %, most recent ABF or private credit commitment (manager, fund, amount, date), the two most relevant decision-makers (CIO and Head of Private Credit or equivalent) with title and email, a 2-3 sentence approach recommendation, and the edge we should lead with. One institution per page, formatted as a tearsheet I can hand to our partners before a roadshow.
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 Farm-Belt Mission-Fit Allocator Tearsheet Pack
For: Partners and Heads of Capital Formation at growth equity managers investing in agtech, food processing, and agricultural supply chain businesses. The Job: Building a tearsheet pack of land-grant university endowments, agriculture-mission foundations, and farm-belt public pensions with a natural mandate fit for a food and agtech growth fund, with an approach note and a mission-fit edge for each.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of 6-8 institutions for our $400M Fund II, a growth equity strategy investing in agtech, food processing, and agricultural supply chain businesses. I’m a Partner and Head of Capital Formation at the firm. Prioritize land-grant university endowments, foundations with agriculture, rural development, or food security missions, and public pensions in farm-belt states (IA, NE, KS, MN, ND, SD, WI). For each, give me AUM, private equity or real assets allocation percentage, last relevant commitment (manager, fund, amount, date), and two key contacts: the decision-maker and the day-to-day due diligence lead, with titles. Add an approach note on how to open the conversation and an edge note explaining why our strategy fits their mission or portfolio gap right now.
4. The Venture Debt White-Space Allocator Tearsheet Pack
For: Managing Partners and Heads of Investor Relations at venture debt managers raising a successor fund. The Job: Building a tearsheet pack of endowments, foundations, and public pensions that already back venture capital funds but have no venture debt commitment on record, so venture lending can be positioned as a lower-volatility, cash-yielding complement to their existing VC exposure.
The prompt
Using Dakota Marketplace, build a tearsheet pack of 6 endowments, foundations, and public pensions that hold at least three active venture capital fund commitments made since 2021 but have no venture debt or growth-lending commitment on record. I’m the Managing Partner and Head of Investor Relations at a venture debt manager, and we are raising a $400M Venture Debt Fund III (senior secured loans to Series B-D companies, 12-14% target gross yield, quarterly cash distributions). For each institution include: type and location, AUM, private credit and venture allocation %, most recent venture or private credit commitment (manager, fund, size, date), two key contacts (CIO or head of private markets plus the PM covering private credit or venture) with titles, a recommended APPROACH that ties our yield and downside protection to their stated liquidity or distribution goals, and an EDGE section noting any board minutes, pacing-plan language, or VC portfolio overlap that gives us a reason to call now. Give each institution its own card.
5. The Healthcare Mission-Fit Allocator Tear Sheet Pack
For: Partners and Heads of Capital Formation at healthcare services growth equity firms raising a successor fund. The Job: Building a tear sheet pack of health-system investment pools, health foundations, academic medical endowments, and healthcare-worker pensions whose mission and operating knowledge make them natural LPs, with a mission-tied approach angle and a liquidity flag for each.
The prompt
Using Dakota Marketplace, build me a tear sheet pack of 6 allocators for our $600M healthcare services growth equity Fund III: nonprofit health-system investment pools, health-focused private foundations, academic medical center endowments, and public or Taft-Hartley pensions covering nurses and hospital workers. I’m a Partner and Head of Capital Formation at the firm. Only include institutions with $750M+ in AUM, an active private equity or growth equity allocation, and at least one private markets commitment since January 2025. For each, give me: institution type and location, AUM, PE/growth target allocation %, most recent commitment (manager, fund, amount, date), the two most relevant decision-makers with titles, a recommended approach angle that ties our thesis to their mission or operating expertise, and any edge we have (shared portfolio relationships, consultant overlap, prior meetings logged in Dakota). Flag any health system with liquidity constraints from bond covenants or operating losses.
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.
