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 State Workers' Compensation Fund Private Credit List
For: Business development teams at investment-grade private credit managers The Job: Targeting state workers' comp insurance funds, a public insurance pool distinct from the state pension system
The prompt
Using Dakota Marketplace and web research, identify U.S. state workers' compensation insurance funds with $5B+ in invested assets that have a documented fixed income or private credit allocation. Show invested assets, current allocation percentage and target, the fund's chief investment officer or treasurer contact, and any recent manager searches or RFPs referencing private credit.
2. The Professional Association Pension Pool List
For: Fundraisers at diversified fixed income and alternatives managers The Job: Targeting pension pools sponsored by professional associations rather than a single employer
The prompt
Using Dakota Marketplace and web research, identify pension and retirement plans sponsored by national professional associations (medical, legal, engineering, or similar) with $1B+ in plan assets. Show plan AUM, current alternatives or private credit allocation, the plan administrator or investment committee contact, and any recent manager searches.
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 University Quasi-Endowment Direct Lending List
For: Heads of IR at direct lending funds The Job: Targeting the quasi-endowment or board-designated fund pool separately from the true endowment, since liquidity rules often differ
The prompt
I want to target the quasi-endowment or board-designated fund pool at universities separately from their true endowment, since these pools sometimes carry more flexible liquidity terms. Using Dakota Marketplace, identify universities with a disclosed quasi-endowment or board-designated fund of $250M+ that has a documented private credit or direct lending allocation. Show quasi-endowment AUM, allocation percentage, the treasurer or CIO contact, and any distinctions in investment policy versus the main endowment.
4. The Asian Insurance LP-Led Secondaries List
For: Managing directors at secondaries and continuation vehicle funds The Job: Identifying Asian insurers as both LPs and potential sellers in the secondaries market
The prompt
I'm raising capital for a secondaries fund and want to explore Asian insurance companies as both LPs and potential secondary sellers. Using Dakota Marketplace and web research, identify insurance companies in Japan, South Korea, and Taiwan with $10B+ in invested assets that have participated in an LP-led secondary transaction in the last 3 years, either as buyer or seller. Show invested assets, current private equity allocation, key investment contact, and details of their most recent secondary transaction.
5. The Islamic Waqf Endowment Shariah-Compliant PE List
For: Fundraisers at Shariah-compliant private equity and real assets funds The Job: Identifying waqf endowments and Islamic charitable trusts, a distinct LP category with specific structuring requirements
The prompt
I'm raising a Shariah-compliant private equity fund and want to target waqf endowments and Islamic charitable trusts in the Gulf and Southeast Asia. Using Dakota Marketplace and web research, identify waqf endowments and Islamic foundations with $250M+ in assets that have a documented private equity or real assets allocation structured to be Shariah-compliant. Show AUM, allocation percentage, the trust's investment committee or Shariah board contact, and any existing relationships with Shariah-compliant fund managers.
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.
