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 PE and Hedge Fund Compliance Software Prospect List
For: Enterprise Account Executives at legal and compliance technology companies targeting growing alternative investment managers with rising regulatory complexity. The Job: Building a prospecting list of hedge funds and PE funds with active Form D filing history, lean compliance teams, and AUM in the sweet spot for compliance software adoption
The prompt
I sell compliance and regulatory reporting software to alternative investment managers. Using Dakota Marketplace, build me a prospecting list of hedge funds and PE funds in the United States that are likely to have increasing compliance burdens — specifically: funds that have filed a Form D in the last 24 months for a new fund raise over $100M, have AUM between $500M and $5B, and are unlikely to have a fully built-out compliance team — smaller organizations with 10 to 50 employees. For each firm, include the fund name, registered investment adviser name, AUM, key operations or compliance contact — COO, CFO, General Counsel, or Chief Compliance Officer — with email and phone, and number of Form D filings in the last 3 years as a proxy for growth. Prioritize firms that have filed multiple Form Ds recently, indicating active growth and rising compliance complexity.
2. The University Endowment Infrastructure Emerging Manager List
For: Directors of Capital Formation at mid-market infrastructure debt funds raising for energy transition and digital infrastructure strategies. The Job: Identifying university endowments with existing real assets allocations, recent infrastructure commitments, and sustainability mandates that align with an energy transition thesis
The prompt
I'm raising a $750M infrastructure debt fund with a focus on energy transition and digital infrastructure in North America. Using Dakota Marketplace, identify university endowments with AUM between $500M and $5B that have a current real assets or infrastructure allocation of at least 5%, have made a new infrastructure or real assets commitment to a manager in the last 24 months, or have a sustainability or impact overlay in their investment policy. For each endowment, return: institution name, city, total AUM, real assets allocation percentage, current infrastructure or real assets managers in their portfolio, key investment contacts with name, title, and direct email, most recent commitment date, and any consultant relationship. Rank by AUM descending and flag endowments where the current infrastructure manager is approaching a typical hold period or where the endowment has publicly stated a commitment to net-zero or impact targets.
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 MSP Add-On Acquisition Target Map
For: Principals at technology-focused PE funds identifying add-on acquisition targets for recently acquired managed services platform companies. The Job: Surfacing PE-backed MSP, IT services, and cybersecurity managed services companies in the Western US approaching hold period end, with sponsor and leadership contacts for proactive outreach
The prompt
I'm a Principal at a $4B technology-focused PE fund. We recently acquired a regional managed services provider in the Pacific Northwest with $80M revenue. Using Dakota Marketplace, identify potential add-on acquisition targets — specifically PE-backed technology companies in the MSP, IT services, or cybersecurity managed services space with estimated revenue between $20M and $150M. Focus on companies headquartered in the Western US and Pacific Northwest. Show the company name, sponsor name, fund vintage, estimated revenue, CEO and CFO contact info, and any recent Form D filings or add-on acquisition activity. Flag companies that have been backed by the same sponsor for 5+ years or are approaching typical hold period end.
4. The Healthcare IT Sell-Side Mandate Pipeline
For: Managing Directors in healthcare technology investment banking proactively sourcing sell-side mandates before portfolio companies enter a formal process. The Job: Identifying PE-sponsored healthcare IT, health tech, and healthcare SaaS companies from 2019 to 2021 vintages, sorted by investment age to prioritize the most time-sensitive exit conversations
The prompt
Using Dakota Marketplace transaction and sponsor data, show me all PE-sponsored healthcare IT, health tech, and healthcare SaaS companies that received their initial investment between 2019 and 2021. I want to proactively surface potential sell-side mandates before these companies go to a formal process. For each, include: company name, location, sponsor name and fund vintage, estimated revenue range, company description, known leadership contacts — CEO and CFO — and whether any advisor is currently attached. Sort by investment year ascending so I can prioritize the oldest vintage first.
5. The Alternatives Manager Consolidation Benchmark
For: CIOs at mid-sized public pension funds executing board-mandated alternatives manager consolidation across PE, private credit, real assets, and hedge funds. The Job: Benchmarking the most widely held PE and credit managers among comparable public pensions to build a consensus picks list for investment committee review
The prompt
I'm the CIO of a $12B public pension fund. Our board has asked us to reduce the number of alternative managers we work with from 45 to 30 across private equity, private credit, real assets, and hedge funds. Using Dakota Marketplace, pull data on the current alternatives manager landscape to help me benchmark: which PE and credit managers in our size range — $2B to $20B AUM — are most commonly held by comparable public pensions with $8B to $20B AUM? I want to see the top 25 most widely held PE managers by public pension funds in our comparable universe, with each manager's fund count, strategy type, AUM, and how many pensions in our comparable universe hold them. Present as a ranked consensus picks list I can bring to our investment committee.
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.
