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 looks like in practice, five things we learned today.
1. The Corporate DB Plan Direct Lending Tearsheet Pack
For: Directors of Investor Relations at direct lending funds targeting corporate defined benefit pension plans that are derisking into fixed-income-like private credit strategies. The Job: Building a tearsheet pack of corporate DB plans with a quantified gap to their private credit target, tiered by funded status and consultant relationship.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of U.S. corporate defined benefit pension plans that are credible LP targets for our $1.2B senior direct lending fund. Filter to plans with $1B+ in assets and a funded status above 90% that either hold an existing private credit or direct lending allocation or carry a stated target they're underinvested against. For each plan give me total plan assets, funded status, current and target private credit allocation percentages, the dollar gap to target, the most recent disclosed direct lending or private credit commitment with manager name and size, and the investment consultant of record. Include full contact details for the Director of Pension Investments or Treasurer. Rank into Tier 1 (dollar gap plus a consultant we already know), Tier 2 (build the relationship this year), and Tier 3 (monitor). For each Tier 1 name draft a two-sentence opening angle referencing their specific funded status and allocation gap.
2. The Fund-of-Funds Debut Manager Allocation List
For: Placement agents raising capital for a debut private equity fund from multi-manager and fund-of-funds platforms. The Job: Building a target list of fund-of-funds platforms with a documented history of backing first-time managers, tiered by check size fit and recency of emerging manager activity.
The prompt
Using Dakota Marketplace, build me a target list for a $400M debut buyout fund. I'm placing capital with fund-of-funds and multi-manager platforms that have a documented history of anchoring or co-investing alongside first-time private equity managers. Filter to platforms with $2B+ in assets under management that have made at least one emerging manager commitment in the past 24 months. For each platform, give me total AUM, typical emerging manager check size, sector and geography preferences, the most recent disclosed first-time fund commitment with manager name and size, and whether they typically take a board or advisory seat. Include the lead manager selection contact with full details. Tier into Tier 1 (recent debut-fund commitment, check size fits), Tier 2 (stated emerging manager mandate, no recent deal), and Tier 3 (plausible but unconfirmed appetite). For each Tier 1 name draft a two-sentence opening angle referencing their specific prior debut-fund commitment.
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 Institutional-Threshold Hedge Fund Prospecting List
For: Business Development Directors at operational due diligence software vendors selling to hedge funds preparing for institutional capital. The Job: Identifying hedge funds approaching the AUM and headcount thresholds where institutional allocators start requiring formal ODD infrastructure, tiered by urgency.
The prompt
Using Dakota Marketplace, I'm prospecting for our operational due diligence software. Identify U.S. hedge funds between $300M and $2B in AUM that have raised assets by 30%+ in the past 18 months or have recently received a first-time allocation from a public pension, endowment, or insurance company, a signal that institutional ODD scrutiny is coming. For each firm give me current AUM, AUM growth rate, most recent disclosed institutional allocator commitment, COO or CFO name and tenure, and any disclosed third-party ODD provider relationship. Tier into Tier 1 (recent institutional commitment, no disclosed ODD provider, urgent need), Tier 2 (growing but likely already served), and Tier 3 (stable, low urgency). For each Tier 1 firm draft a two-sentence opening line referencing their specific institutional allocator win.
4. The Sovereign and Family Office GP Stakes Target List
For: Heads of Capital Formation at GP stakes funds raising a vehicle to acquire minority interests in alternative asset managers. The Job: Building a target list of sovereign wealth funds and large family offices with a track record of direct minority investments in asset management firms, tiered by check size and sector fit.
The prompt
Using Dakota Marketplace, build me a target list for a $1.5B GP stakes fund acquiring minority interests in mid-market alternative asset managers. I need sovereign wealth funds and single-family offices with $10B+ in assets that have made a direct minority investment in an asset management firm or GP stakes vehicle in the past five years. For each name give me total AUM, typical direct investment check size, sector preferences within asset management, the most recent disclosed GP stakes or minority-interest commitment with target firm and size, and whether they invest directly or through an intermediary. Include the primary alternatives investment contact with full details. Tier into Tier 1 (recent GP stakes activity, check size fits), Tier 2 (broader alternatives mandate, no confirmed GP stakes deal), and Tier 3 (plausible but unconfirmed appetite). For each Tier 1 name draft a two-sentence opening angle referencing their specific prior minority investment.
5. The Head of Investor Relations Candidate Search Map
For: Senior Search Consultants running confidential Head of Investor Relations searches for private equity firms preparing to launch their next flagship fund. The Job: Mapping candidates currently running LP relationships at comparable-sized firms, cross-referenced against the client's target LP base for fit and potential relationship overlap.
The prompt
Using Dakota Marketplace, I'm running a confidential search for a Head of Investor Relations at a $5B buyout firm launching its fifth flagship fund. Identify individuals currently serving as Head of IR, Head of Capital Formation, or CFO with fundraising oversight at private equity firms with $3B to $8B in AUM who have led a fund close in the past three years. For each candidate, give me current firm, tenure in role, the size and close date of the most recent fund they led fundraising for, and a summary of the LP base they managed by type and geography. Flag any candidates whose current LP base has meaningful overlap with our target list. Tier into Tier 1 (strong track record and LP-base overlap), Tier 2 (strong track record, limited overlap), and Tier 3 (adjacent experience only). For each Tier 1 name draft a two-sentence outreach angle referencing their specific fund close.
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.
