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 London & Zurich Roadshow Tearsheet Pack
For: VPs of international distribution at direct lending funds preparing a two-city European roadshow The Job: Building tearsheets on non-U.S. sovereign wealth funds and international public pensions with private credit allocations before the flight to London
The prompt
I'm planning a two-city roadshow in London and Zurich for our $6B direct lending fund's next vintage. Using Dakota Marketplace, identify the top 6 non-U.S. sovereign wealth funds and international public pension funds with $10B or more in AUM that have a documented allocation to private credit or direct lending. For each institution, include total AUM, current private credit allocation percentage and target, most recent manager commitment in the space, key decision-maker contacts with title and email, and any notes on their U.S. manager relationships. Package this as a tearsheet pack organized by institution type that I can review on the flight to London.
2. The New Investment Leadership Tearsheet Pack
For: Heads of capital formation at growth equity funds running a targeted outreach campaign ahead of a fund close The Job: Building tearsheets on institutions that recently appointed new CIOs or heads of alternatives — the best window for a first manager meeting
The prompt
I'm building a targeted outreach campaign ahead of our Fund III close. Using Dakota Marketplace, identify endowments, foundations, and public pensions with $1B or more in AUM that have appointed a new CIO, Head of Alternatives, or Director of Investments within the last 6 months. New investment leadership often triggers a full manager roster review, which is our best window for a first meeting. For each institution, include total AUM, current alternatives/private equity allocation and target, the new leader's name, title, start date, and prior employer, and any documented manager search or RFP activity since their arrival. Package this as a tearsheet pack organized by institution type that I can review before our outreach sprint kicks off.
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 Hedge Fund Peer Benchmark & Manager Concentration Review
For: Heads of alternatives at single family offices reviewing hedge fund manager concentration against peers The Job: Mapping hedge fund strategy preferences and manager rosters at peer SFOs and endowments to identify gaps and flag managers losing traction
The prompt
I manage alternatives for a $2B single family office with current hedge fund exposure concentrated in multi-strategy and long/short equity. I want to understand how our current manager roster compares to what similar-sized family offices and endowments are holding. Using Dakota Marketplace, pull a list of single family offices and endowments with $1B–$5B AUM that have documented hedge fund allocations. For each, identify which strategies they favor (long/short, macro, event-driven, multi-strategy), their known manager names if available, and their approximate hedge fund allocation as a percentage of total AUM. Then identify any hedge fund managers that appear frequently in peer portfolios that we do not currently hold, and flag any managers in our current book that appear to be losing traction based on recent redemption activity or reduced new commitments from peers.
4. The Peer Public Pension Asset Allocation Benchmark
For: Senior investment officers at state public pension funds benchmarking their alternatives strategy against peers The Job: Mapping target and actual allocations across 15–20 peer plans and flagging managers appearing in four or more peer portfolios not yet evaluated internally
The prompt
Using Dakota Marketplace, show me the current asset allocation strategies of 15–20 peer public pension funds with total AUM between $5B and $15B. For each peer plan, include: total AUM, current target and actual allocation percentages across public equity, fixed income, private equity, real assets, hedge funds, and other alternatives, named external managers in the private markets categories, and any recent changes to their investment policy statement or target allocations. Highlight any external managers that appear in 4 or more peer portfolios that our fund has not yet evaluated — these are candidates for our next manager review cycle. Flag any peer plans that have increased their total private markets allocation above 35% in the last two fiscal years, and note which asset classes drove that increase so I can benchmark our own alternatives strategy.
5. The Insurance Channel Structured Credit Tearsheet Pack
For: Heads of institutional sales at structured credit-focused private credit managers raising from insurance company general accounts The Job: Building tearsheets on insurance GAs increasing structured credit allocations or running an open manager search before the insurance-channel roadshow
The prompt
I'm raising capital from insurance company general accounts for our next structured credit vintage. Using Dakota Marketplace, identify insurance companies with $5B or more in general account assets that have increased their structured credit or private credit allocation in the last 18 months or have an open manager search in the space. For each institution, include total general account AUM, current and target structured credit allocation percentages, most recent commitment to a structured credit manager, names and titles of the relevant investment staff contacts, and any recent board or investment committee commentary on credit strategy. Package this as a tearsheet pack organized by institution type that I can review before our insurance-channel roadshow.
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.
