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 Infrastructure Debt Roadshow Tearsheet Pack
For: Heads of institutional sales at infrastructure debt funds prioritizing outreach ahead of a fund close The Job: Building tearsheets on Taft-Hartley and public pension plans with open or recently increased infrastructure debt allocations before the roadshow kicks off
The prompt
I'm raising capital for our infrastructure debt fund's next vintage and want to prioritize Taft-Hartley multiemployer pension funds and public pension funds with $2B or more in AUM. Using Dakota Marketplace, identify plans that have a documented allocation to infrastructure debt or real assets credit, or that have increased their infrastructure allocation target in the last 12 months. For each institution, include total AUM, current and target infrastructure allocation percentages, most recent commitment to an infrastructure or real assets credit manager, key decision-maker contacts with title and email, and any board or investment committee commentary referencing infrastructure as a priority. Package this as a tearsheet pack organized by institution type that I can review before our fund's roadshow kickoff next week.
2. The Insurance Investor Conference Tearsheet Pack
For: Heads of insurance channel sales at structured credit managers attending an insurance asset management conference The Job: Building tearsheets on insurance general accounts with documented structured credit and private placement allocations before the conference
The prompt
I'm attending an insurance asset management conference next week and want tearsheets ready beforehand. Using Dakota Marketplace, build a tearsheet for each of the top insurance company general accounts with $5B or more in invested assets that have a documented allocation to structured credit, private placements, or asset-based finance. For each account, include total invested assets, current fixed income/structured credit allocation percentage, most recent external manager commitment in that space, the name and title of the CIO or Head of Investments, and any recent notes on manager searches. Package this as a tearsheet pack I can hand out at the conference.
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 Private Credit LP Expansion Tracker
For: Directors of placement agent services at investment banks raising on behalf of direct lending fund clients The Job: Identifying institutional allocators actively expanding private credit exposure with upcoming review windows and verified commitment history
The prompt
Using Dakota Marketplace, identify institutional allocators — including public pension funds, corporate pensions, endowments, and insurance companies — actively expanding their private credit or direct lending allocations. For each, show current private credit allocation versus target, recent private credit commitments in the last 18 months, key contacts, and upcoming review windows. Filter for AUM above $500M.
4. The GP Fund Accounting Upgrade Prospect List
For: VPs of sales at fund accounting and portfolio analytics software companies targeting scaling GPs The Job: Identifying PE, VC, and private credit managers launching second and third funds that are outgrowing manual processes
The prompt
I sell fund accounting and portfolio analytics software to GPs in the $500M–$5B AUM range who are scaling beyond Excel and legacy systems. Using Dakota Marketplace, identify PE, VC, and private credit fund managers who: (1) have launched their second or third fund in the last 3 years, signaling growth and operational complexity, (2) have AUM between $500M and $5B, (3) have 5 or more active portfolio companies or investment strategies, and (4) are not already known users of institutional-grade fund administration software. For each, provide the firm name, AUM, fund count, primary operations or CFO contact with title and direct email, and any recent fundraising activity that suggests imminent operational scaling needs. Return a prioritized list of 40 prospects.
5. The Private Credit Due Diligence Roadshow Tearsheet Pack
For: Heads of consultant relations at private credit funds preparing for a due diligence roadshow The Job: Building tearsheets on endowments, foundations, and public pensions with recent private credit manager searches or active commitments before each meeting
The prompt
I'm meeting with a mix of endowments, foundations, and public pension plans next month as part of our private credit fund's due diligence roadshow. Using Dakota Marketplace, identify institutions with $1B–$8B in AUM that have made a new commitment to a private credit or direct lending manager in the last 18 months, or that have an active manager search open in the space. For each institution, include AUM, current private credit/alternatives allocation percentage, most recent manager commitment, and the CIO or key allocator contact with title and email. Package this as a tearsheet pack organized by institution type that I can review before each meeting.
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.
