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 Pre-Marketing LP Re-Engagement List
For: VPs of institutional sales at alternatives firms preparing to launch a new fund after a recent close The Job: Identifying prior fund LPs who have gone dark and flagging accounts where leadership has changed before pre-marketing begins
The prompt
I'm a VP of Institutional Sales at a $6B alternatives firm. Our most recent buyout fund closed 14 months ago and we begin pre-marketing our next vehicle in Q3. Using Dakota Marketplace, identify all LPs in our database who committed to our prior two funds but haven't been contacted in the last 120 days. For each, show AUM, current alternatives allocation percentage, most recent commitment to any manager, primary decision-maker contact, and a recommended re-engagement talking point based on their stated investment priorities. Flag any accounts where the CIO or head of alternatives has changed in the last 12 months — those need relationship resets. Return a ranked re-engagement list as a PDF.
2. The Behavioral Health Add-On Target Map
For: VP-level deal team members at PE firms building out platform companies through add-on acquisitions The Job: Identifying sponsor-backed behavioral health targets in the Southeast within the right revenue range for a platform add-on
The prompt
I'm a VP at a PE firm and we own a behavioral health services platform in the Southeast. I need to identify add-on acquisition targets. Using Dakota Marketplace, find all sponsor-backed behavioral health, mental health, or substance use treatment companies in the Southeast with estimated revenue between $10M and $75M. Include the sponsor name, year of investment, company description, CEO name and contact info, and any known transaction history.
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 Real Estate Debt Fund LP Prospect List
For: Real estate capital markets directors at investment banks advising on LP raises for commercial real estate debt funds The Job: Identifying institutional allocators with active real estate debt mandates and recent commitment history for a $2B CRE debt fund raise
The prompt
I run real estate capital markets at a mid-size investment bank advising a $2B commercial real estate debt fund on its LP raise. Using Dakota Marketplace, identify institutional allocators — insurance companies, corporate pensions, and family offices with AUM over $1B — that have an active real estate debt or commercial mortgage allocation and made a new real estate credit commitment in the last 24 months. Include the head of real assets or fixed income contact at each institution, their AUM, typical commitment size, and preferred fund structure (closed-end vs. evergreen). Prioritize accounts with no existing relationship with this manager and deliver a ranked prospect list as a PDF.
4. The CCO Candidate Universe for a Private Markets RIA
For: VP-level financial services executive search professionals leading compliance officer searches at registered investment advisers The Job: Building a candidate universe of CCOs with SEC RIA experience and illiquid asset compliance exposure for a growing private markets platform
The prompt
I'm leading a CCO search for a $4B RIA expanding its private markets platform. The firm is adding a new private credit fund and needs a CCO with deep SEC RIA experience and working knowledge of illiquid asset compliance. Using Dakota Marketplace, find all Chief Compliance Officers, Heads of Compliance, and Deputy CCOs currently at registered investment advisers with AUM between $1B and $15B in the Northeast and Mid-Atlantic that manage both liquid and alternative strategies. Include name, current firm, AUM, years in role, and direct contact info. Flag individuals at firms that recently launched a new private fund or navigated an SEC examination — those have the exact compliance complexity my client faces. Return a ranked PDF of the top 20 candidates.
5. The Consultant Universe Map for a Fixed Income RFP
For: Deputy CIOs at large public pensions preparing to issue a manager RFP and needing to understand the consultant landscape first The Job: Mapping OCIOs and investment consultants serving public pensions in the South and Southeast with fixed income expertise before an RFP is issued
The prompt
We're a $20B state pension preparing to issue an RFP for a new global fixed income manager. Using Dakota Marketplace, find all OCIOs and investment consultants that serve public pension funds in the South and Southeast with at least $1B in advised assets. Show me the key consultant contact, their client pension focus, AUM advised, and any specialty in fixed income or liability-driven investing. I want to know which consultants already have strong fixed income manager relationships before we issue the RFP.
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.
