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 Sponsor-Backed Software M&A Target Screen
For: Directors of M&A Advisory at bulge bracket banks covering software and technology transactions for PE clients. The Job: Identifying PE-sponsored vertical market software companies in North America that are ripe for a sale or add-on — filtered by hold period, revenue band, and recent inactivity
The prompt
I'm an M&A advisor at a bulge bracket bank covering software and technology. A PE client wants to build in vertical market software — targeting founder-led or PE-owned SMB SaaS businesses with $10M–$50M ARR. Using Dakota Marketplace, identify all PE-sponsored vertical market software companies in North America with estimated revenue between $15M and $75M that have been owned by a sponsor for 3+ years and haven't completed an add-on in the last 18 months. Include the sponsor name, fund vintage, current CEO and CFO, estimated EBITDA if available, and flag any companies with recent Form D activity or advisory mandates. Return a ranked list of the top 25 priority targets.
2. The Peer Allocator Cross-Reference for Manager Vetting
For: Senior Investment Analysts at endowments conducting due diligence on emerging and first-time fund managers before an investment committee decision. The Job: Mapping which peer endowments and foundations have already backed a first-time VC manager — surfacing warm references and building a call list before committing capital
The prompt
We're doing due diligence on a first-time fund manager raising a $300M VC fund focused on enterprise software. Using Dakota Marketplace, find all endowments and foundations with AUM over $500M that have made venture capital commitments in the last three years. For each, show the primary investment contact, AUM, and any notes on VC mandate or first-time fund appetite so I can identify which peers may already know this manager and who to call for a reference check.
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 Portco Operating Benchmark & Talent Map
For: Portfolio Managers and Operating Partners at large-cap buyout firms managing consumer brand portfolios post-acquisition. The Job: Benchmarking portfolio companies against peer PE-backed consumer platforms on key operating metrics — and simultaneously mapping recently available senior operator talent for board and operating roles
The prompt
I'm a Portfolio Manager / Operating Partner at KKR overseeing our consumer portfolio. Using Dakota Marketplace, benchmark our portfolio companies against peer consumer brands on growth, margin, and leverage. Also map senior operator talent — CEO, CFO, CMO, CRO — who have exited comparable PE-backed consumer platforms in the last 24 months and could be candidates for board or operating roles. Deliver as a PDF.
4. The Manual Reporting Pain Point Prospect List
For: Regional Sales Managers at LP portfolio monitoring and reporting software firms targeting institutional allocators with high PE commitment volume. The Job: Identifying pension funds, endowments, and foundations whose private equity activity suggests manual reporting strain — and surfacing the right operational and investment contacts to open a conversation
The prompt
I sell LP portfolio monitoring and reporting software to institutional allocators. Using Dakota Marketplace, find all pension funds, endowments, and foundations with AUM over $1B that have made five or more private equity commitments in the last three years. Show me the Director of Investments, CIO, or Head of Operations at each account, plus their contact info. I want to prioritize accounts where manual LP reporting is a likely pain point given the volume of PE relationships.
5. The Sovereign Wealth & Government Fund Infrastructure Target List
For: Managing Directors of Institutional Sales at large infrastructure equity managers launching non-U.S. capital raises for new flagship funds. The Job: Building a prioritized global outreach list of sovereign wealth funds and government pension reserves with active infrastructure programs — filtered by AUM, recent commitment activity, and co-investment appetite
The prompt
I run institutional sales for a $15B infrastructure equity manager and we are launching a $4B infrastructure fund targeting non-U.S. capital. Using Dakota Marketplace, identify sovereign wealth funds and government pension reserves globally — including Middle East SWFs, Nordic pension funds, and Asian government reserves — with an existing infrastructure allocation and AUM above $10B. For each, show the infrastructure team lead or head of real assets, their historical infrastructure commitment sizes, preferred co-investment appetite, and geographic focus. Flag any that have made a new infrastructure commitment in the last 18 months and prioritize the top 20 for first outreach.
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.
