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 Private Credit Direct Lending RFP Manager Shortlist
For: Directors of Investments at corporate pension funds building a manager shortlist for an open private credit direct lending RFP. The Job: Screening direct lending managers by strategy fit, fund vintage, verified performance, and investment team stability — with key-person departures treated as a disqualifying filter before ranking.
The prompt
Our corporate pension fund has opened an RFP for a new private credit direct lending allocation and I need to build a manager shortlist. Using Dakota Marketplace, identify direct lending managers with $1B to $8B in AUM raising a current fund vintage, and pull each manager's strategy focus, fund size, AUM, most recently reported net IRR and TVPI if available, and any key-person departures or investment team turnover in the last 18 months. Exclude any manager where a lead portfolio manager or CIO has departed in that window. For each remaining manager, show firm name, strategy, current fund vintage, AUM, 3-year net IRR, team stability notes, and key relationship contact. Rank the list by performance and team stability, and return a PDF shortlist of the top 15 managers I can circulate to our investment committee ahead of the RFP deadline.
2. The Public Pension Growth Equity Roadshow Tearsheet Pack
For: Heads of Fundraising at growth equity funds preparing targeted public pension roadshows ahead of a Fund IV raise. The Job: Identifying public pensions with documented growth equity or venture allocations and recent commitment activity, packaged as a tearsheet pack organized by institution type for pre-roadshow review
The prompt
I'm raising capital for our Fund IV growth equity vehicle and want to prioritize public pension funds with $5B or more in AUM that have a documented growth equity or venture allocation, or that have increased their private equity target in the last 12 months. Using Dakota Marketplace, identify these institutions, and for each one include total AUM, current and target private equity and growth allocation percentages, most recent commitment to a growth equity or venture manager, key decision-maker contacts with title and email, and any board or investment committee commentary referencing growth equity as a priority. Package this as a tearsheet pack organized by institution type that I can review before our roadshow next month.
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 Pre-IPO PIPE and Crossover Investor Target List
For: VPs of Equity Capital Markets at regional investment banks preparing investor targeting lists for pre-IPO PIPE raises in enterprise software. The Job: Identifying crossover funds, long/short equity hedge funds, and large mutual fund complexes with documented late-stage private round and PIPE participation in SaaS companies above $300M ARR.
The prompt
I'm preparing an investor targeting list for a pre-IPO PIPE raise for a $400M revenue SaaS business planning to go public in Q3. Using Dakota Marketplace, identify crossover funds, hedge funds with long/short equity strategies, and large mutual fund complexes that have a track record of participating in late-stage private rounds and PIPE transactions in enterprise software. For each, include the fund name, AUM, primary investment contact with title and direct email, their known allocation to technology and software, and any documented investments in SaaS companies at or above $300M ARR in the last 24 months. Rank by fit and activity level and return a tiered target list of 50 investors.
4. The CIO Transition Map at Endowments and Foundations
For: Partners at private markets executive search firms identifying newly installed CIOs at endowments and foundations who are actively re-evaluating inherited manager relationships. The Job: Surfacing endowments and foundations where a CIO or Head of Investments transition has occurred in the past 12 months, prioritized by AUM and paired with PE and private credit allocation data for immediate context.
The prompt
Surface endowments and foundations where a CIO or Head of Investments transition has occurred in the past 12 months. For each, provide AUM, the new leader's name, prior firm, effective date, PE and private credit allocation percentages, and Dakota-verified contact information. Prioritize by AUM.
5. The Post-Fundraise GP Onboarding Pipeline
For: VPs of Sales at alternative investment data and analytics platforms targeting GPs scaling LP reporting and performance analytics infrastructure after a recent fund close. The Job: Identifying PE and VC managers that have closed a new fund in the last 18 months, flagging firms managing multiple active vintages simultaneously and those with rapid AUM growth as highest-priority technology upgrade prospects.
The prompt
Using Dakota Marketplace, identify PE and VC fund managers that have closed a new fund in the last 18 months with a fund size between $200M and $1.5B. For each firm, show: firm name, fund name, close date, fund size, estimated number of LPs, key operational contacts — CFO, COO, Head of Operations, Head of Finance — and total firm AUM across all active funds. Flag firms managing two or more active fund vintages simultaneously, as these GPs face the greatest reporting complexity and are most likely to be evaluating infrastructure upgrades. Also flag any firms that grew AUM by more than 50% in their most recent fund versus the prior vintage, as rapid growth typically triggers a technology review cycle.
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.
