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 Fund V Reinvestment-Timing Target List
For: Heads of Fundraising at mid-market buyout funds raising a Fund V, looking to prioritize LPs likely to have fresh capital freed up from maturing commitments elsewhere. The Job: Identifying endowments, foundations, and public pensions holding 2015-2017 vintage buyout commitments now approaching distribution years, ranked by near-term liquidity rather than AUM alone.
The prompt
Using Dakota Marketplace, identify endowments, foundations, and public pension plans with $1B to $10B in AUM that have existing commitments to buyout funds vintage 2015 to 2017, now approaching typical harvest and distribution years, in a similar size range to our Fund V. For each institution, show AUM, private equity allocation percentage, the maturing fund relationship and vintage, most recent commitment activity, and the best current contact with title and email. Rank by likely near-term liquidity and return a PDF reinvestment-timing target list of the top 15 LPs for our fundraising team to prioritize this quarter.
2. The Venture Fund II First-Close Tearsheet Pack
For: Directors of Investor Relations at early-stage venture funds pushing toward a Fund II first close. The Job: Profiling foundations and single-family offices with active venture allocations and recent Fund I or Fund II commitment history, surfaced with relationship warmth and co-investor overlap ahead of an LP roadshow.
The prompt
We're pushing toward a first close on Fund II and need tear sheets before our LP roadshow next week. Using Dakota Marketplace, identify foundations and single-family offices with $500M to $5B in AUM that have an active venture capital or early-stage allocation and have made at least one new fund commitment to a Fund I or Fund II vehicle in the past 24 months. For each institution, show AUM, venture and early-stage allocation percentage, most recent relevant commitment with manager, fund size, and date, the two best contacts with title and email, and notes on relationship warmth or any shared co-investor overlap with our existing LP base. Format as a tear sheet pack I can review with our GP team before the roadshow.
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 Regional Fund IV Re-Up Prioritization Pack
For: Managing Directors of Investor Relations at lower middle-market buyout firms building a regional Fund IV roadshow around LPs who backed a prior fund but haven't re-upped. The Job: Sequencing Midwest and Mountain West endowments, foundations, and public pensions by re-up probability, complete with the approval chain, the right opening angle for each institution, and any staffing or pacing-driven timing windows.
The prompt
Build me a tearsheet pack of Midwest and Mountain West endowments, foundations, and public pensions that already committed to one of our prior funds but have not yet re-upped, ahead of a regional Fund IV roadshow. Using Dakota Marketplace, give me six institutions, each on its own page. For each, show institution type and metro area, total AUM, private markets allocation as a percentage of the portfolio, and the most recent commitment on record including size and vintage year. Then give me the two contacts who actually control the re-up decision, with titles, tenure in seat, and where they sit in the approval chain. Then tell me two things I cannot get from a database alone: how to open the conversation given this LP's specific process and board or investment committee calendar, and the concrete reason this institution is more likely to re-up than a cold prospect of the same size. Sequence the six by re-up probability rather than by AUM, and flag any where staff turnover, a pacing study, or a manager roster review creates a timing window I need to hit.
4. The Faith-Based and Mission-Aligned Value Equity Target Map
For: Heads of Institutional Distribution at value equity boutiques whose concentrated, screenable mandate fits faith-based and mission-driven asset pools. The Job: Mapping denominational pension boards, diocesan offices, religious order endowments, and mission-driven foundations by exclusion criteria, manager turnover, and search activity to identify the institutions with the most immediate opening.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of faith-based and mission-aligned institutional investors in the United States with $250M to $5B in investable assets: denominational pension boards, diocesan and archdiocesan investment offices, religious order endowments, faith-affiliated hospital and university pools, and mission-driven foundations that apply values-based or socially responsible screens. For each institution give me total AUM, public equity allocation percentage and dollar amount, whether they run screened or unscreened mandates, their stated exclusion criteria, current external equity managers, their most recent manager hire or termination with date and size, average mandate size, minimum track record and firm AUM requirements, investing consultant or OCIO if any, and next investment committee meeting date. Include two named contacts per institution with titles, emails, and direct phone numbers, prioritizing the CIO or Director of Investments and whoever owns public equity manager selection. Rank the pack by fit with a concentrated 30-stock domestic value strategy that can accommodate custom exclusion lists, and tell me which three institutions have the most immediate opening based on recent turnover, a live search, or a stated intent to add screened equity managers.
5. The Health System Investment Office Discretion Map
For: Heads of Institutional Capital at private credit and real assets managers targeting U.S. health system investment offices and health-affiliated asset pools. The Job: Mapping where private markets discretion actually sits across health system pools, including OCIO relationships, consultant authority, and any captive or opportunistic sub-pools with separate approval paths, paired with an approach angle for each.
The prompt
Using Dakota Marketplace, build me a tearsheet pack profiling U.S. health system investment offices and health-affiliated asset pools with $2B or more in total assets and an existing private markets allocation. For each institution return total assets under management, current private markets allocation percentage against policy target, the date and strategy of the most recent private markets commitment, whether the pool is internally managed or run by an OCIO and which firm, the consultant of record and their level of discretion, the two most relevant decision-makers with titles and direct contact details, any values-based or faith-based screening policy that would apply, and any distinct captive insurance, foundation, or opportunistic sub-pool with separate discretion. Then tell me for each institution how to approach them and what the specific opening is.
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.
