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 Taft-Hartley Pension Core Infrastructure Tearsheet Pack
For: Heads of Fundraising at core infrastructure equity fund managers building a first-close roadshow target list among Taft-Hartley multi-employer pension plans. The Job: Filtering Taft-Hartley plans with active real assets or infrastructure allocation sleeves, ranked by likelihood to commit to a core infrastructure equity strategy, with active RFP flags and meeting history included.
The prompt
In Dakota Marketplace, filter allocator accounts to Taft-Hartley and multi-employer pension plans with a stated real assets or infrastructure allocation sleeve and total plan assets between $500M and $5B. For each, pull the current infrastructure and real assets allocation percentage, most recent GP commitment, the primary and secondary investment staff contacts, and any meeting history our firm has logged with them. Build a tearsheet-ready shortlist of the top 8 plans ranked by likelihood to commit to a core infrastructure equity strategy, and note any that have an active RFP or manager search open in this space.
2. The Healthcare Growth Equity Fund III First-Close Tearsheet Pack
For: Heads of Institutional Relationships at healthcare-focused growth equity firms preparing a targeted tearsheet pack six weeks from a first close. The Job: Profiling six public pensions, endowments, and foundations with documented growth equity or healthcare-sector allocations and recent relevant commitments, paired with relationship warmth notes and consultant gatekeeper context for pre-roadshow review.
The prompt
We're six weeks from a first close on a $900M healthcare-focused growth equity Fund III. Using Dakota Marketplace, build a tearsheet pack of 6 public pensions, endowments, and foundations with a documented growth equity or healthcare-sector allocation and at least one growth equity commitment in the past 30 months. For each institution, include AUM, allocation percentage to growth equity or healthcare strategies, most recent relevant commitment with manager, size, and date, the two best contacts with title and email, and notes on relationship warmth and any consultant gatekeeper. Format as a tearsheet pack I can review with my team before our roadshow kicks off next week.
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 Consumer Technology Fund II Family Office and Fund-of-Funds Tearsheet Pack
For: Directors of Fundraising at venture capital funds raising a Fund II in consumer technology, targeting family offices and funds-of-funds with emerging manager commitment history. The Job: Profiling six family offices and funds-of-funds with documented venture or early-stage allocations and first-time or Fund II manager commitment history, filtered to exclude existing firm relationships, with gatekeeper context for roadshow scheduling.
The prompt
We're two months from a first close on our $250M consumer technology Fund II. Using Dakota Marketplace, build a tearsheet pack of 6 family offices and funds-of-funds with a documented venture or early-stage allocation, a history of committing to first-time or Fund II managers, and no existing relationship with our firm. For each institution, include AUM, venture and early-stage allocation percentage, most recent relevant commitment with manager, sector, and date, the two best contacts with title and email, and notes on relationship warmth and any consultant or gatekeeper involved. Format as a tearsheet pack I can review with our GP before we finalize the roadshow schedule next week.
4. The Direct Lending Fund III Public Pension Pipeline Tearsheet Pack
For: Heads of Fundraising at lower middle market direct lending funds refreshing their pipeline ahead of a Fund III launch. The Job: Profiling six public pension plans that have increased their private credit or direct lending target allocation in the last 12 months and made at least one direct lending commitment in that window, with relationship warmth and consultant gatekeeper notes for pre-launch pipeline calls.
The prompt
We're refreshing our fund page and pipeline before launching Fund III at $750M. Using Dakota Marketplace, build a tearsheet pack of 6 public pension plans that have increased their private credit or direct lending target allocation percentage in the last 12 months and have made at least one direct lending commitment in that window. For each plan, include AUM, allocation percentage to private credit, most recent relevant commitment with manager, size, and date, the two best contacts with title and email, and notes on relationship warmth and any consultant gatekeeper involved. Format as a tearsheet pack I can review with my investment team before our refreshed pipeline call next week.
5. The Infrastructure Debt Roadshow LP Tearsheet Pack
For: Heads of Institutional Sales at real assets and infrastructure managers launching a new infrastructure debt fund and preparing a targeted pre-roadshow prospect pack. The Job: Identifying endowments, foundations, and public pensions that have increased their real assets or infrastructure allocation target in the past 18 months and have not yet committed to the current fund family, ranked by allocation increase and proximity to next investment committee meeting.
The prompt
We're launching a $750M infrastructure debt fund and I want tearsheets before our roadshow. Using Dakota Marketplace, identify endowments, foundations, and public pension plans with $2B to $15B in AUM that have increased their real assets or infrastructure allocation target in the past 18 months and have not yet committed to our current fund family. For each institution, show AUM, current real assets and infrastructure allocation percentage, most recent related commitment with manager and size, and the two most relevant investment contacts with title and email. Rank by allocation increase and proximity to their next investment committee meeting, and return a PDF tearsheet pack profiling the top 6 targets for our roadshow.
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.
