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 New-Leadership Lower Middle-Market Tearsheet Pack
For: Partners and Heads of Investor Relations at lower middle-market buyout managers rebuilding institutional relationships around leadership turnover. The Job: Building a tearsheet pack of endowments, foundations, and public pensions that appointed a new CIO or Head of Private Markets in the last 18 months, when the manager roster gets rebuilt and new relationships actually get added.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of 8 US endowments, foundations and public pension plans that have appointed a new Chief Investment Officer or Head of Private Markets within the last 18 months and allocate to lower middle-market private equity. For each institution give me: full name, institution type, city and state, total AUM, private equity target and actual allocation, the most recent private equity commitment with manager name, fund, size and date, the new leader's name, start date and prior employer, one additional private markets contact with title and email, any consultant or OCIO relationship, and our full meeting and contact history. Then for each institution write a two-sentence APPROACH note on how to open the conversation given the leadership change, and a one-sentence EDGE note on why our strategy fits what that leader built at their prior institution. Order the pack from most recent appointment to least recent, and flag any institution where the private equity allocation is more than 200 basis points below target.
2. The Growth Equity Re-Underwriting Window Tearsheet Pack
For: Managing Partners and Heads of Capital Formation at growth equity firms timing outreach to the narrow window after an LP changes investment leadership. The Job: Building a tearsheet pack of endowments, foundations, and public pensions that appointed a new CIO, Deputy CIO, or Head of Private Markets in the last eighteen months, the window in which incumbent manager lineups get re-underwritten and new relationships are genuinely winnable.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of U.S. endowments, foundations, and public pension plans between $800M and $12B in assets that have appointed a new Chief Investment Officer, Deputy CIO, or Head of Private Markets within the last eighteen months. For each institution, give me a full profile: total assets, current private equity and growth equity target versus actual allocation and the resulting over- or under-weight, the new investment leader's name, start date, prior institution and the manager relationships they built there, the private markets team members beneath them with direct contact details, the plan's investment consultant or OCIO and whether that relationship has also changed, their most recent private markets commitments with manager names, sizes, and dates, their stated minimum and maximum commitment sizes, whether they will back a Fund III or a manager under $3B, their board or investment committee meeting calendar for the next two quarters, and any documented emerging-manager or first-time-fund program. Prioritize institutions where the incoming leader previously allocated to growth equity managers of our size, since that is the strongest predictor of receptivity. For each tearsheet, close with a recommended approach: who to contact first, what to reference from their new leader's prior allocation history, and the ideal timing relative to their next committee meeting.
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 General Counsel Candidate Slate for a Multi-Strategy Fund Manager
For: Principals in the legal and compliance practice of executive search firms running General Counsel searches for private fund managers.The Job: Building a candidate slate of legal leaders with fund formation and regulatory experience relevant to the client's structure, screened against known conflicts with the client's existing counsel relationships.
The prompt
Using Dakota Marketplace, I'm running a confidential search for a General Counsel at a $4B multi-strategy private fund manager expanding into a new fund structure. Identify individuals currently serving as General Counsel, Deputy General Counsel, or Chief Legal Officer at private equity, private credit, or hedge fund managers with $2B to $8B in AUM who have overseen a new fund launch or structure change in the past three years. For each candidate, give me current firm, tenure in role, prior roles, the firm's AUM and number of fund vehicles, and any public record of regulatory matters handled during their tenure. Flag candidates whose current firm uses outside counsel that would conflict with our existing relationships. Tier into Tier 1 (direct fund-formation experience at a comparable structure), Tier 2 (relevant background, different fund type), and Tier 3 (adjacent experience only). For each Tier 1 name draft a two-sentence outreach angle referencing their specific fund launch experience.
4. The RIA Alts Distribution Platform Prospecting List
For: Business Development Directors at wealth management technology platforms selling alternatives distribution infrastructure to RIAs and independent broker-dealers. The Job: Identifying RIAs and broker-dealers building out a private markets allocation program for their clients, tiered by how far along they are in standing up the infrastructure.
The prompt
Using Dakota Marketplace, I'm prospecting for our alternatives distribution platform. Identify U.S. RIAs and independent broker-dealers with $2B+ in client assets that have added a private equity, private credit, or real assets fund to their approved product list in the past 18 months, or have hired a dedicated alternatives research or due diligence lead. For each firm, give me total client assets, number of alternative products currently approved, the most recent fund added with manager name and strategy, the alternatives lead's name and tenure, and any disclosed custodian or subscription-processing partner. Tier into Tier 1 (recent product additions, no disclosed distribution infrastructure partner), Tier 2 (building the program, partner unclear), and Tier 3 (early-stage, no dedicated lead yet). For each Tier 1 firm draft a two-sentence opening line referencing their specific recent fund addition.
5. The Institutional Secondaries Seller Outreach List
For: Directors of Origination at secondaries buyers sourcing LP-led portfolio sales directly from institutional sellers. The Job: Identifying endowments, foundations, and pensions showing signals of an active or upcoming private equity portfolio sale, tiered by likelihood of a near-term transaction.
The prompt
Using Dakota Marketplace, build me an outreach list of U.S. endowments, foundations, and public pensions that show signals of a potential LP-led secondaries sale: a private equity allocation more than 300 basis points over target, a recently reduced target allocation, or a new CIO with a documented history of using secondaries sales to rebalance a portfolio. For each institution, give me total assets, current versus target private equity allocation, the private equity fund vintages and vintage concentration on record, the investment consultant or OCIO of record, and the lead private markets contact with details. Flag any institution where the new investment leader executed a secondaries sale at their prior institution. Tier into Tier 1 (over-allocated plus a leader with secondaries-sale history), Tier 2 (over-allocated, no confirmed history), and Tier 3 (mild overweight, worth monitoring). For each Tier 1 name draft a two-sentence opening angle referencing their specific allocation imbalance.
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.
