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 CIO Roster Reset Tearsheet Pack
For: Managing Partners at lower middle-market buyout firms targeting institutional allocators where a newly seated CIO is actively reshaping inherited manager rosters. The Job: Building a tearsheet pack of six endowments, foundations, and public pensions that have hired a new CIO or built out an internal investment office in the last 24 months, with a tailored approach note for each account based on the new CIO's background and the incumbent manager roster.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of six institutional allocators — university endowments, private and community foundations, and public pension plans — between $500M and $4B in AUM that have hired a new Chief Investment Officer or built out an internal investment office within the last 24 months, and that allocate to lower middle-market private equity. For each institution give me: institution type and city and state, total AUM, private equity target and current allocation, the most recent private equity commitment with manager, vehicle, size, and date, the new CIO's name, start date, and prior employer, plus one additional manager-research or private markets contact with title and email. Then write a short recommended approach for each — how to frame a $600M Fund IV lower middle-market buyout strategy given the CIO's background and prior portfolio, what we should lead with, and what to avoid — and note our edge versus the incumbent managers already on their roster.
2. The Co-Investment Program Expansion Tearsheet Pack
For: Heads of Capital Formation at mid-market buyout funds building a co-investment sleeve alongside a primary fund raise. The Job: Identifying endowments, foundations, and public pensions that have formalized or expanded a co-investment program in the last 24 months, with decision authority thresholds, meeting cadence, and per-deal capacity disclosed for each account.
The prompt
Using Dakota Marketplace, build tearsheets on endowments, foundations and public pensions that have formalized or expanded a co-investment program in the last 24 months. For each institution include total AUM, private equity target allocation, the most recent disclosed co-investment commitment with manager and date, whether co-investment authority is delegated to staff or requires board or IC approval and at what dollar threshold, board and IC meeting cadence, the two staff members who evaluate co-investment opportunities with titles, emails and phone numbers, and any public disclosure of co-investment pacing or per-deal capacity. Prioritize institutions with existing buyout exposure and no current relationship with our firm, and note for each whether a consultant or OCIO sits between staff and the decision.
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 First-Time Fund Emerging Manager Program Target List
For: Managing Partners at first-time growth equity funds building a tiered LP target list sized to a $250M first close. The Job: Identifying endowments, foundations, and public pensions with documented emerging manager or first-time fund programs whose minimum and maximum check sizes fit a $250M first close, ranked by commitment size fit and filtered to exclude oversized minimums.
The prompt
I am raising a $250M first institutional growth equity fund and need a tiered target list of endowments, foundations, and public pensions with documented emerging-manager or first-time-fund programs. For each institution, give me total AUM, private markets allocation percentage, the most recent private markets commitment on record, the two people who actually control manager selection with titles and contact detail, whether the plan has committed to a Fund I in the last three years, and the minimum and maximum check size their program supports. Exclude anyone whose minimum check exceeds 20% of my $250M target and rank the remainder by how well their typical commitment size fits a $250M first close.
4. The Pension Consultant Relationship Mapping Report
For: Directors of Capital Formation at established private equity funds building a systematic consultant relations program to unlock multiple LP relationships simultaneously. The Job: Mapping which investment consultants advise the highest concentration of target pension and endowment LPs in a fund's sweet spot, with approved manager list status and primary consultant contact flagged for each relationship.
The prompt
I run capital formation at a $1.8B lower middle-market buyout fund and want to build a systematic consultant relations program. Using Dakota Marketplace, identify investment consultants — NEPC, Verus, Mercer, Cambridge Associates, Wilshire, Aon, and others — whose client rosters have the highest overlap with pension funds and endowments between $500M and $5B in AUM that allocate to lower middle-market buyout strategies. For each consultant, show the number of relevant client relationships on their roster, the names of those clients, whether our fund currently appears on their approved manager list, the primary consultant contact responsible for private equity manager research with title and direct email, and any recent additions or removals from their approved list in our strategy category. Rank consultants by the number of target LP relationships they influence and return a prioritized outreach plan I can execute over the next two quarters.
5. The Allocation Undershoot Recovery Prospect List
For: Heads of Fundraising at private equity funds targeting LPs that are currently running below their stated private equity allocation target and have dry powder to deploy. The Job: Identifying public pensions, corporate pensions, and endowments where current PE allocation is more than 200 basis points below stated target, signaling active deployment pressure and near-term commitment capacity for a new manager relationship.
The prompt
I'm raising a $1.2B mid-market private equity fund and want to focus my near-term outreach on LPs that have active deployment pressure — specifically institutions where current private equity allocation is running more than 200 basis points below their stated target. Using Dakota Marketplace, identify public pensions, corporate pensions, and endowments with AUM between $2B and $15B where this gap exists. For each institution, show total AUM, stated PE target allocation, current PE allocation, the gap in basis points, most recent PE commitment with manager and date, and the two key investment contacts with title and direct email. Rank by allocation gap size descending — the widest gaps represent the most urgent deployment mandates — and return a prioritized prospect list of the top 25 institutions I can contact this month.
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.
