Dakota Marketplace is the global private markets intelligence platform used by thousands of investment professionals to research LPs, GPs, and private companies — and the data source powering every prompt in this post. Built by fundraisers for fundraisers, Dakota Marketplace delivers complete, accurate, and daily-updated intelligence across every allocator channel — from family offices and RIAs to sovereign wealth funds and public pensions. 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 Mountain West & Plains Public Pension Prospect Map
For: Managing directors of business development at large-cap buyout funds prospecting into underworked geographies The Job: Identifying new-to-firm public pension prospects in the Mountain West and Plains states with large-cap buyout allocation and near-term meeting capacity
The prompt
Search Dakota Marketplace for public pension funds headquartered in Colorado, Utah, Wyoming, Montana, Idaho, North Dakota, South Dakota, Nebraska, and Kansas that allocate to large-cap buyout strategies. Filter for pensions with AUM over $500M and show me each fund's primary investment contact name, title, email, direct dial, current private equity allocation percentage, AUM, most recent new manager meeting date, and any notes on their consultant relationships. Highlight funds where the investment officer has been in their role for fewer than 18 months, as these represent the strongest new relationship opportunities. Sort by AUM descending.
2. The LP Co-Investment Candidate List for a Platform Acquisition
For: VP-level deal team members at lower middle market PE funds running a parallel co-investment process alongside a new platform acquisition The Job: Identifying existing LPs with active co-investment programs and documented industrial services appetite before Thursday's IC meeting
The prompt
I'm a VP on the deal team at a $1.2B lower middle market PE fund and we're closing a platform acquisition in industrial services. Using Dakota Marketplace, pull a list of our current LP base and identify which LPs have an active co-investment program with documented appetite in the $5M–$25M range. For each eligible LP, show their AUM, alternatives allocation, co-investment history (sector preferences and deal size), primary investment contact name and email, and any noted co-investment restrictions. Flag LPs that have co-invested with us before and any that have specifically expressed interest in industrial or business services. Return a ranked PDF of the top 15 co-investment candidates I can share with our CFO and deal team before Thursday's IC 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 Digital Health Follow-On Equity Investor Target List
For: Directors in healthcare investment banking building institutional investor coverage for a follow-on equity offering The Job: Identifying healthcare-focused institutional investors segmented by existing holders and new-to-name opportunities for a $300M digital health follow-on
The prompt
I'm a Director in healthcare investment banking advising on a $300M follow-on equity offering for a publicly-traded digital health company focused on ambulatory care software. Using Dakota Marketplace, identify institutional investors — large asset managers, healthcare-focused endowments, long-only equity funds, and healthcare system investment offices — with AUM over $500M that have an active allocation to healthcare or life sciences equities. Surface accounts where the portfolio manager or healthcare analyst name and direct contact are on file. Flag investors that have participated in comparable digital health or health IT equity raises in the last 24 months. Return a ranked target list of 40 priority accounts as a PDF, organized by tier: existing healthcare holders vs. new-to-name opportunities.
4. The Investment Management COO Candidate Universe
For: Managing directors in financial services executive search sourcing COO candidates from hedge funds, PE firms, and RIAs The Job: Mapping senior operations leaders with 4-plus years in role at AUM-qualified firms, with leadership change flags that signal near-term availability
The prompt
Using Dakota Marketplace, find senior operations leaders at investment management firms who fit this profile: (1) current title includes COO, Chief Operating Officer, Head of Operations, or Managing Director of Operations, (2) firm AUM between $1B and $20B, (3) firm is a hedge fund, private equity firm, or registered investment advisor, (4) individual has been in their current role for 4 or more years (potential openness to move). For each contact, return: full name, current title, firm name, firm AUM, firm strategy, direct email, LinkedIn URL if available, years in current role, and prior firm. Also flag any contacts whose firm has recently had a leadership change at the CEO or CIO level, as COO departures often follow within 12–18 months of a top-level transition.
5. The RIA & Family Office Portfolio Reporting Prospect List
For: VP-level sales professionals at portfolio monitoring and reporting SaaS platforms targeting underserved RIAs and family offices The Job: Building a prospect list of firms likely running on legacy or manual reporting infrastructure that are ready for a technology upgrade
The prompt
Search Dakota Marketplace for registered investment advisers and multi-family offices with AUM between $1B and $10B that currently lack a primary institutional portfolio reporting or performance attribution system, or are flagged as using legacy / manual processes. For each firm, return: firm name, city, total AUM, number of investment professionals, CTO or COO contact name and email where available, current technology stack or CRM noted in the system, and any recent firmographic changes (AUM growth, leadership change, new strategy launch) that signal readiness for a technology upgrade. Prioritize firms in the Northeast and Texas.
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.
