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 Private Credit LP Pipeline Builder
For: VPs of investor relations at credit-focused private debt funds building a prospect pipeline of public pensions and insurance companies The Job: Identifying allocators actively reviewing or expanding private credit allocations with verified commitment history and decision-maker contacts
The prompt
Using Dakota Marketplace, build a pipeline of public pension plans and insurance companies actively reviewing or expanding their private credit allocations. Filter for AUM above $1B. For each, show current private credit allocation targets, direct lending commitments in the last 24 months, key decision-makers, and any publicly stated allocation intent from investment committee minutes or trustee reports.
2. The Succession-Ready Family Business Origination List
For: Senior associates at lower middle market PE funds targeting founder-owned and family-owned businesses approaching a generational transition The Job: Identifying privately held business services and light industrial companies in the Southeast and Mid-Atlantic where long-tenured founders signal succession pressure and motivated seller dynamics
The prompt
I'm on the deal team at a lower middle market PE fund focused on business services and light industrials. We specifically target founder-owned or family-owned businesses where the founder is approaching retirement or the family is in a generational transition — these are the most motivated sellers and best candidates for our first institutional capital model. Using Dakota Marketplace's company database, identify privately held business services or light industrial companies in the Southeast and Mid-Atlantic that: (1) were founded more than 25 years ago, (2) have estimated revenue between $10M and $75M, (3) are NOT currently sponsor-backed, (4) have a CEO or founder who has been in their role for 15 or more years — suggesting potential succession pressure, and (5) are headquartered in NC, SC, VA, TN, GA, FL, MD, or PA. For each company, return: company name, state, industry subsector, estimated revenue, CEO/founder name and contact, years in role, any recent financing or transaction activity, and any known private equity interest. Flag companies where the founder has been in role for 20+ years as highest priority. Return a ranked PDF of the top 30 targets with a brief sourcing rationale for each.
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 IR Career History Map & Warm Introduction Path Finder
For: Partners at executive search firms conducting retained IR searches at credit-focused alternative asset managers The Job: Mapping career trajectories of current IR professionals at credit managers, flagging warm introduction paths through shared LP relationships and alumni connections to the client firm
The prompt
Using Dakota Marketplace career history data, I am conducting a retained search for a Head of Investor Relations at a $3B credit-focused alternative asset manager. Map the career trajectories of current IR professionals at credit-focused managers (direct lending, CLOs, distressed, special situations) with $1B–$10B AUM. For each candidate, show: current employer and title, previous firms and tenure at each, total years in IR roles, and whether they have moved roles in the last 18 months. Additionally, flag any individuals whose career history includes time at firms that are current LPs in my client's existing funds — these represent warm introduction paths. Finally, identify any alumni connections between candidate pool members and my client firm's current leadership team based on shared prior employers, and highlight those candidates as highest-priority outreach targets.
4. The LP Reporting SaaS Prospect List for Scaling GPs
For: Enterprise account executives selling cloud-based LP communications and investor reporting platforms to PE and VC fund managers The Job: Identifying GPs managing three or more active funds that are still running manual reporting workflows and showing capacity strain signals
The prompt
I sell a cloud-based LP communications and investor reporting platform to private equity and venture capital fund managers. My ideal customer is a GP that has grown past 3 funds under management and is still using Excel, email, and PDF attachments to deliver quarterly reports and capital call notices — a clear signal they need a modern reporting infrastructure. Using Dakota Marketplace, identify registered investment advisers that manage private equity or venture capital strategies, have AUM between $300M and $3B, manage at least 3 active fund vehicles simultaneously, and have between 10 and 75 employees. Filter for firms where the Form ADV shows growth — at least two new funds filed in the last 4 years. For each firm, show: firm name, AUM, number of active funds on record, employee count, headquarters city and state, key operations or technology contact (COO, CFO, or Head of Operations) with title and email if available, and any known existing technology vendors or CRM systems. Flag firms where a new fund was closed in the last 12 months and the employee count has NOT grown proportionally — those firms are likely under capacity strain and most receptive to automation. Return a ranked PDF of the top 30 prospects.
5. The New CIO Alternatives Portfolio Audit
For: Newly appointed CIOs at community foundations conducting a full alternatives manager roster audit before their first investment committee meeting The Job: Benchmarking eight existing managers against vintage-year peers, surfacing peer foundation allocation patterns, and flagging concentration risk and fee outliers for the board
The prompt
I was just named CIO of an $800M community foundation with a 20% target allocation to alternatives. My board has asked me to conduct a full audit of our existing alternatives manager roster before my first investment committee meeting. We currently have commitments to 8 managers across private equity, private credit, real assets, and hedge funds, but I don't have good benchmarking data on whether they are top-quartile performers or whether our fees are competitive. Using Dakota Marketplace, for each of the following managers: [list manager names], pull available performance data including IRR, TVPI, and DPI versus vintage-year benchmarks. Also identify 3-5 comparable managers in each strategy that other foundations of our size ($500M–$2B AUM) have committed to in the last 3 years — specifically peer community foundations and family foundations that allocate similarly. Flag any of our current managers where (1) peer allocators have meaningfully reduced their allocation, (2) the fund strategy is overrepresented in our portfolio relative to peers, or (3) the manager is actively fundraising for a new vehicle with materially different terms. Return a comparison PDF organized by asset class that I can use as a briefing document for my board.
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.
