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 Lapsed LP Re-Engagement List
For: Heads of investor relations at mid-market growth equity funds. The Job: Surfacing dormant LP accounts ranked by AUM and GP due diligence signals for a targeted re-engagement push.
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 HR Tech Exit Buyer Universe
For: Senior associates at lower middle market PE funds targeting founder-owned and For: VPs of business development at growth equity funds preparing to exit a B2B SaaS HR technology portfolio company. The Job: Building a ranked buyer universe of financial and strategic acquirers with demonstrated HR tech M&A activity and current deployment capacity.
The prompt
Using Dakota Marketplace, I am preparing to exit a portfolio company in the B2B SaaS / HR technology space with approximately $25M ARR and targeting a $150–300M transaction. Help me build a buyer universe: (1) Financial buyers — PE funds and growth equity managers that have completed acquisitions in HR tech or workforce management software in the last 36 months, have current funds of $500M+ with available dry powder, and target companies in the $100–500M enterprise value range. (2) Strategic buyers — public and private companies in the HCM, payroll, or HR software sector with demonstrated M&A activity in the last 3 years. For each buyer, show: firm or company name, relevant deal history, key contact at the firm, fund size or revenue, and any Dakota data signals indicating current acquisition activity. Flag buyers that have co-invested with our existing advisors or LPs.
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 Industrial Services Sponsor Target List
For: Directors of financial sponsors coverage at middle market investment banks pitching a new sell-side mandate. The Job: Ranking PE sponsors by recency of industrial services transaction activity and flagging those actively fundraising for near-term capital deployment.
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 PE & VC Portfolio Monitoring Software Prospect List
For: VPs of sales at CRM and portfolio monitoring platforms targeting PE and VC firms. The Job: Identifying GPs scaling their LP base after a recent fund close that are most likely in an active vendor evaluation.
The prompt
Using Dakota Marketplace, build a target account list of PE and VC firms likely evaluating or upgrading their portfolio monitoring and LP reporting technology. Filter for firms with 3+ active portfolio companies and AUM between $500M and $5B. Cross-reference against firms that closed a new fund in the last 24 months — these GPs are scaling LP bases and most likely in an active vendor evaluation. Flag firms with recent headcount growth in ops or finance roles.
5. The Fund IV/V IR Talent Pipeline Map
For: Managing directors at executive search firms mapping IR and distribution talent across mid-market PE funds ahead of a fundraise. The Job: Identifying IR professionals at funds actively raising Fund IV or V, with vacancy and recent hire flags as high-priority search targets.
The prompt
Using Dakota Marketplace, map the current IR and distribution talent across mid-market PE firms actively fundraising for Fund IV or V. For each firm, identify Head of IR, VP of Investor Relations, and distribution professionals, including career history, prior employers, and tenure. Flag firms where the IR role has been vacant or newly filled in the last 12 months as high-priority search targets.
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.
