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 Timberland and Agriculture Fund Fundraising Tearsheet Pack
For: Managing Directors of Real Assets Fundraising at timberland and agriculture managers preparing board-level fundraising updates. The Job: Identifying endowments, foundations, and public pensions with documented real assets, natural resources, or timberland allocations and recent related commitment activity, packaged as a tearsheet pack organized by institution type for pre-meeting review.
The prompt
I'm raising capital for our $900M timberland and agriculture fund and want to prioritize endowments, foundations, and public pensions with $3B or more in AUM that have a documented real assets, natural resources, or timberland allocation, or that have made a related commitment in the last 24 months. Using Dakota Marketplace, identify these institutions, and for each one include total AUM, current real assets allocation percentage, most recent related commitment, and key investment and relationship contacts with title and email. Package this as a tearsheet pack organized by institution type that I can review with our board before next month's fundraising update.
2. The Southeast Commercial HVAC Roll-Up Target List
For: VPs of Portfolio Operations at middle-market buyout funds executing regional roll-up strategies through existing portfolio companies. The Job: Identifying founder-owned and family-operated commercial HVAC and mechanical services businesses in the Southeast with long-tenured owners and succession signals, ranked as bolt-on acquisition candidates with sourcing rationale.
The prompt
Our portfolio company is a regional provider of commercial HVAC services and we're executing a roll-up strategy in the Southeast. Using Dakota Marketplace, identify privately held, non-sponsored HVAC and mechanical services companies headquartered in GA, FL, NC, SC, and TN with estimated revenue between $5M and $30M and a founder or family owner who has been in the role for 15 or more years. For each target, show company name, location, estimated revenue, owner name and tenure, and any known succession or transaction signals. Return a ranked PDF of the top 25 bolt-on candidates 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 European and Asian Strategic Buyer Universe
For: Directors of Cross-Border M&A at global investment banks building strategic buyer universes for U.S. industrial technology sell-side processes. The Job: Mapping European and Asian acquirers with recent U.S. transaction history in industrial automation, robotics, and industrial software, flagged by repeat buyer status as the strongest targets for early process outreach.
The prompt
I'm running a sell-side process for a U.S. industrial automation and technology platform and need to build our strategic buyer universe. Using Dakota Marketplace, identify European and Asian strategic acquirers that have completed at least one U.S. acquisition in industrial automation, robotics, or industrial software over the past 24 months. For each acquirer, show headquarters country, sector focus, their most recent U.S. transaction with deal size, and the corporate development contact who led it. Flag any acquirers that have made more than one U.S. acquisition in this window, since repeat buyers are the strongest targets for an early call. Return a ranked buyer list I can share with the deal team.
4. The Multi-Strategy RIA Chief Compliance Officer Candidate Longlist
For: Managing Directors at financial services executive search firms running retained CCO searches at multi-strategy RIAs that have recently expanded into private funds. The Job: Building a ranked CCO candidate longlist from comparable SEC-registered advisers managing private funds, flagged by tenure signals and a prior larger-to-smaller platform transition pattern indicating openness to new challenges.
The prompt
I'm retained to find a Chief Compliance Officer for a $4B multi-strategy RIA that recently expanded into private funds. Using Dakota Marketplace's Form ADV and career history data, identify current CCOs and Deputy CCOs at other SEC-registered advisers with $2B to $8B in AUM that also manage private funds. For each candidate, show current firm, title, tenure in role, total years in compliance roles, firm AUM and number of private funds managed, and any prior firms in their career history. Flag candidates who have been in their current role for three or more years and previously worked at a larger platform before moving to a smaller adviser. Return a ranked longlist of the top candidates as a PDF for my client intro call.
5. The Pre-Commitment Peer Pension Manager Termination Check
For: Directors of Manager Research at state public retirement systems conducting final due diligence checks before committing capital to a new manager. The Job: Surfacing peer public pension terminations, redemptions, or position reductions in a target manager over the last 24 months, with disclosed reasons from board minutes or meeting materials flagged as red flags for investment committee review.
The prompt
We're in final due diligence on a global macro hedge fund manager and want a last check before committing capital. Using Dakota Marketplace, identify peer public pension plans above $5B in AUM that currently or previously allocated to this manager, and flag any that have terminated, redeemed, or reduced the position in the last 24 months. Where board minutes or meeting materials disclose a reason, include it. Return a summary table so I can bring any red flags to our investment committee before we finalize the allocation.
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.
