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 Taft-Hartley Real Assets Gap Tearsheet Pack
For: Managing Directors of Investor Relations at infrastructure and real assets managers running an open-end core infrastructure raise. The Job: Building a tearsheet pack of Taft-Hartley and multi-employer labor union pension plans with a quantified gap to their real assets target, tiered by whether a consultant bridge already exists.
The prompt
Using Dakota Marketplace, build me a tearsheet pack of Taft-Hartley and multi-employer labor union pension plans that are credible LP targets for our $1.5B open-end core infrastructure fund. I'm the MD of IR at a real assets manager and we carry a responsible-contractor policy and prevailing-wage commitment that plays well in this channel. Filter to U.S. multi-employer and union plans with $750M+ in total assets that either hold an existing infrastructure or real assets allocation or carry a target they are underinvested against. For each plan give me total plan assets, current and target real assets allocation percentages, the dollar gap to target, the most recent disclosed infrastructure or real estate commitment with manager name and size, the investment consultant of record, board meeting cadence, and whether they invest directly or are consultant-gated. Include full contact details for the Executive Director or Fund Administrator and the lead investment staffer or trustee committee chair. Rank the pack into Tier 1 (near-term: dollar gap plus a consultant we already know), Tier 2 (build the relationship this year), and Tier 3 (monitor), and for each Tier 1 name draft a two-sentence opening angle that references their specific allocation gap.
2. The Private Credit CIO Search Candidate Map
For: Senior Search Consultants in the asset management practice of executive search firms running CIO mandates. The Job: Building a candidate mapping grid for a CIO search at a large public pension, cross-referencing current allocators of the client's target asset class with tenure and track record, tiered by placeability.
The prompt
Using Dakota Marketplace, I'm running a CIO search for a $4B public pension client that's building out a private credit-heavy portfolio. Map me candidates currently serving as CIO or Deputy CIO at U.S. public pensions and large corporate plans with 2B-8B in assets who have overseen a private credit allocation build from under 5% to over 12% in the last five years. For each candidate give me current plan, tenure in role, prior roles, the plan's private credit allocation trajectory during their tenure, board reporting structure they operate under, and whether the plan uses a consultant or runs allocations in-house. Flag any candidates whose current plan has had CIO turnover rumors or recent public board conflict. Tier into Tier 1 (strong track record match, likely open to a call), Tier 2 (good background, lower likelihood of movement), and Tier 3 (long-shot but worth a listen). For each Tier 1 name, draft a two-sentence outreach angle referencing their specific allocation build.
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 Sell-Side Co-Investment Target List
For: Vice Presidents of Deal Sourcing on the financial sponsors coverage team at middle-market investment banks. The Job: Building a target list of family offices and small institutional allocators actively deploying into direct co-investments in a specific sector, sized to check size, for a live sell-side process.
The prompt
Using Dakota Marketplace, build me a co-investment target list for a $180M sell-side process in industrial services. I need U.S. family offices and small institutional allocators (endowments, foundations under $1.5B) that have made at least one direct co-investment or club deal commitment of 10M-40M in industrials or business services in the past 24 months. For each name give me total plan/office assets, typical direct-deal check size, sector focus areas, most recent disclosed co-investment with deal size and sponsor, whether they lead or follow in club deals, and the primary decision-maker with contact details. Rank into Tier 1 (check size and sector fit confirmed by recent activity), Tier 2 (plausible fit, thesis unconfirmed), and Tier 3 (speculative). For each Tier 1 name draft a two-sentence pitch angle referencing their specific past co-investment.
4. The Compliance Threshold Prospecting List
For: Directors of Business Development at compliance and regulatory technology vendors. The Job: Building a prospecting list of RIAs and private fund managers approaching key regulatory thresholds, tiered by urgency, for a compliance software sales push.
The prompt
Using Dakota Marketplace, I'm prospecting for our compliance software targeting SEC-registered RIAs and private fund managers between $500M and $5B in AUM that are likely underserved on compliance infrastructure. Filter to firms that have grown AUM by 40%+ in the past two years, have added 3+ new investment vehicles or share classes recently, or have expanded into a new state or jurisdiction. For each firm give me current AUM, AUM growth rate, headcount if disclosed, number of registered investment vehicles, CCO name and tenure, and any recent regulatory or exam disclosures. Tier into Tier 1 (fast AUM growth plus thin compliance headcount, urgent need), Tier 2 (growing but adequate staffing, medium-term opportunity), and Tier 3 (stable, low urgency). For each Tier 1 firm draft a two-sentence opening line referencing their specific growth trigger.
5. The Metro Roadshow Allocator Briefing
For: Regional Sales Directors at fintech data and analytics platforms. The Job: Building a roadshow prep briefing on institutional allocators in a target metro, quantifying platform fit and prior vendor relationships, ahead of an in-person swing.
The prompt
Using Dakota Marketplace, I'm prepping a roadshow through Chicago and I want a briefing on institutional allocators headquartered in the metro with $500M+ in assets who are plausible targets for our analytics platform. For each allocator give me total assets, asset class allocation mix, current data/analytics vendor if disclosed or inferable from RFP history, investment team size, and whether they've issued an RFP or vendor review in the past 18 months. Flag any allocator with a known contract renewal or vendor dissatisfaction signal. Tier into Tier 1 (active vendor review or contract expiring, book the meeting), Tier 2 (fit but no urgency signal, worth a coffee), and Tier 3 (low fit, skip unless time allows). For each Tier 1 allocator draft a two-sentence meeting-request angle referencing their specific vendor situation and give me the lead investment ops or research contact with details.
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.
