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 Emerging Manager First Close Tearsheet Pack
For: Heads of capital formation at debut credit funds targeting emerging manager program allocators The Job: Building tearsheets on endowments, foundations, and public pensions with active emerging manager programs and first-time commitment history before a roadshow leave-behind
The prompt
We're six weeks from a first close on our debut credit fund and need to fill the emerging-manager bucket of our LP base. Using Dakota Marketplace, build a tearsheet pack of endowments, foundations, and public pensions with a documented emerging-manager or diverse-manager program, first-time commitment history in the $5M–$20M range, and no existing relationship with our firm. For each institution include AUM, program mandate details, key program contact with title and email, and the most recent emerging-manager commitment logged in Dakota. Package it as a leave-behind for our roadshow next month.
2. The Fund III Direct Lending First Close Shortlist
For: Heads of fundraising at lower-middle-market private credit funds building a ranked LP shortlist ahead of a first close The Job: Surfacing the top eight allocators with private credit allocation history and sub-$1B fund commitment track records, each with a "why now" rationale
The prompt
We are raising a $750M Fund III lower-middle-market direct lending vehicle. Pull allocator accounts from Dakota Marketplace with $2B–$15B in total AUM, a documented private credit or direct lending allocation of at least 3%, and at least one commitment to a fund under $1B in the past 3 years. Include endowments, foundations, and public pension plans. For each institution, surface AUM, private credit target allocation, most recent private credit commitment (manager, size, date), key contacts with title and email, and any recent Public Plan Minutes or Dakota Search referencing direct lending. Build a ranked shortlist of 8 institutions with a one-paragraph 'why now' 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 Multifamily Recapitalization Equity Target List
For: VPs in real estate investment banking building an equity target list for a $500M multifamily portfolio recapitalization The Job: Identifying institutional investors with real estate allocation gaps of 200 basis points or more ranked by gap size
The prompt
I'm advising a sponsor on a $500M multifamily portfolio recapitalization and need to build the equity target list. Using Dakota Marketplace, identify public and corporate pension funds, insurance companies, and family offices with $1B or more in AUM whose current real estate allocation is below their stated target by 200 basis points or more. For each institution, show total AUM, current vs. target real estate allocation, most recent real estate commitment date and size, key decision-maker contact with title and email, and any recent public plan minutes referencing real estate pacing or multifamily interest. Rank by allocation gap size and return a PDF target list I can bring to our capital markets kickoff.
4. The Diverse Manager Pipeline Refresh for a Taft-Hartley Plan
For: Directors of manager research at multi-employer Taft-Hartley plans refreshing their diverse manager program pipeline The Job: Screening women- and minority-owned PE and private credit managers against peer Taft-Hartley commitments and flagging unevaluated names
The prompt
I run the diverse manager program for our $4B Taft-Hartley plan and need to refresh our private equity and private credit manager pipeline. Using Dakota Marketplace, identify women- and minority-owned investment firms with $100M–$2B in AUM that focus on private equity or private credit strategies and have Form ADV filings on record. For each firm, show AUM, strategy focus, ownership structure notes, key contact with title and email, and any recent institutional commitments they have received from peer Taft-Hartley or multi-employer plans. Also flag firms that other similarly sized Taft-Hartley plans have committed to in the last 18 months but that we have not yet evaluated. Return a briefing PDF organized by asset class for our next diverse manager committee meeting.
5. The Peer Endowment Private Credit Benchmark
For: Directors of investments at university endowments preparing for an annual asset allocation committee review The Job: Mapping private credit allocations and manager commitments across peer endowments and surfacing commonly held managers not yet evaluated internally
The prompt
I'm prepping for our investment committee's annual asset allocation review and need to see how peer endowments are positioned in private credit. Using Dakota Marketplace, identify endowments with $1.5B–$5B in AUM that have a disclosed private credit allocation and show their current allocation percentage, target allocation if disclosed, and most recent manager commitments in the space over the last 24 months. Aggregate which private credit managers have received the most commitments from this peer set, and flag any managers appearing in multiple peer portfolios that we have not yet evaluated. Return a briefing PDF organized by peer institution with a summary table of the most-commonly-held managers at the end.
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.
