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 Secondaries Portfolio Buyer List
For: Managing Directors at private equity secondaries placement agents. The Job: Building a buyer list of fund-of-funds and gatekeepers with disclosed secondaries dry powder, sized and vintage-matched to a diversified LP-led secondaries portfolio sale.
The prompt
Using Dakota Marketplace, I'm placing a $400M diversified LP-led secondaries portfolio spanning 2016-2019 vintage buyout and growth funds. Build me a buyer list of secondaries funds of funds and institutional gatekeepers with $2B+ in dedicated secondaries capital that have closed a secondaries fund or side pocket in the past 24 months. For each buyer give me total secondaries AUM, most recent secondaries fund size and close date, typical deal size range, vintage and strategy preferences if disclosed, and the lead secondaries deal contact with details. Flag any buyer who has previously bid on or acquired positions from the underlying GPs in this portfolio. Tier into Tier 1 (active dry powder, vintage and strategy fit confirmed), Tier 2 (capacity likely but fit unconfirmed), and Tier 3 (speculative). For each Tier 1 name draft a two-sentence outreach angle referencing their most recent secondaries close.
2. The First-Time VC Fund LP Target List
For: VPs of Fundraising at first-time venture capital funds building their initial institutional LP base. The Job: Building an LP target list of family offices and RIAs with disclosed venture allocations sized appropriately for a first institutional venture fund's minimum check.
The prompt
Using Dakota Marketplace, I'm raising a $150M Fund I focused on seed and Series A enterprise software and need a target list of U.S. family offices and RIAs with a disclosed venture capital allocation or a documented history of writing checks into early-stage VC funds. Filter to those whose typical fund commitment falls between $2M and $10M, since that's our minimum check range. For each name give me total AUM, venture allocation as a percentage of the book, most recent VC fund commitment with manager name and vintage, whether they've backed a first-time fund before, the primary decision-maker with title and contact details, and whether they invest directly or through an advisor. Tier into Tier 1 (fits check size, has backed a Fund I before), Tier 2 (fits check size, no Fund I history), and Tier 3 (borderline fit). For each Tier 1 name draft a two-sentence opening angle referencing their most recent VC commitment.
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 Fund Administration Emerging Manager Prospecting List
For: Business Development Executives at fund administration and fintech providers. The Job: Building a prospecting list of newly launched or fast-scaling emerging managers likely to need outsourced fund administration, tiered by urgency of need.
The prompt
Using Dakota Marketplace, I'm prospecting for our fund administration platform and want to target emerging private equity and private credit managers that have closed a Fund I or Fund II in the past 18 months with $100M-$750M in committed capital. Filter to managers who don't appear to have a well-known institutional-grade administrator already disclosed, or who have added a second or third fund vehicle recently, since that signals operational strain. For each manager give me fund size and close date, number of active vehicles, headcount if disclosed, CFO or COO name and tenure, and any recent LP-side complaints or delayed capital call notices if publicly referenced. Tier into Tier 1 (multiple vehicles, thin back office, urgent need), Tier 2 (single fund, growing, medium-term opportunity), and Tier 3 (stable, low urgency). For each Tier 1 manager draft a two-sentence opening line referencing their specific operational growth signal.
4. The GP-Led Continuation Vehicle Buyer List
For: Managing Directors in restructuring and secondaries advisory at investment banks. The Job: Building a buyer list of LPs and secondary funds most likely to anchor a GP-led continuation vehicle, sized and sector-matched to the underlying assets.
The prompt
Using Dakota Marketplace, I'm advising on a $600M GP-led continuation vehicle for two industrial assets coming out of a 2015-vintage buyout fund. Build me a buyer list of secondary funds, funds of funds, and institutional LPs with $1B+ in assets that have participated in a GP-led continuation vehicle or single-asset secondary in the past three years, with a preference for existing industrials exposure. For each name give me total assets, secondaries program size if disclosed, most recent GP-led or continuation vehicle participation with size and sector, typical check size in a continuation deal, and the lead secondaries or private equity contact with details. Tier into Tier 1 (recent continuation vehicle participation plus sector fit), Tier 2 (secondaries capacity but no sector-specific history), and Tier 3 (plausible but unconfirmed appetite). For each Tier 1 name draft a two-sentence angle referencing their most recent continuation vehicle deal.
5. The Insurance Company Direct Lending Target List
For: Heads of Capital Formation at private credit funds pursuing direct lending strategies. The Job: Building a target list of insurance companies with disclosed private credit allocations and capacity for a new direct lending manager relationship.
The prompt
Using Dakota Marketplace, I'm raising a $2B direct lending fund and want to prioritize insurance companies as an LP channel. Filter to U.S. life and P&C insurers with $3B+ in general account assets that have a disclosed private credit or direct lending allocation, or that have made a private credit manager commitment in the past three years. For each insurer give me total general account assets, current private credit allocation percentage, most recent private credit or direct lending commitment with manager name and size, whether allocations run through an internal team or a sub-advisor, NAIC designation considerations if disclosed, and the lead private credit or alternatives contact with details. Tier into Tier 1 (active allocator, no relationship with us yet), Tier 2 (allocates to the space but consultant-gated), and Tier 3 (limited or unclear capacity). For each Tier 1 name draft a two-sentence opening angle referencing their specific private credit commitment history.
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.
