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 Corporate Pension Re-Up Prioritization List
For: Managing Directors of Investor Relations at large-cap buyout funds preparing a re-up campaign ahead of a Fund VI first close. The Job: Identifying existing corporate pension LPs that have increased their private equity target allocation in the last 12 months, ranked by re-up likelihood based on commitment history, relationship warmth, and consultant gatekeeper status.
The prompt
We're 90 days from launching our Fund VI first close and want to prioritize re-up conversations with our existing corporate pension LP base. Using Dakota Marketplace, pull all corporate pension plans currently in our fund family and flag those that have increased their private equity target allocation in the last 12 months or made a new PE commitment to another manager in that window — both are signals of active deployment. For each, show current AUM, PE allocation percentage, most recent commitment to our fund family with vintage and size, the two best relationship contacts with title and email, and any consultant gatekeeper on the account. Rank by re-up likelihood and return a prioritized list I can use to sequence our outreach over the next 60 days.
2. The Sovereign Wealth Fund First-Time Allocation Opportunity Map
For: Heads of International Fundraising at established private credit managers identifying sovereign wealth funds that are newly building out private credit allocations. The Job: Surfacing sovereign wealth funds globally that have made their first or second private credit commitment in the last 24 months, with decision-maker contacts and consultant relationships flagged for first-meeting outreach.
The prompt
I lead international fundraising for a $4B private credit manager and want to identify sovereign wealth funds that are in the early stages of building a private credit allocation — meaning they have made one or two commitments to the asset class in the last 24 months but do not yet have a mature, diversified private credit portfolio. Using Dakota Marketplace, identify sovereign wealth funds globally with AUM over $20B that fit this profile. For each, show total AUM, number of private credit commitments on record, most recent commitment with manager name and size, key investment decision-makers with title and direct contact, and whether they work with an external consultant or OCIO. These are my highest-priority first-meeting targets — return a ranked list of the top 15.
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 Consultant-Approved Manager List Gap Analysis
For: Directors of Capital Formation at mid-market private equity firms building a consultant relations strategy to unlock multiple LP relationships simultaneously. The Job: Identifying the top investment consultants whose approved manager lists cover the highest concentration of target LPs, ranked by number of relevant client relationships, with consultant coverage contacts for outreach prioritization.
The prompt
I run capital formation at a $2.5B mid-market PE fund focused on business services and industrials. Using Dakota Marketplace, identify the investment consultants — NEPC, Verus, Mercer, Cambridge Associates, Wilshire, and others — whose client rosters have the highest overlap with my target LP universe: public pensions, corporate pensions, and endowments with $1B to $10B in AUM and an active PE allocation. For each consultant, show the number of relevant client relationships on their roster, the names of those clients, whether our firm currently appears on their approved manager list, and the primary consultant contact responsible for PE manager research. Rank consultants by the number of target LP relationships they influence, so I can prioritize where to invest relationship-building time first.
4. The Insurance Company Separate Account Prospect List
For: Managing Directors of Capital Formation at large-cap private credit managers building an insurance company LP channel with a focus on separate account mandates. The Job: Identifying North American insurance companies with AUM over $10B that have awarded separate account mandates to private credit managers in the last 24 months, flagged by asset-liability matching profile and key investment decision-maker contacts.
The prompt
I'm building our insurance company LP channel for our private credit platform and want to focus specifically on insurers that have awarded separate account mandates rather than commingled fund commitments — a structure we can accommodate and that tends to drive larger ticket sizes. Using Dakota Marketplace, identify North American insurance companies with AUM over $10B that have awarded a separate account or SMA mandate to a private credit or direct lending manager in the last 24 months. For each, show total AUM, investment portfolio size, private credit allocation percentage, the specific mandate awarded with approximate size and strategy, and the Head of Fixed Income, Head of Private Assets, or CIO contact with direct email. Flag any accounts where our firm has no prior relationship and return a ranked prospect list of the top 20.
5. The Family Office Co-Investment Network Build
For: VP of Investor Relations at a growth equity fund building a family office co-investment program alongside a primary fund raise. The Job: Identifying single-family offices and multi-family offices with active co-investment programs in growth equity or technology, ranked by check size fit and prior co-investment activity, to build a parallel co-invest pipeline alongside the primary fund close.
The prompt
We're raising our $600M Fund III and want to build a co-investment program alongside the primary raise to accommodate family offices that prefer direct deal access. Using Dakota Marketplace, identify single-family offices and multi-family offices with AUM between $500M and $3B that have made at least two documented co-investments in growth equity or technology in the last 36 months. For each office, show total AUM, typical co-investment check size if known, sector preferences, the key investment contact with title and direct email, and any existing relationship with our firm. Exclude offices already in our fund as primary LPs. Rank by co-investment activity level and return a target list of 25 family offices I can approach with a co-investment conversation in parallel with our fund marketing.
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.
