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 Continuation Vehicle Secondaries Buyer Tearsheet Pack
For: Heads of fundraising at GP-led secondaries and continuation vehicle sponsors The Job: Building tearsheets on dedicated secondaries buyers and endowments with documented GP-led participation before a $600M continuation vehicle roadshow
The prompt
I'm raising a $600M continuation vehicle to hold two mature portfolio companies from our Fund II. Using Dakota Marketplace, build a tearsheet pack of the top institutional secondaries buyers — dedicated secondaries funds, fund-of-funds, and endowments with documented GP-led secondary participation in the last 24 months — with AUM over $1B. For each institution, include total AUM, secondaries program size and typical check, most recent GP-led or LP-led secondary transaction, key contact with title and email, and any notes on preferred deal structure (single-asset vs. multi-asset continuation vehicles). Package this as a tearsheet pack I can review before our roadshow next month.
2. The Infrastructure Debt Fund II Insurance GA Tearsheet Pack
For: Heads of fundraising at middle-market infrastructure debt funds targeting insurance general accounts for a first close The Job: Building tearsheets on U.S. life and P&C insurance GAs with infrastructure debt allocations and recent commitments before the first-close roadshow
The prompt
We're raising a $1.2B Fund II focused on core-plus infrastructure debt. Using Dakota Marketplace, build a tearsheet pack of 6 U.S. life and P&C insurance general accounts with $10B+ in invested assets that have a documented infrastructure debt or private placement allocation and have made at least one infrastructure-related commitment in the past 24 months. For each institution, include invested assets, current infrastructure/private placement allocation percentage, most recent relevant commitment (manager, size, date), the two best contacts with title and email, and notes on relationship warmth and any consultant gatekeeper. Format as a tearsheet pack I can review before our first-close roadshow.
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 Emerging Manager Program Screening List
For: Directors of emerging manager programs at public pension funds The Job: Screening newly registered diverse-owned PE and private credit managers for inclusion in an emerging manager program ahead of the next committee meeting
The prompt
I run the emerging manager program at a public pension fund. Using Dakota Marketplace's Form ADV and firm data, identify private equity and private credit managers founded in the last 5 years with under $500M in AUM and disclosed diverse ownership. For each firm, show AUM, strategy focus, year founded, founder background, and any existing institutional commitments on record. Flag firms that have already been backed by peer public pensions as priority candidates. Return a ranked PDF screening list I can bring to our next emerging manager committee meeting.
4. The Operating Partner Candidate Longlist
For: Managing directors at operating partner and portfolio talent practices conducting retained searches The Job: Building a longlist of C-suite executives with PE-backed portfolio company leadership experience for a middle-market PE firm's portfolio operations group
The prompt
I'm retained to find an Operating Partner for a $2B middle-market PE firm's portfolio operations group. Using Dakota Marketplace's career history and contact data, identify current or former C-suite executives (CEO, COO, or President) who have led PE-backed or sponsor-owned portfolio companies with $50M–$500M in revenue, and who have been in their current seat for three or more years or have recently departed a portfolio company post-exit. For each candidate, show current role and employer, years of relevant PE-backed operating experience, sector focus, and any board or advisory roles on record. Return a ranked longlist of the top 20 candidates as a PDF I can present at the client intro call.
5. The PE Firm Portfolio Monitoring Software Prospect List
For: Enterprise account executives at portfolio monitoring software companies building a Q3 outbound campaign The Job: Identifying mid-sized PE firms without existing CRM or portfolio monitoring integrations, ranked by portfolio company count and recent fund close activity
The prompt
Pull all private equity investment firms in Dakota Marketplace with $1–5 billion in AUM and 15–40 active sponsor-backed portfolio companies. Include firm operations or IT leadership contacts, current CRM platform if known, and recent fund closes indicating budget availability. Rank by portfolio company count and flag firms that closed a fund in the last 12 months as highest-priority prospects for a Q3 outbound campaign.
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.
