Dakota Marketplace is the global private markets intelligence platform used by thousands of investment professionals to research LPs, GPs, and private companies — and the data source powering every prompt in this post. Built by fundraisers for fundraisers, Dakota Marketplace delivers complete, accurate, and daily-updated intelligence across every allocator channel — from family offices and RIAs to sovereign wealth funds and public pensions. 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 Tiered Family Office Pipeline for a Q3 Fund Close
For: Managing directors of fundraising at lower mid-market buyout funds building a prioritized LP pipeline ahead of a close The Job: Segmenting family offices by prior buyout commitment history and check size fit to sequence outreach for a Q3 close
The prompt
Search Dakota Marketplace for family offices that meet all of the following criteria: (1) have made at least one prior commitment to a lower mid-market buyout fund (sub-$1B fund size), (2) typical check size between $5M and $25M, (3) based in the US, (4) direct investment team of at least 2 people. For each result, return: family office name, city, AUM, investment team headcount, key decision-maker name and email, most recent PE fund commitment (manager, vintage, amount), and any notes on preferred sectors or geographies. Segment results into Tier 1 (prior buyout commit + check size match) and Tier 2 (check size match only) so I can prioritize outreach sequencing for our Q3 close.
2. The Scaled RIA Fund Administration Prospect List
For: Enterprise account executives selling fund administration and back-office automation software to mid-market PE and private credit firms The Job: Identifying newly scaled RIAs managing private funds that have recently closed a new vehicle and are likely feeling operational strain
The prompt
I sell fund administration and back-office automation software to mid-market private equity and private credit firms. Using Dakota Marketplace, identify registered investment advisers that manage at least one private fund with AUM between $200M and $2B, have closed a new fund in the last 18 months, and have 10 or more LP investors across their platform. These firms are likely experiencing operational scaling pain and are strong candidates for fund administration modernization. Show the firm name, AUM, number of active funds, fund strategy, key operational contact (CFO, COO, or Head of Operations) with email if available, and any signals of rapid growth such as a new fund close in the last 12 months. Prioritize firms headquartered in the Northeast and Mid-Atlantic. Return a ranked outreach list of the top 30 targets as a PDF.
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 Private Credit Manager Re-Up Evaluation
For: CIOs at university endowments evaluating whether to re-up with an existing private credit manager or diversify to a new one The Job: Benchmarking active private credit funds against peer endowment commitments and performance comparables ahead of an investment committee decision
The prompt
I'm the CIO of a $2.5B university endowment with a 12% target allocation to private credit. We're evaluating whether to re-up with our existing manager or diversify to a new manager for our next commitment cycle. Using Dakota Marketplace, pull a list of private credit funds that have closed or are actively fundraising in the last 24 months with fund sizes between $1B and $10B. For each fund, show the manager name, fund name, fund size, vintage year, strategy (direct lending, distressed, mezzanine, etc.), management fee and carry if available, IRR and TVPI benchmarks from comparable funds, and any peer university endowments (AUM $1B–$5B) that have made a commitment. Flag managers where 3 or more peer endowments have committed in the same vintage year. Return a comparison PDF I can share with my investment committee.
4. The Head of Portfolio Operations Candidate Map
For: Managing partners at PE-focused executive search firms sourcing candidates for newly created portfolio operations roles at growth equity funds The Job: Identifying operating partners and portfolio operations professionals at PE and growth equity firms with recent firm changes or fund activity that signals availability
The prompt
I run an executive search firm specializing in private equity. A $6B growth equity fund has retained me to find candidates for a newly created Head of Portfolio Operations role — someone who will work directly with portfolio company C-suites on operational improvements, revenue acceleration, and M&A integration. Using Dakota Marketplace, identify individuals currently in portfolio operations, operating partner, or value creation roles at PE and growth equity firms with AUM between $2B and $15B. Return their full name, current firm, title, AUM of their firm, city, and any professional contact information available. Flag anyone who has changed firms in the last 12 months or whose firm has recently closed a new fund (indicating potential role displacement or opening).
5. The Field Service Software Add-On Target List
For: Senior associates at middle-market PE funds building rollup theses in vertical market SaaS for field service industries The Job: Identifying sponsor-owned and founder-led field service software companies in the right revenue range for a platform add-on, with motivated seller flags
The prompt
I'm a Senior Associate at a middle-market PE fund building a rollup thesis in vertical market SaaS for field service businesses — HVAC, plumbing, landscaping, and similar trades. Our platform company has $45M ARR and we need add-on targets. Using Dakota Marketplace, find all sponsor-owned and founder-led field service software or workflow automation companies in North America with estimated revenue between $5M and $40M. Include company name, primary sector, sponsor or ownership status, estimated revenue, CEO and CTO contact info, and year of last known transaction or financing. Flag companies sponsor-owned for more than 4 years with no transaction — those are likely motivated for a strategic exit. Return a ranked PDF of the top 25 targets.
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.
