Every CEO of a data company eventually learns the same lesson: the hardest competitor isn’t the better product. It’s the workflow your buyer has already built.
A deal team that has spent two years learning an incumbent’s UI, filters, exports, and saved searches will tolerate stale data, missing fields, and clunky search before they’ll voluntarily learn a new system.
The switching cost isn’t the contract. It isn’t the price.
It’s the cognitive load of rebuilding the muscle memory of daily work.
That’s why incumbents in data SaaS hold positions they don’t deserve — not because they’re better, but because they’re already there.
For nearly thirty years at Dakota, I have watched this dynamic play out across every category that touches private markets data. The team with superior coverage, better accuracy, and faster updates often loses to the team that got there first and trained the workflow. The devil you know wins.
The Claude app changes that dynamic completely.
The Inversion
The Claude app changes the unit of analysis… and with it, the entire competitive equation.
The workflow is no longer the data vendor’s UI. The workflow is the conversation with Claude. Deal teams across private equity, venture, private credit, and corporate development are already there. They are already drafting memos, prepping for meetings, screening targets, building precedent transactions, and synthesizing research inside Claude every day. That workflow is established. It is growing every week.
The Dakota Marketplace connector for the Claude app does not ask a deal team to adopt anything new. There is no new login. No new interface. No new search syntax. No new mental model. No retraining. No project plan. No internal champion required to drive adoption.
It simply puts the most complete, accurate, enriched, and daily-refreshed private markets dataset (covering GPs, LPs, private companies, transactions, funds, fund performance, benchmarks, and technology) directly inside the workflow your team has already chosen.
You are not asking a deal team to switch. You are asking whether the Claude they already use should have the best data available, or the second-best.
Not Additive. Multiplicative.
This is the part most buyers miss on the first pass, and it is the most important point in the entire argument.
In the old model, better data meant better lookups. You searched, you found, you exported into Excel or Word. Data quality scaled linearly with output quality. Ten percent better coverage produced roughly ten percent better answers.
In the Claude plus Dakota model, data quality compounds with reasoning. Claude is not retrieving. Claude is synthesizing, drafting, comparing, reformatting, and reasoning across the dataset in real time. When the underlying data is incomplete, every downstream operation inherits that incompleteness. When the data is stale, every memo, every screen, every comp set, every briefing is stale. When the data is complete, accurate, and current, every reasoning step Claude performs gets better.
That is why connecting Dakota Marketplace to the Claude app is not a ten percent improvement over connecting an incumbent. It is ten to twenty times the output.
Better data multiplied by Claude’s reasoning multiplied by the number of workflows a deal team runs in a week is exponential, not linear. The improvement does not show up once. It shows up in every memo, every meeting prep, every sourcing pull, every comp set, every IC briefing — all week, every week, compounding indefinitely.
A small advantage in data quality, run through an AI reasoning layer thousands of times a month, becomes a transformative advantage in output.
The Reframe a Deal Team Head Should Hear
The question is no longer “should we switch from our current provider to Dakota.”
The question becomes: “Our team is already using Claude. Should the data powering that work be the most complete private markets dataset in the industry — or something less?”
Framed that way, the decision is trivial. The switching-cost objection evaporates because there is no workflow to switch. There is only fuel to upgrade.
The Bind it Puts Incumbents In
The harder problem belongs to the incumbents. Their data sits behind their UI, and that UI was their moat. To stay relevant in an AI-native workflow, they have to become connectors too. The moment they do, the UI advantage disappears and they are competing on pure data quality — coverage, accuracy, freshness, structure, and depth.
Which is the fight Dakota was built to win.
The New Era
The engine is AI. The fuel is data. For three decades we have been building the fuel… practitioner by practitioner, transaction by transaction, GP by GP, fund by fund, LP by LP. Now there is an engine worthy of it, and that engine is already running inside your deal team’s daily work.
You don’t switch workflows. You fuel them.
Dakota Marketplace covers the full private markets stack: GPs, LPs, private companies, transactions, funds, fund performance, benchmarks, and the technology behind them. 69,000+ private market transactions tracked. Tens of thousands of sponsor-backed companies. Daily updates by a 60-person research team.
The connector is live. Your team's workflow doesn't change. The data behind it does.
Book a demo to see Dakota Marketplace inside the Claude app.
