Why Accuracy and Not just Data is Becoming the Real Advantage in Private Markets
By Kumar Ujjwal, Founder and CEO
How One Dataset Exposed the Limits of Generative AI
For years, private-market teams believed their biggest challenge was “data.” Every workflow from capital calls to outreach was redesigned around cleaning it, aggregating it, and trying to make sense of it.
But that assumption is cracking.
Walk into any fund administrator, secondaries shop, or LP office today and you’ll notice the same thing:
there is no shortage of data. There is a shortage of truth.
The industry is flooded with inputs spreadsheets from conferences, half-complete CRM exports, investor lists passed between teams, legacy reports, folders stacked inside folders. There’s more information than ever. But almost none of it is immediately usable.
Everyone is operating with a reality they don’t fully trust.
The problem isn’t that data is missing.
The problem is that the data isn’t right.
It’s inconsistent, outdated, context-blind, and structurally misunderstood.
And that is the real constraint holding private markets back.
Why Generative AI Breaks Inside Private Markets
The last two years created a wave of excitement around AI copilots. Teams plugged them into their workflows expecting clarity, structure, and acceleration. What they got instead was something far more familiar:
Speed without understanding. Answers without accuracy.
General-purpose AI was never designed for private markets. It doesn’t recognize the difference between a sub-fund and an SPV, or why a CIO and a Partner don’t carry the same weight in an investment committee. It can’t trace backer lineage across multiple vintages or interpret why two entities with similar names are legally, financially, and operationally unrelated.
This isn’t a technical limitation, it’s a fundamental mismatch.
Horizontal AI models were trained to predict language, not to understand capital flow, ownership structure, or decision architecture. They are impressive generalists trying to operate in a domain where generalists fail.
And when a model doesn’t understand the system, every output looks polished but lands wrong: the wrong title, the wrong backing, the wrong structure. It doesn’t matter how elegant the language is, the substance collapses.
This is why teams test generative AI once, admire the prose, and then quietly return to spreadsheets and manual checks.
The Real-World Test That Changed Everything
A few weeks ago, one of our users ran a simple experiment.
No prompting tricks. No advanced workflows. Just a dataset any operations team would recognize.
Ten rows.
Basic company names.
Partial titles.
Fragmented notes.
They ran this same sheet through four of the biggest AI tools in the world — tools with billions of dollars behind them and billions of users.
Every one of them failed in the exact same way.
- They hallucinated backers.
- They misidentified the CIO.
- They flattened multi-entity fund structures into a single “fund.”
- They turned a clean operational question into an imprecise, text-heavy answer.
It wasn’t just wrong, it was confidently wrong.
Then they pushed those same 10 rows through our vertical engine.
Suddenly, the noise disappeared.
- The correct backers showed up.
- CIOs and Partners were separated cleanly.
- The multi-layer fund structure rendered accurately.
- Geography, strategy, and context fell into place.
The user paused, looked at the output, and said something we’ve heard more often lately:
“Your AI understands how our world works. The others don’t.”
That single sentence captures the turning point private markets are entering.
Truth, Not Data, Is Becoming the New Operating Advantage
Once a team sees truth-grade enrichment just once even on ten rows they understand the shift instantly.
It’s no longer about having more data. It’s about having the right representation of reality.
Because truth compounds.
When enrichment is accurate:
- Outreach becomes targeted instead of generic.
- DDQs get answered in minutes instead of days.
- Audit prep stops being a scramble.
- Reconciliation becomes a verification, not a rebuild.
- Capital-call workflows stop depending on tribal knowledge.
- Reporting stops requiring twelve versions of the same spreadsheet.
- And leadership starts trusting what they see.
Truth changes how a firm moves.
This is why teams using vertical AI are accelerating, not because the AI is faster, but because the inputs are finally trustworthy.
Speed without accuracy creates rework.
Accuracy creates scale.
The Quiet, Industry-Wide Split Now Forming
Every private-market firm is unknowingly choosing between two paths:
Path 1: AI that sounds good but knows nothing.
You get summaries, sentiment, and clean language but you're still verifying every detail manually.
It’s the illusion of progress.
Path 2: AI that understands how capital, people, and structures actually interact.
You get enrichment that survives scrutiny, informs strategy, and integrates seamlessly into workflows.
It’s the foundation for operational transformation.
The industry is not asking for “more AI.”
It is asking for AI that understands the asset class.
Vertical AI Is Becoming the New Infrastructure Layer
The future of private markets won’t be defined by who has the biggest dataset or the most automation.
It will be defined by who has the most accurate picture of reality and who can act on it instantly.
Vertical AI becomes the unseen operating system behind:
- Fund administration
- Investor relations
- Secondaries and co-invest
- Compliance and reporting
- Capital calls and alerts
- Outreach and fundraising
- Data rooms and audit prep
- Reconciliation and NAV cycles
Not because it replaces people but because it removes the friction that has weighed the industry down for decades.
When truth becomes accessible, strategy becomes inevitable.
Private markets have always rewarded those who see clearly.
Now, for the first time, AI can actually help them do it.