About

Data analysis built to produce answers professionals can trust and defend.

Across enterprises, teams sit on large, messy datasets. Getting from raw numbers to a conclusion you can defend usually means a specialist, a spreadsheet nobody fully trusts, and days of back-and-forth. The recurring obstacles:

  • knowing which questions matter
  • structuring the right analysis
  • producing answers that hold up under scrutiny
  • tracing a number back to where it came from
  • keeping definitions consistent across the organization

The result: decisions that should take an afternoon stretch into weeks of specialist handoffs and brittle spreadsheets.

The missing piece is translation

Most analytics platforms assume deep familiarity with schemas and modeling concepts. Conversational AI tools assume the data is small enough that rigorous computation is optional. Enterprise data fits neither assumption: it's large, messy, and consequential.

Nowhere is that more true than in private markets, where the data is bespoke, born in documents, and reaches investors directly. Decision-makers need a defensible answer they can act on immediately, rather than a table to interpret themselves.

The platform works on any large, complex dataset. We built it first for private markets, where these problems are most acute.

How it works

TabInt follows a guided workflow:

  1. Upload: a CSV or Excel file, or documents like credit agreements, data-room files, and deal docs
  2. Extract: pull the key terms out of documents into structured, governed data
  3. Understand: schema, quality issues, and data structure detected automatically
  4. Enrich: optionally add AI-generated columns (firmographics, categories, signals)
  5. Analyze: suggested analyses ranked by relevance; you decide which to run
  6. Output: export enriched data and insights, ready for decisions

Core computation is deterministic: exact, reproducible, and run over the full dataset rather than a sample.

AI's role is enrichment and suggesting what to look at next. It never touches the underlying math, so the numbers themselves carry no hallucination risk.

That matters because every answer needs to hold up after the fact. Each one traces back to its source, down to the clause or cell it came from, terms are defined consistently, and you can reconstruct what the data showed as of any point in time.

How we handle your data

We built TabInt for firms that work with confidential material, and we treat your documents and data accordingly:

  • Your data is never used to train, fine-tune, or improve any model. It works only for you.
  • Data is encrypted in transit and at rest.
  • Access is controlled with per-customer tenant isolation: your data is separated from every other customer's, and you decide who inside your organization can see what.
  • Your data is retained only as long as you need it, and you can delete it at any time.

We are completing SOC 2 Type II. For the full security detail, reach us at security@tabint.ai.

Why now?

Excel tops out well before enterprise-scale data does. Language models, meanwhile, aren't built to operate reliably over millions of rows directly. That gap shows up most clearly in private markets, where new capital and new AI tooling are arriving faster than the data underneath them is getting organized. What limits firms here is the data estate: whether the numbers are owned, defined, and consistent across the firm.

At the same time, expectations are shifting: people want systems that guide the analysis and produce defensible answers without requiring specialized database expertise.

TabInt is built for that shift: a consistent experience whether your dataset has 50 rows or 5 million.

Who you're working with

TabInt was founded by Daniel Hellwig and Goran Karlic, who together authored a book on distributed-ledger technology and have spent years inside the data estates of financial institutions. We kept hitting the same problem: AI efforts stalling not on the models, but on data nobody owned, defined, or could trace. We built TabInt to fix that, starting where the problem is sharpest, in private markets.