A new type of financial intelligence. See the whole picture.

TypeDB gives firms a reasoning database built for connected, regulated data. It models financial products, instruments, accounts, and transactions semantically.

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Complex financial products re-imagined

Financial systems run on either OWL, or complex relational tables and pipelines held together by ETL. Data is often siloed, and extracting insight is laborious and tooling is inadequate for purpose.

What’s missing is semantic depth: a shared language of how these entities relate and what that actually means.

We enable you to turn financial intelligence into a USP for your business. In a market where finding an edge can mean the difference between success and failure, the power of data intelligence cannot be overstated.

Semantic precision

Define complex financial products with roles and constraints, so “ownership,” “exposure,” and “risk” have exact meaning.

Connected analysis

Query across nested relationships to uncover indirect risk, linked accounts, or related counterparties.

Auditable functions

The inferences made by TypeDB functions are always transparent, explainable, and governed by schema logic.

Why You Should use a DB with a Semantic Layer

Most databases store structure, not meaning. Learn how semantic layers preserve data integrity, enable query safety, and simplify complex systems.

Read article

TypeQL ACM Paper

Read the preprint of the paradigm-defining paper behind TypeQL, due to be published in Proceedings of the ACM on Management of Data.

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Queries as Functions

Functions provide powerful abstractions of query logic, which can be nested, recursed, or negated, and they natively embed into TypeQL's declarative patterns.

Read docs

Model financial products precisely

Financial products are complex: a fund owns a derivative; a derivative references an underlying; a counterparty executes on behalf of a client. TypeDB lets you express these directly in the data model.

  • Define instruments, trades, parties, and exposures as typed entities with formal relationships.
  • Capture multi-role relationships (e.g., “issuer,” “guarantor,” “beneficiary”) without losing fidelity.
  • Extend the schema safely as regulations and products evolve.

Value: Data models that mirror real-world financial logic, not approximations of it.

TypeDB Studio
A visualization of the Social Network TypeDB schema in TypeDB Studio

Risk propagates through relationships. TypeQL's PERA model and nested relations can surface indirect exposure that would take hours of joins and code in a traditional stack.

  • Discover which counterparties are linked via shared holdings or obligations.
  • Propagate stress scenarios automatically through dependency chains.
  • Detect patterns like layered ownership or fund-of-fund exposure.

Value: Visibility into second and third- order risk, not just direct exposure.

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