AI-ready by design
For over a decade, we've built the data layer that many institutional investors rely on.
That same layer is now the foundation of their AI projects: with natural-language queries and AI agents,
their teams can put StarQube's data layer, backtests, risk models, analysis and optimizations to work simply by asking.
The foundation
No AI model can compensate for data that is unchecked, ambiguous, undated or ungoverned. Before an agent can rely on a single number, the data beneath it must meet four conditions. StarQube takes care of all four before any AI model is involved.
A data layer, referential included, is only as good as what flows into it. Errors not caught on the way in reach the agent as facts.
Automated quality checks, resolution rules and exception handling validate every value before any agent uses it.
"What is our exposure to this company?" means resolving parent companies, subsidiaries, multiple identifiers and fund look-through. Without that structure, an agent guesses from table names — plausibly, and wrong.
A unified referential resolves every entity, in any asset class, to a single identity, and every field is documented and defined. An agent always knows exactly what it is looking at.
Vendors restate their data. An agent working with today's values uses information nobody had at the time: the backtest looks excellent, but the live strategy underperforms.
Native point-in-time history, vendor restatements included, so research, backtests, reporting and agents all run on data as it was known at the time.
Vendor licences restrict which data may be used by an AI model or leave your firm, and regulators expect you to reconstruct the inputs behind any decision. Both require permissions and lineage at field level, not at database level.
Granular permissions by data field: an agent never sees more than the person (or process) using it is allowed to see. And lineage from raw vendor value to trade order can be found live, not in an overnight report.
Other data platforms leave all four for you to solve, and they answer at report speed, not agent speed.
Built on that foundation
With those four conditions met, agents — ours or your own — can be trusted with real investment data.
Anyone on your team can work with our system in natural language: ask questions about a portfolio, have an agent configure a strategy or a screen, or get a clear explanation of complex analytics.
Through MCP (Model Context Protocol), the open standard for connecting AI agents to business systems, your own agents and tools connect directly to StarQube's data and calculation layer. They are subject to the same permissions and governance as everyone else on the platform.
Why StarQube
Ask an AI model the same question twice and you may get two different answers. That is why every agent works inside StarQube's framework: whatever it creates, from a strategy to a screen, is saved as a versioned object that gives the same result every time it runs. Every output is traceable, never a black box.
To answer a single question, an agent runs many small queries in quick succession, which soon become slow on a database built for reports. StarQube's compact proprietary store answers most queries in a fraction of a second, so an agent can explore several paths before answering, in a reasonable time.
The whole layer can be deployed in your own datacenter or cloud tenant, with local AI models where required, so confidential data never has to leave your infrastructure. We do not use your data to train AI models.
Our AI layer is not an experiment: it runs on the same data platform that institutional investors already use every day.
What's hard to replicate is not the AI model, but what makes its answers trustworthy: calculations that run on proven engines, data that is checked and point-in-time, and a deep understanding of how asset managers actually work. We've been building exactly that for over a decade.
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