RubiLabs Launches Lapis, a Platform to Predict Global Risks
RubiLabs' Lapis risk analytics platform draws on more than 40,000 data points to flag regulatory, geopolitical and economic risks before they cause losses.

RubiLabs is building Lapis, a risk analytics platform designed to warn users about regulatory, geopolitical and economic threats before they turn into financial losses. The startup presented the product at Monthly Pitch, a closed event where early-stage startups meet investors each month.
How Lapis works
Users start by uploading files related to the risk they want to examine, from which Lapis builds a baseline. The platform then measures how much risk external events could create for the uploaded information, data and profiles, and how far that risk could spread. If it finds a potential for risk formation or contagion, it displays a risk score, alerts and recommended actions.
According to RubiLabs CEO Juan Markus, turning text into a risk score takes four steps: cause and effect, fact checking, verification of assumptions, and risk assessment. Combining them, the company says, delivers information that supports the user's decisions.
Why it matters
The pitch rests on a familiar problem: global events can cause large economic losses almost overnight. The Monthly Pitch editors point to the impact of the conflict between the United States and Iran on the weak yen, rising interest rates and higher prices. Financial institutions spent 5 trillion US dollars on risk mitigation in 2025 alone, yet risk has not been fully eliminated.
The reason, according to the pitch, is that risk management remains reactive and relies on past data and events. Lapis aims to make it proactive. The platform already connects more than 40,000 data points worldwide, including hard-to-obtain sources such as interviews, podcasts and public opinion polls.
RubiLabs has not disclosed funding, customers or a launch timeline in the material available. The test for Lapis will be whether its alerts arrive early enough, and are specific enough, to change decisions at financial institutions before losses occur.
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