89% of safety risk models in use today were trained on data from fewer than 20 countries. The other 195 countries exist in these models as gaps, assumptions, or extrapolations from populations that share none of their context. That is not a technical limitation. It is a data collection problem. And it has a cost.
Visa officers making decisions about applicants from Jamaica, Nigeria, or Colombia have no reliable safety infrastructure to consult. They default to national-level proxies that tell them nothing about the individual. Insurance underwriters pricing risk for policies in emerging markets use models calibrated on Western populations. Employers conducting background checks on candidates from 140 countries cannot access verified safety profiles through any existing channel.
The data to fix this does not need to be invented. It needs to be collected, structured, and made queryable. That is what Maestro AI Labs built with Global Safety Score and the Meridian World Models platform beneath it.
What Global Safety Score Does
Global Safety Score is a portable safety identity system built on Meridian, Maestro AI Labs' world model infrastructure covering 140 or more countries. It assigns individual-level safety signals to people who currently have no verifiable safety record in the systems that govern their movement, employment, and access to services.
The signal is built from ground-collected data: location safety indexes, community safety records, mobility patterns, institutional risk assessments, and cross-border movement data. The system is consent-first and individual-controlled. The person scored can see their own profile and submit corrections. The output is a score and structured safety profile that can be queried by any institution that makes decisions about human movement.
"Geography is context, not destiny. Global Safety Score reflects an individual's actual verifiable history, not the statistical profile of the neighbourhood, country, or demographic group they belong to."
The Buyers and the Market
Every institution that makes decisions about people without reliable safety data is a potential buyer. The list is long.
Governments processing visa applications, managing border control, assessing internal displacement, and targeting development programs all require safety evaluations of individuals from regions where no reliable safety infrastructure currently exists. Seven CARICOM states are in active API scoping conversations with Maestro AI Labs.
Insurance companies pricing travel, life, and health insurance underwrite risk based on location and population safety assessments. Current models are calibrated on Western populations. Extending accurate underwriting to emerging markets requires accurate safety data from those markets. That data does not currently exist. Global Safety Score provides it.
Multinational employers conducting background checks on candidates from 140 or more countries cannot access verified safety profiles through existing channels. Global Safety Score gives them an API endpoint that covers the gap.
Development banks allocating resources based on safety and stability assessments operate with incomplete data. Meridian's world models provide the signal they currently lack and cannot generate internally.
Addressable Market
The global insurance market is $6.3 trillion. Emerging market insurance penetration sits below 3% of the global average, largely because accurate risk pricing is impossible without accurate safety data. Every percentage point of emerging market insurance penetration unlocked by better safety data represents hundreds of billions in addressable premiums.
The global background check and identity verification market is $8.4 billion and growing at 14% annually. The gap in this market is accuracy for non-Western populations. Global Safety Score addresses that gap directly.
Government border and security technology spending exceeds $45 billion annually worldwide. The data infrastructure that makes cross-border movement decisions more accurate is a direct replacement for systems currently producing incorrect outputs. Seven active government API scoping conversations represent the starting point, not the ceiling.
Revenue Model
Global Safety Score generates revenue through B2B API access, government contracts, insurance licensing agreements, and data licensing to development finance institutions. The per-query model scales with usage. Government contracts provide baseline recurring revenue.
The revenue potential scales with accuracy in a compounding way: the more people scored, the more accurate the model becomes, the more institutions integrate it, the more queries the API processes. That is a flywheel, not a linear revenue curve. Every institutional integration deepens the data asset and increases the cost for any competitor to match the coverage.