The products.
The numbers.
Deep dives into each Maestro AI Labs product: the problem it solves, the market it addresses, the revenue it unlocks, and the questions people ask most often.
Credit Garden: The $380 Billion Lending Gap That Existing AI Cannot See
1.7 billion people are credit invisible. Not because they lack financial history, but because every model that exists was built without their data. Credit Garden converts the signal that already exists into scores that work in Kingston, Kigali, and Karachi.
Global Safety Score: What a Portable Safety Identity for 4.2 Billion People Is Worth
89% of safety risk models were trained on fewer than 20 countries. Meridian covers 140+. Every government, insurer, border agency, and employer making decisions about human movement is a buyer.
OYA AI: 72 Hours of Hurricane Warning Instead of 6. What That Is Worth.
The same prediction engine that saves lives generates the climate risk data that reinsurers lose $90-130 billion annually because they cannot accurately price. OYA AI is both things at once.
AI Agents for Regulated Entities: Why Compliance Cannot Be an Afterthought
Governments, central banks, and compliance-heavy institutions cannot deploy agents that guess confidently and explain nothing. Harmonics was built for the sectors where the audit trail, escalation path, and human override are the product.
Harmonics: Why AI Agents Without Regional Context Fail in the Field
An agent that does not know what a SUSU is cannot assess creditworthiness in Jamaica. Harmonics agents are trained on regional knowledge graphs, not system prompts. 87% task accuracy. Under 90 days to deploy.
AI Playbook: Why the 94% Implementation Rate Is the Product
The enterprise AI implementation average is 23%. The AI Playbook hit 94% because it was built from scratch for the businesses that enterprise guides were never written for. 47% of Caribbean and LATAM SMEs have no AI strategy.
Data Archaeology: The Field Work That Makes Every Other AI Product Possible
92% of Maestro's 2.3 million records exist in no other AI training set. They were not scraped. They were excavated: pre-digital government archives, SUSU records, 47 indigenous language datasets. This is the moat.