We work where a system's failures fall unevenly on people who cannot appeal them. Depth in a domain is what lets us build a test set that resolves the populations that matter, so we go deep in five rather than shallow across everything.
The five below carry equal weight. They are the domains where AI is already making decisions at scale across the continent, and where a wrong decision is difficult or impossible for the affected person to contest.
Credit scoring on alternative and behavioural data, digital lending and buy-now-pay-later underwriting, fraud and anti-money-laundering screening, mobile money risk engines, insurance pricing, and the automated eligibility decisions layered on top of them.
Computer-aided detection on chest X-ray and ultrasound, symptom checkers and triage tools, maternal and neonatal risk scoring, diagnostic support in pharmacy and primary care, and supply forecasting for essential medicines.
Biometric identity and verification, social-protection targeting, benefit and subsidy eligibility, document and claims processing, tax and customs risk scoring, and the language systems used to deliver public services.
Satellite-derived flood and drought mapping, early-warning triggers, parametric insurance indices, crop yield and pest forecasting, farm advisory systems, and the exposure and land-cover databases underneath them.
Large language models answering procedural, agricultural, health and legal questions for people with no other source of advice, in English, Pidgin, and the major African languages. Increasingly embedded in government helplines, banking assistants, and agricultural extension services.
Depth in a sector is not depth in every system within it, and none of these domains is uniform across the continent. Where an engagement needs domain or country knowledge we do not hold, we say so at scoping and either bring in a named external specialist, whose role and interests are disclosed in the finding, or decline the work.