UUAMNI Incorporated · Delaware C-Corp
UUAMNI Energy · CAC Nigeria Registered
Tier III Lagos Datacenter
BIS Export-License Path Active
"Ulunma, ụgbọ ala m na-eweta Ìhè!"
Building the labor, data, and compute infrastructure that AI structurally requires.
In low-resource African languages, frontier model safety collapses up to 55% compared to English. Meanwhile, English-only training has hit an intelligence ceiling that more of the same data cannot break.
UUAMNI closes the floor and breaks the ceiling.
01 / The Dependency
Every AI system on earth runs on human feedback. The question is where that human input comes from — and who controls the supply.
Safety refusal collapse from English to Igala — frontier-model alignment does not transfer to low-resource African languages.
Sovereign A100 80GB GPUs at a Tier III Lagos datacenter — locally owned, on Nigerian soil.
02 / The Stack
Three layers, one site, zero external dependencies. If any layer is separated, someone else controls whether the teaching continues. Together, they make the generative site self-sustaining.
The cultural-IP layer that produces preference data no remote workforce can produce. UUAMNI recruits the population of native Igbo speakers with the cultural depth to score register, honorifics, proverb interpretation, and dialectal nuance — work that requires literary fluency, not training-on-the-job. Pipeline anchored on university linguistic departments, the Igbo cultural-scholar network, and the African NLP community via Masakhane and Lacuna Fund connections. Margin structure flows from data-product pricing, not from labor arbitrage. The labor universe Surge AI and Scale AI cannot recruit is the labor universe we sit on top of.
The first open Igbo-origin DPO dataset: 20,000 strong-preference pairs across eight tracks — translation, monolingual generation, cultural QA, safety and refusal, register and honorifics, code-switching, and proverb interpretation. Native-origin Igbo, not translated English. CC-BY-4.0 public sample at launch, commercial tier for frontier labs. The data layer that closes the safety floor and opens the cross-lingual ceiling at once.
Sovereign, locally-owned A100 capacity at competitive, locked rates — wholesale and reserved tiers for labs, banks, and regulated institutions. Fine-tuned models as a service (MaaS) for banks and regulated institutions ride the same fleet: data never leaves Nigeria, NDPA-compliant by architecture. Co-located with the workforce, data sovereign on Nigerian soil.
Fair wages flowing directly into local Nigerian economies. Skills development creating long-term careers, not gig work. Long-term roadmap: solar-powered, owned-and-operated GPU capacity on the African continent. Excess capacity distributed to communities, hospitals, and schools as the build matures.
03 / Work With Us
One stack, four ways in. Tell us which one you are.
Native-origin African-language preference data with an evidence package, plus sovereign compute to train on. Talk to us →
Fine-tuned fraud, credit, and document models on GPUs in Lagos. Your data never leaves Nigeria — NDPA-compliant by architecture. Explore MaaS →
Fair-wage, expert-level language work — register, proverb, and cultural reasoning. Careers, not gig piecework. Join the workforce →
Pre-seed: native preference data, African-owned compute, one integrated stack. Request materials →
05 / Why Now
Meta paid $14.3B for access to human feedback. The constraint is not compute. It is genuine human intelligence at scale. The companies that secure this input win. The ones that don't, stall.
No intelligence is formed in isolation. Every human civilization evolved through contact with difference. AI trained without African languages, reasoning patterns, and perspectives is not a universal intelligence. It is a mirror admiring its own reflection. The structural gap is growing, not shrinking.
RLAIF—AI evaluating AI—produces model collapse. Systems trained on their own output degrade. The need for genuine human signal is not a phase. It is permanent.
Nigeria's NITDA data classification framework moves to require local processing for regulated data (health, financial, government). Foreign-hosted AI fails the test. UUAMNI is the only company building the workforce, the data, and the Nigerian-hosted compute as a single integrated stack.
Imo State alone has 65,000 technically trained graduates through SkillUpImo, with the literacy and English-fluency foundation that supports advanced annotation training. UUAMNI builds on top of that base with the additional cultural-IP layer — scholars, native-fluency annotators, and PhD-level linguistic expertise — that frontier-lab preference data actually requires.
07 / The Founders
Two engines: an operating company and a public-intellectual research engine, compounding in parallel.
Co-founder & CEO
Nigerian-American, bases in NYC and Lagos. 12+ years technical depth: four years at Microsoft, then 8+ years in capital-markets fintech supporting the equity and retail bookbuilding platforms major US investment banks rely on to run IPO order books, through multiple M&A events. Adversarial detection, incident response, 350+ server production support, AWS migration of five integrated products. Leads UUAMNI's commercial, capital, and Nigerian-operations stack.
Co-founder & architect of the structural-necessity thesis
PhD Princeton Theological Seminary, Spring 2026. Architect of UUAMNI's structural-necessity thesis: if AI is ever to be a universal intelligence, it must have access to ways of reasoning beyond what it is currently trained on. That starts with Africa. Her research program — ontology, model collapse, and the limits of alignment and synthetic training — births the ideas at the root of UUAMNI's products and pushes the conversations that put the company at the frontier of today's AI discourse. Her position paper, The Closure Problem in Alignment, is in submission.