UUAMNI Incorporated · Delaware C-Corp
UUAMNI Energy · CAC Nigeria Registered
Tier III Lagos Datacenter
US Export License Approved · June 2026
"Ulunma, ụgbọ ala m na-eweta Ìhè!"
Building the labor, training data, evaluation, and compute infrastructure that AI structurally requires to evolve across the globe.
UUAMNI exists to expand the native-language reasoning available to artificial intelligence.
Starting with African languages. Igbo first.
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 ~90% in English to 35–55% in Yoruba, Hausa, Igbo, and Igala (worst case shown) — frontier-model alignment does not transfer to low-resource African languages.
Sovereign A100 80GB GPUs licensed for export, staged for a Tier III Lagos datacenter. Built to be owned on the continent, not rented from it.
Published Evidence · July 2026
Ask a frontier model to explain an Igbo proverb in English, then in Igbo — it reasons from a different cultural frame each time. IlùBench is the first benchmark measuring cultural register switching in an African language: a published protocol, attested probes, a scoring rubric where every axis is human-scored, and multi-model evidence. Reproduced on three frontier models; a fourth open-weight model answered Igbo prompts in fluent Yoruba, a failure class nobody had documented. Run it on any model yourself with our open-source runner.
02 / The Stack
One factory, three surfaces, one site. If any layer is separated, someone else controls whether the teaching continues. Each layer finances and improves the next. Data is finite. A data factory compounds.
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 linguistic nuance — work that requires literary fluency, not training-on-the-job. Cultural judgment has no credential to verify, so we built the verification system that makes it auditable: every dataset ships with the reliability statistics to prove it. Margin structure flows from data-product pricing, not from labor arbitrage. The labor universe incumbent data vendors cannot recruit is the one we sit on top of.
The first open Igbo-origin DPO dataset: 20,000 strong-preference pairs across eight tracks, from translation and cultural QA to safety and refusal. Native-origin Igbo, not translated English. Every release ships with benchmark evidence, measured against IlùBench, our published evaluation layer. 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 in-country 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 its stake in Scale AI — 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 central bank now requires every bank, fintech, and payment processor to store and manage all payment transaction data inside Nigeria by January 1, 2027, with sanctions for non-compliance — and 90%+ of these institutions run on foreign cloud today. Compute follows the data. NDPA 2023 and the national cloud policy extend local-processing requirements across regulated sectors. No one else holds 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, based in New York City, operating between NYC and Lagos. 13+ years building enterprise infrastructure for financial services: four years at Microsoft, then nine-plus years in capital-markets fintech supporting the platforms effectively every major US investment bank relies on to run IPO order books. Leads UUAMNI's technical, 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.