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Claims fraud reduction · NHIS Nigeria · 9 months
AI Services — Pillar II
We design and deploy AI systems across health, agriculture, financial services, climate finance, and government. We don’t advise and leave — we embed, deploy, and build internal capability.
Claims fraud reduction · NHIS Nigeria · 9 months
Addressable SMEs via Caspreâ„¢ credit model
Underwriting intelligence · ZEP-RE Pan-Africa
The Engagement Model
Each entry point delivers standalone, measurable value. You do not need to commit to the full architecture to begin.
Scroll or click a point to explore
Entry point for all
Timeline
2–4 wks
Output
Diagnostic Report + AI Roadmap
Scope
AI readiness diagnostic · data maturity · gap mapping · executive briefing
Blueprint phase
Timeline
4–8 wks
Output
AI Architecture Blueprint + Governance Framework
Scope
AI strategy · use case prioritisation · governance charter · data architecture blueprint
Build & deploy
Timeline
8–12 wks
Output
Working AI solution + Impact Dashboard
Scope
Priority use case build · model development · MLOps setup · stakeholder training
Ongoing sovereignty
Timeline
Ongoing
Output
Sovereign institutional AI capability
Scope
Full CoE management · continuous model improvement · IP licensing · AI governance
Ready to begin your AI transformation?
Differentiation
We are one of a small number of organisations on the continent with integrated capability across talent development, enterprise AI delivery, and proprietary AI product IP.
Typical market approach
× Courses only, no delivery pathway
AICE Africa
✓ Proof-of-Work, delivery squads, employment
Typical market approach
× Typically resells or adapts 3rd-party tools
AICE Africa
✓ DataInviz™, Caspre™, Conversational Engine™
Typical market approach
× Less common; advisory only
AICE Africa
✓ Active sovereign AI mandate delivery
Typical market approach
× Often VC-dependent or donor-funded
AICE Africa
✓ Profitable, delivery-backed operating model
Typical market approach
× Single capability focus
AICE Africa
✓ Three-pillar integrated architecture
What We Build
A project ends. A Centre of Excellence endures. When AICE Africa works with a country, institution, or enterprise, we architect a permanent capability – not a one-time deployment.
Every AICE Africa Centre of Excellence is designed around four interdependent pillars that ensure sustainable, sovereign AI impact long after our initial engagement concludes. The institution owns it. The people run it. The intelligence compounds.
Training and developing AI-ready talent within the institution - engineers, analysts, leaders, and change champions.
Co-designing an AI roadmap and governance framework that is specific to the institution's mandate and data landscape.
Deploying working AI solutions - custom models and platforms that generate measurable outcomes from day one.
Measuring, evolving, and compounding the intelligence advantage over time - turning early wins into scaleable transformation.
For governments and public institutions
A sovereign AI capability anchored within a government ministry or national agency – with policy frameworks, public sector AI deployment, and national talent pipelines. AICE Africa has architected national AI strategies across East and Southern Africa.
For universities, research institutions & TVETs
Embedding AI curricula, research capacity, and Proof-of-Work delivery pipelines within academic institutions – linking student talent directly to enterprise deployment through AICE Africa's practitioner model.
For corporates, banks, insurers & industry
Building a permanent internal AI capability – from leadership upskilling to custom model deployment to AICEaaS managed transformation. We do not just consult; we leave a Centre of Excellence behind that the enterprise owns and runs.
Africa needs architects.
We are those architects. Begin with an Intelligence Audit — a 2–4 week assessment that delivers your AI Maturity Score, gap analysis, and a personalised roadmap.
Does your organisation have a documented data strategy that covers collection, governance, and quality standards?
Do you have dedicated AI or data science talent in-house, or a clear plan to build it?
Has your organisation defined an AI ethics policy or governance framework?
Is your data infrastructure (cloud, APIs, pipelines) ready to support AI workloads?
Do you have a clear AI strategy aligned to your institutional objectives for the next 3 years?
Does your organisation prioritise owning its AI models and data, rather than relying entirely on third-party platforms?
Are there executive-level champions actively driving AI adoption within your organisation?
Have you mapped specific use cases where AI could transform your operations or services?