AI & AUTOMATION
AI that works for Africa
Custom agents, RAG applications, fraud-detection models, and AI governance — built on African data and regulatory reality, not imported from markets where the financial and language context is fundamentally different.
WHAT'S INCLUDED
From strategy through to production
We own the full model lifecycle and the business outcome — a readiness assessment and roadmap, then the applications, models, and automation that deliver it, then the MLOps that keeps it reliable in production.
Responsible AI is not a phase at the end. Every model is tested for bias before it ships, documented with a model card, and aligned to the Central Bank of Kenya guidance and the Data Protection Act 2019.
- AI strategy & readiness — maturity assessment, use-case prioritisation, and a 90-day roadmap
- Custom LLM & RAG applications — private document intelligence and domain-specific fine-tuning
- AI agents & workflow automation — multi-step autonomous agents for document and back-office work
- Fraud detection & AML models — transaction scoring and anomaly detection on East African data
- MLOps & governance — model registry, drift monitoring, explainability, and bias audits
90 days
From a readiness session to a buildable roadmap — three high-value use cases scoped and prioritised
HOW IT WORKS
From readiness session to a governed model
A clear, four-step engagement — you know what happens at each stage and what you get out of it.
STEP 01
Assess
We run a readiness session across your data, systems, and goals — then identify the highest-value use cases and give you an honest view of what is buildable in 90 days.
STEP 02
Build
We build the application, agent, or model against your real data — RAG over your documents, a fraud model on your transaction history, or automation for a specific back-office workflow.
STEP 03
Govern
Before anything reaches production it is tested for bias across gender, ethnicity, and geography, documented with a model card, and aligned to CBK guidance and the Data Protection Act.
STEP 04
Operate
We wire in MLOps — a model registry, drift monitoring, explainability reporting, and CI/CD — so the model keeps performing as the data around it changes.
RELATED PRACTICES
Where teams go next
START HERE
Ready to put AI to work?
Start with a readiness session — we assess your data, identify three high-value use cases, and give you an honest view of what is buildable in 90 days. No obligation.