Our services
Practical data, AI and automation solutions.
From data strategy to intelligent automation, we help organisations across East Africa turn data into measurable value.
Industries
Solutions for East Africa's core sectors
Practical data, AI and automation solutions shaped by real operating environments.
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Service
We take machine learning from prototype to production with automated pipelines, monitoring and retraining, so your models stay accurate long after launch day.
Practical mlops solutions designed to solve real business problems.
A clear and structured process to deliver mlops solutions that create value.
Most machine learning value is lost after launch, when data changes and nobody notices. MLOps keeps models honest, auditable and improving.
We work with leading platforms and choose what fits your stack, budget and skills.
Banking
A bank's fraud model was retrained by hand twice a year and slowly lost accuracy. We built automated pipelines, drift monitoring and safe weekly releases.
Read full case studyQuick answers to common questions about our mlops services.
The practices and tooling that make machine learning reliable in production: versioning, testing, deployment, monitoring and retraining.
Yes. Many engagements start by productionising or stabilising models a team has already built.
Either. We design for Azure, AWS, Databricks or on-premise Kubernetes depending on your data residency needs.
We monitor input data distributions and live accuracy, and alert when either moves beyond agreed thresholds.
We favour open standards such as MLflow and containers, so models stay portable.
A first production pipeline for one model typically takes 6 to 10 weeks.
Yes. We document lineage, approvals and model risk controls in line with banking and insurance expectations.
Stuck between prototype and production?
Book a free consultation and we will review one model and the path to running it reliably.
