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Machine Learning & AI from Nairobi, Kenya

Practical AI for the data you actually have.

We built ACTS ML, a no-code machine learning platform used by research scientists at the African Centre for Technology Studies, so we understand both the power and the practical limits of AI in production. Real-world datasets are smaller, noisier, and less well-labeled than the benchmark datasets you see in research papers. We build AI solutions that work on the data you actually have, not the data you wish you had.

What this means for your organization

Decisions supported by models built on the data you actually hold, with human review where it matters and monitoring that catches drift early.

What We Do

Everything included in our Machine Learning & AI service.

No-Code & Low-Code ML Platforms

We build tools that let domain experts, agronomists, public health researchers, financial analysts, train and deploy models without writing code. Our experience building ACTS ML taught us how to make ML accessible to users who understand their problem domain but not the mathematics behind the model.

Predictive Modelling

Loan default prediction, customer churn, crop yield forecasting, demand planning, we build supervised learning models trained on your historical data. We handle the full pipeline: data cleaning, feature engineering, model selection, validation, and deployment.

Natural Language Processing

Text classification, sentiment analysis, named entity recognition, and document extraction, including for under-resourced languages and mixed-code text such as Swahili, Amharic, and Sheng, which benefit from models tuned to them.

Recommendation Systems

Product recommendations for e-commerce, content personalization for media platforms, and matching systems for marketplaces. We build recommendation engines that handle the cold-start problem common to platforms with smaller user bases.

AI Feature Integration

Embedding AI features into your existing web or mobile application: smart search, automated document processing, anomaly detection in transaction data, or intelligent form completion. We handle the ML engineering and the API integration.

Data Pipeline & MLOps

ML models that aren't retrained on fresh data degrade. We build automated data pipelines, model monitoring, and retraining schedules so your AI features stay accurate as your business evolves, without manual intervention.

Why Afriq Silicon

What makes our Machine Learning & AI service different.

1

We built a real ML platform, not a proof of concept

ACTS ML (actsml.com) is a production system used by research scientists to build machine learning models without writing code. This wasn't a demo or a proof-of-concept, it's a live platform with real users solving real research problems. We know what it takes to make ML accessible and reliable.

2

We design for messy, real-world data

Missing values, small sample sizes, class imbalance, and inconsistent labelling are the norm in operational datasets, not the exception. We build models and evaluation frameworks built around the data you actually have.

3

We recommend AI where it earns its place

AI is genuinely useful for specific problems, and we are direct about which ones. Where a simpler rule-based approach will do better on your data, we will say so and build that instead. The aim is the result you need, at a cost that stays worth it.

Frequently Asked Questions

Common questions about our Machine Learning & AI service.

Do we need a large dataset to use machine learning?
It depends on the problem, simpler models can work from a few thousand labeled examples while deep learning needs more, and we recommend the right approach after reviewing your data.
How do you handle data privacy with AI projects?
We handle personal data in compliance with Kenya's Data Protection Act 2019 and, where applicable, GDPR, using data minimization, anonymization, audit trails, and clear documentation.
Can you help us understand what AI can realistically do for our business?
Yes, we often start with an AI readiness assessment of your data, processes, and problems, ending with a prioritized list of AI opportunities specific to your business.
What happens when the AI makes a wrong prediction?
Since all models make errors, we design around them with confidence thresholds, human review queues, audit trails, and ongoing monitoring for performance degradation.

Related Projects

Real work we have done in this service area.

Engineering Kilele Hub’s Behavioral Messaging Engine
project

Engineering Kilele Hub’s Behavioral Messaging Engine

At Afriq Silicon, we don’t just write code; we build tools that change how businesses communicate. Our work on Kilele Hub is a perfect example.

  • TypeScript
  • JavaScript
  • Vue.js
  • Go
  • Directus
 What if anyone could build a machine learning model?
project

What if anyone could build a machine learning model?

Afriq Silicon built a no-code ML platform for ACTS, giving Africa's researchers and field practitioners the power to run machine learning without writing code.

  • Astro
  • Vue.js
  • TypeScript
  • CSS

Further Reading

Articles from our team on topics related to Machine Learning & AI.

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How Fintech Automation Is Reshaping African Finance

From mobile money to CFO automation, fintech is quietly rewiring how African businesses handle money. Here is what is changing, why now, and where to start.

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