We design, train, and operate AI and machine learning systems for African contexts, including model selection, training data, and ongoing evaluation.
Production AI for African problems.
PANEOTECH designs, trains, and operates AI and machine learning systems for institutional clients. The practice combines applied machine learning depth with the operational discipline of running ML in production, in environments where accuracy is non-negotiable and traceability is required. We build AI that holds up under audit, not AI that holds up in a demo.
Across the AI and machine learning stack.
Applied ML
Classical and deep learning model development for forecasting, classification, anomaly detection, and the structured prediction problems institutional clients actually face.
Generative AI
LLM-based agent platforms, retrieval-augmented generation systems, document intelligence pipelines, and the guardrails that make generative AI safe in regulated contexts.
Computer vision
Image and video analysis pipelines for satellite imagery, surveillance, document processing, and the operational use cases that benefit from machine perception.
NLP and language
Text classification, named entity recognition, multi-language pipelines including African language support, and the language engineering institutional content workflows require.
Model evaluation
Rigorous evaluation frameworks, fairness audits, drift detection, and the ongoing assurance work that distinguishes models that get deployed from models that should.
Production ML
Model serving, feature stores, monitoring and observability, and the MLOps disciplines that keep deployed models reliable across the full lifecycle.
AI engineered for institutional accountability.
Most AI systems fail in operations.
An AI model is easy to train and difficult to operate. The hard parts are the parts that come after the demo: model drift, edge cases that surface only in production, the audit obligations institutional clients have, the cost discipline that keeps inference economics from breaking the business case. Our practice is built around those hard parts, because they are what determines whether AI actually delivers value.
