Software & Machine Learning Solutions

Proximaai is a technology startup specializing in software development and machine learning model development, with a focus on ethical, scalable, and real-world AI applications.

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About Proximaai

Proximaai is an early-stage technology company focused on building high-quality software systems and training machine learning models that solve practical problems. We work at the intersection of software engineering, data science, and applied AI.

Our mission is to develop reliable, transparent, and responsible AI solutions, with particular interest in emerging markets and underserved domains.

What We Do

Our core technical capabilities

Software Development

Design and development of web applications, APIs, and backend systems using modern frameworks and cloud-native architectures.

Machine Learning Model Development

Development and training of machine learning models, including computer vision, predictive analytics, and data-driven decision support systems.

AI Integration & Deployment

Integration of trained models into production systems via APIs, dashboards, and lightweight portals suitable for low-resource environments.

Focus Areas

AI for Health

Early-stage work on AI-assisted clinical decision support systems, with an emphasis on ethical data use and local relevance.

Data-Driven Systems

Building platforms that collect, process, and analyze data to support better operational and strategic decisions.

Projects & Research

Applied research and early-stage projects focused on real-world impact

Africa-First AI for Early Cancer Detection

Status: Concept & Pilot Preparation

An open-source research initiative to develop machine learning models for early breast cancer risk detection, trained on ethically governed clinical data from African women.

  • Focus on clinical decision support
  • Hospital-led data governance
  • Designed for low-resource settings
Secure AI Model Deployment Platforms

Status: In Development

Development of lightweight software platforms for deploying and managing machine learning models via APIs and web portals, with a focus on security, scalability, and responsible use.

  • API-based model access
  • Simple verification portals
  • Cloud-native architecture
Our Research Principles
  • Ethical approval and informed consent first
  • Transparency and reproducibility
  • Human oversight over all AI-assisted decisions
  • Open-source release where appropriate

Our Approach

  • Problem-first, not technology-first
  • Strong emphasis on data governance and ethics
  • Open standards and open-source where appropriate
  • Designed for scalability and real-world deployment

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