Logistify
Logistify is making inventory management seamless
AI | KenyaWhat they do:
Logistify AI has developed an AI-powered automated inventory verification platform that integrates with a factory’s existing CCTV cameras and ERP system, enabling inventory detection, identification, tracking, and counting using computer vision. This innovative solution addresses the challenges of inventory losses due to theft or incompetence and reduces labor costs associated with manual verification processes.
Why we invested:
The global manufacturing industry, valued at $16 trillion and comprising 17% of global GDP, presents a significant opportunity. Logistify AI focuses on the $120 billion inventory verification segment within this market, while in Africa, where manufacturing constitutes 15% of GDP exceeding $500 billion, the company anticipates high growth potential, particularly due to rising AI adoption, expanding 5G connectivity, and model optimization techniques facilitating its expansion across the continent.
With a clear global demand and Africa's favorable conditions for technology adoption, Logistify AI is primed for rapid expansion, starting with the African market.
The Founder:
Daniel's interests lie in developing principled probabilistic models for scenarios with limited training data, leveraging expertise from subject-matter specialists and contextual information. He focuses on flexible Bayesian models like Gaussian and Dirichlet processes, along with data-efficient methods such as Bayesian optimization and model-based reinforcement learning. Application domains include last-mile distribution, anomaly detection, financial risk analytics, and urban mobility.
Daniel is the creator of Pymc-learn, a Python library for probabilistic machine learning. He is also a PhD Candidate in Transportation Engineering at UNLV, where his research concentrates on nonparametric Bayesian methods for developing flexible statistical models for traveler-behavior analytics. His work at the intersection of human activity-based modeling (ABM) and Bayesian Nonparametrics (BNP) has resulted in numerous academic publications and awards.
During his PhD, he interned at IBM Research, where he developed probabilistic models of latent roadway traffic volumes in Nairobi using local context information.
Active in:
Uganda and Kenya