AI-powered workforce management and operations platform for retail
Logile operates a SaaS platform for retail workforce and operations optimization, built on a polyglot stack spanning Java, Python, and React with significant ML depth (TensorFlow, PyTorch, scikit-learn, MLflow). Active adoption of modern test-automation tools (TestRigor, Testim, Mabl) paired with infrastructure reliability listed as a top pain point suggests the company is scaling toward enterprise-grade deployments while reworking QA processes. Leadership-heavy hiring (director, leads, managers) combined with large-scale enterprise delivery projects signals a shift toward managed-services delivery for tier-1 customers.
Notable leadership hires: Director of Delivery
Logile provides workforce management, scheduling, forecasting, and labor-modeling software for retail operations. Founded in 2005 and headquartered in Texas, the company serves mid-to-large retailers with a platform spanning employee scheduling, task management, food safety compliance, and labor-cost optimization. The product runs on cloud infrastructure (AWS and Azure) and is distributed as multi-tenant SaaS. Current focus includes fresh food and inventory management implementations, large-scale enterprise deployments, and success-planning frameworks to improve customer retention and on-time delivery.
Java, Python, JavaScript, and TypeScript for core platform. ML pipelines use TensorFlow, PyTorch, and scikit-learn. Testing uses Selenium, Playwright, and Jest; CI/CD runs on GitLab and Azure DevOps. Infrastructure spans AWS, Azure, and GCP with Docker and Kubernetes for orchestration.
Fresh food and inventory management system implementations, large-scale SaaS deployments for tier-1 enterprise customers, AI-powered test automation tool integration, and scalable test-automation frameworks. Customer success process evolution and enablement asset creation are parallel workstreams.
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Logile, Inc.'s technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.