Full-stack digital transformation and product engineering for enterprises and startups
Codelogicx is a product engineering and digital transformation firm founded in 2013, operating across web, mobile, cloud, AI/ML, and enterprise infrastructure. The stack reveals heavy ML/data infrastructure (TensorFlow, PyTorch, Keras, MLflow, LangChain) alongside enterprise deployment (Kubernetes, Docker, AWS/GCP/Azure), and the project list—on-prem/cloud networks, SIEM/SOAR, vulnerability management, CI/CD automation, telemetry agents—shows they're building security-first, ops-heavy systems, not just consumer software. Engineering-forward hiring (7 of 8 open roles) and pain points around five-nines availability, incident response, and cloud cost optimization confirm they operate in high-stakes, regulated environments.
Codelogicx partners with startups, growth-stage companies, and enterprises to design and build digital solutions across healthcare tech, logistics, retail, and enterprise IT. The company spans product engineering (MVP through scale), web and mobile development, cloud infrastructure, ML/AI systems, and blockchain. Operating as an AWS Partner, they maintain ISO 9001:2015 and ISO/IEC 27001:2013 certifications and employ a distributed team with hiring concentrated in India. The client portfolio includes large pharmacy platforms in the UK and Australia, EU pharmaceutical shipment-tracking systems, and fintech platforms serving over 1 million users across African markets.
Python, TensorFlow, PyTorch, Keras, scikit-learn, Docker, Kubernetes, AWS, GCP, Azure, Jenkins, Prometheus, Grafana, Figma, and JavaScript. They are adopting CloudFormation and use ML frameworks (MLflow, LangChain, LangGraph) and observability tools (Prometheus, Grafana).
On-prem and cloud network implementation, SIEM/SOAR integration, vulnerability management and patching, CI/CD automation, infrastructure and cloud operations, telemetry capture agents, and firmware integration for hardware systems.
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Codelogicx'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.