ERP and HCM platform for Brazilian mid-market and enterprise
Sankhya is a 35-year-old Brazilian ERP vendor serving 2,000+ customers with modular, configurable business management software. The tech stack reveals a Java + Spring + React core migrating toward AI capabilities—actively adopting LangChain, LlamaIndex, and BigQuery across AWS/GCP/Azure—while scaling sales and engineering headcount. Pain points center on go-live execution, enterprise implementation complexity, and reducing unnecessary customizations, suggesting the platform is moving upmarket from SMB toward larger deployments where configuration management and deployment velocity matter most.
Notable leadership hires: Tech Lead, Branding & Go-to-Market Head, Head of Events
Sankhya builds enterprise resource planning (ERP) and human capital management (HCM) software for Brazilian businesses. The product suite includes core modules for payroll, general ledger, logistics, and finance, with a payroll product currently in active go-live cycles across the customer base. Customers range from mid-market to large enterprises; Sankhya's business model combines software licensing with implementation services and consulting. The company operates exclusively in Brazil, with headquarters in Uberlândia, MG, and currently maintains 1,001–5,000 employees across sales, engineering, and delivery functions.
Sankhya's core platform runs on Java, Spring, React, and Node.js, with Oracle as the primary database. Infrastructure is deployed on AWS, GCP, and OCI using Docker, Kubernetes, CloudFormation, and Terraform. The stack includes Spring Boot, Hibernate, and Spring Security for backend services, plus Kafka for messaging.
Current projects focus on ERP implementations for enterprise clients, payroll system go-live and stabilization, modular composable ERP features (logistics, finance), and reducing unnecessary customizations. The company also publishes tax regulatory news and is mapping labor law compliance requirements into internal tooling.
Sankhya'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.