Software intelligence via source-code analysis for modernization and cloud
CAST analyzes source code semantically to map applications and portfolios, supporting technical debt, modernization and cloud programs. Its stack spans modern languages (Go, Python, Node.js, React) alongside mainframe tech like COBOL, CICS, DB2 and z/OS, reflecting the legacy estates it must parse. Senior-heavy engineering hiring centers on Neo4j query performance, RBAC and a distributed queue system, pointing to investment in the graph-based analysis core.
CAST builds software intelligence technology that analyzes source code to produce maps and dashboards of individual applications and whole portfolios. Executives and technology leaders use it to track initiatives such as technical debt reduction, GenAI, modernization and cloud migration. Customers include enterprises, governments, consultancies and cloud providers. Focus areas include portfolio governance, technical due diligence, open-source risk and ISO 5055 adherence. The privately held company is headquartered in New York, NY, with 201–500 employees, and is currently hiring in India and Italy.
CAST uses Go, Python, Java, Node.js, .NET, React and Angular, Neo4j, Docker and Kubernetes on AWS, Azure and GCP, plus mainframe technologies such as COBOL, CICS, DB2 and z/OS.
CAST is currently hiring in India and Italy, mostly senior engineering roles with some support and sales positions, and hiring is accelerating.
Job posts point to an RBAC system, a distributed queue system, Neo4j query performance optimization, sales pilots and PoCs, and modernization and migration program delivery.
Other companies in the same industry, closest in size
CAST'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.