Gloverlabs offers an agentic platform that ingests all artifacts of legacy mainframe applications—COBOL, PL/I, RPG code, documentation, tickets, and logs—to build a living knowledge graph of the system. This structured, continuously updated graph serves as a single source of truth that AI agents and developers can query for automated documentation, dead‑code detection, impact analysis, and modernization recommendations, reducing reliance on scarce experts and lowering maintenance costs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises with large legacy mainframe applications face growing technical debt because knowledge about COBOL, PL/I, RPG code, associated documentation, tickets, and logs is scattered across decades of development and few remaining experts. This results in costly maintenance, difficulty modernizing, and high risk of errors when changes are made.
Solution
Glover provides an agentic platform that ingests all artifacts of a legacy portfolio—including source code, user interfaces, documentation, support tickets, logs, and expert knowledge—to construct a living knowledge graph of the system. The platform continuously maps and understands the codebase, exposing a single source of truth that both humans and AI agents can query. Built-in AI agents use this structured understanding to automate documentation, identify dead code, suggest refactoring, and guide modernization efforts, reducing reliance on scarce domain experts. The solution delivers actionable insights through a web interface and APIs, enabling faster, safer maintenance and migration of legacy systems.
Target Audience
Primary customers are large enterprises and financial institutions that maintain critical mainframe applications and have dedicated IT operations or modernization teams seeking to reduce legacy maintenance costs and accelerate migration.
Features
- Automated ingestion of COBOL, PL/I, RPG, and other mainframe code along with related docs, tickets, and logs
- Construction of a dynamic knowledge graph that captures code structure, dependencies, and historical decisions
- Continuous agentic loop that updates the graph as the system evolves and provides real-time insights
- AI-driven tools for dead‑code detection, impact analysis, and automated generation of up‑to‑date documentation
- Guided modernization recommendations and risk assessments based on the living specification
- Web dashboard and API access for developers, operations teams, and modernization consultants