AI-driven legacy code modernization via a codebase knowledge graph

Build a service that reverse-engineers an entire legacy software estate (COBOL mainframes, monoliths) into a knowledge graph where every node and edge carries provenance (file, revision, symbols, extraction evidence), then orchestrates thousands of AI agents that each receive a precise 'task context packet' — their slice of the graph, constraints, evidence and open questions — so changes preserve system-wide behaviour. The key insight is that AI fails at modernization not because it can't write code but because it doesn't know the 14 other places a piece of logic is used, and bigger context windows don't fix it since usable context caps around 200k tokens regardless of advertised window size.

online B2B Service / Consulting / Agency

From AI Engineer — How We Mapped a 35-Million-Line Mainframe in 4 Days - Stephen Chin, Neo4j & Neeraj Deshmukh, Blitzy at 06:03

Problem: Traditional modernization is multi-year, error-prone and often fails because nobody understands the whole system — batch jobs, copybooks, data layouts, transaction contracts — before changing it; vector search and SQL-based retrieval only give single-hop facts, while code is inherently a graph-shaped problem needing multi-hop context

For: Banks, insurers and other large enterprises running decades-old mainframe and monolith systems they are afraid to touch

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Blitzy Neo4j

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