Eagle: The Planner

Eagle is Onix’s automated data migration tool and the discovery, assessment, and planning agent inside Wingspan 2.0. Every data migration project fails for the same reason: teams don’t know what they have before they start moving it. Eagle fixes that. It autonomously scans your data warehouse, SQL logs, ETL mappings, BI reports, and scheduler exports, traces end-to-end column-level lineage, maps every dependency, and surfaces undocumented business logic buried in legacy code. What used to take a team of engineers 2 to 3 months gets done automatically, in 4 to 6 weeks, before a single byte moves.

The Semantic Twin is a living knowledge graph of your enterprise. Not just your data schema, but the business meaning behind it: what “revenue” means in your context, what processes depend on which data sources, what breaks if a table changes. Eagle builds it automatically by ingesting your existing systems, normalizing everything into a graph model, and continuously updating it as your environment evolves. No manual ontology mapping. No upfront definition work required.

Most data migration software starts at movement. Eagle starts at understanding. The Semantic Twin Eagle builds is not a one-time assessment output. It is the permanent context layer that Raven uses to convert code, Pelican uses to validate data, and every future AI initiative inherits. You are not buying a migration assessment. You are building the intelligence foundation your enterprise runs on going forward.

Yes. Many organizations start with Eagle specifically to understand what they have before deciding where to go. The assessment output, which includes dependency maps, workload complexity scores, cost estimates, and lineage reports, stands on its own as a planning artifact. It also makes any subsequent migration move faster because discovery is already done.

AI fails not because of bad models but because the data those models run on lacks context and lineage. Eagle’s Semantic Twin resolves this directly. It builds a four-layer architecture progressing from business glossary and taxonomy through ontology and knowledge graph to an AI activation layer where agents operate on semantic context, not raw tables. After Eagle runs, your AI environment has one glossary, one lineage path, and one source of meaning. That is what drives Wingspan’s 40% hallucination reduction metric.