A leading EdTech company was carrying the weight of a monolithic database, one with over 600 tables and 400 procedures, and that complexity was creating real problems. Redundant and over-leveraged data was driving governance and accuracy issues, while reporting and transactional data mixed together in ways that muddied system clarity. Modernizing that environment the traditional way meant manual decomposition, and manual decomposition meant high cost and long timelines, neither of which the business could afford. To move forward with confidence, the client needed a phased modernization approach, one built on complete dependency transparency rather than guesswork.
Ascendion implemented an agentic AI-based solution architecture delivered through 25+ agents on the client’s AWS cloud. The system automatically captured metadata and business logic from both the database and application, enabling a clear view of existing structures. Using machine learning, the approach scored complexity, clustered modules, and classified dependencies for efficient modernization planning. Finally, Neo4j visualized data lineage and built living documentation of data flow, supporting ongoing modernization efforts with accuracy and transparency.
Using machine learning, the approach scored complexity, clustered modules, and classified dependencies to make modernization planning far more efficient than manual review ever could. Neo4j then visualized data lineage and built living documentation of data flow, giving the client an accurate, transparent foundation to support modernization efforts as they continued to evolve.
Tech Stack:
Reduced modernization timeline by 60%, accelerating delivery from 30 weeks (traditional) to just 12 weeks using Agentic AI with AAVA.
Delivered 40% faster insights generation by eliminating silos and streamlining data structures.
Built 90+ Power BI reports and 50+ trend dashboards in just 3 months.
Reverse-engineered and mapped lineage for 3K+ T-SQL scripts and over 2 million lines of code in 3 months.
