An EdTech leader was running critical operations on a system that, while powerful, had grown genuinely complex over time. Redundant structures and tightly coupled logic made it difficult to understand how data actually flowed across the platform, and reporting and transactional workloads had started to overlap, limiting clarity for anyone trying to make sense of the system. Every modernization effort demanded extensive manual analysis just to identify dependencies and avoid disrupting operations already in motion. That complexity was quietly taxing the whole organization. It slowed the ability to generate insights, introduce new capabilities, and evolve the platform at the pace the business needed. Engineering teams were spending valuable time interpreting legacy systems instead of building new solutions, data teams faced delays delivering reliable reports, and business leaders lacked the timely visibility they needed to support fast-moving decisions. To unlock agility and prepare for future growth, the organization needed a way to modernize its data foundation without risking the operational continuity it depended on every day.
Ascendion partnered with the client to transform its approach to data modernization using AAVA, its Agentic AI platform. Rather than relying on manual decomposition, Ascendion deployed an intelligent, agent-based architecture on AWS that automated the discovery and analysis of the client’s entire data ecosystem, with more than 25 AI agents working collaboratively to capture metadata, analyze business logic, and identify dependencies across thousands of scripts and database objects.
Machine learning models assessed complexity, grouped related components, and classified dependencies, creating a structured roadmap for modernization. Neo4j lineage visualization gave teams a real-time, interactive view of exactly how data moved across the system, something the organization had never had before: complete transparency. With that visibility in place, engineering teams could modernize systems confidently, guided by automated insight rather than manual interpretation, and data structures could be reorganized and optimized without disrupting business operations along the way.
Tech Stack:
Modernization timelines were reduced by 60%, shrinking delivery cycles from 30 weeks to just 12 weeks.
Automated lineage mapping reverse-engineered over 3,000 T-SQL scripts and more than 2 million lines of code in three months, eliminating months of manual effort.
By eliminating structural silos, insight generation improved by 40%, enabling faster, more reliable reporting.
Within three months, the organization delivered over 90 Power BI reports and 50 dashboards, equipping teams with the visibility needed to support learners and institutional partners.
