Modernizing legacy ETL rules with AI agents
Authenticom’s automotive data platform processes hundreds of thousands of transactions every day.
However, its core ETL layer had been built nearly two decades ago and had grown to more than 28,000 legacy transformation rules. Over time, even minor changes became slow, risky, and expensive. A complete rewrite was unrealistic, yet incremental fixes would only prolong the accumulation of technical debt.
2000
2000
legacy transformation rules
0 years
0 years
System was growing
5+
5+
Team members
To avoid a blind large-scale migration, we started with an AI modernization workshop.
Together with the client, we evaluated realistic transformation paths and aligned on measurable objectives: predictable delivery, transparency of transformation logic, and reduced operational risk. The workshops made it clear that only an AI agent-driven approach could deliver both the required speed and governance.
We then engineered a multi-agent AI system designed to automate the full transformation lifecycle — from interpreting legacy logic to generating modern ELT code and validating outputs. Crucially, the system operated with structured validation checkpoints embedded across analysis, conversion, and verification phases, ensuring traceability and control at every stage. Human experts remained in governance roles, shifting the focus from manual rewriting to validation, oversight, and quality assurance.
As a result, Authenticom gained significantly improved delivery velocity, full visibility into transformation logic, and a modernized ELT foundation.
These improvements enabled the company to evolve without recreating legacy constraints. The modernization initiative transformed what once seemed operationally risky into a controlled, scalable, and audit-ready data architecture.