Mission-critical ETL platform, modernized with agentic AI
We transformed the ETL modernization approach from manual rewrites into a governed, multi-agent AI system designed for scale, control, and long-term growth.
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Client
Authenticom
Industry
Automotive
Country
USA
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Service
AI-assisted legacy systems modernization
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Solution
Multi-agent AI migration framework
CLIENT’S CONTEXT
Authenticom provides mission-critical data integration services for the automotive industry
A winner of the 2025 Merit Gold Award for its work in transforming automotive data connectivity, Authenticom operates a platform that connects dealerships, OEMs, and technology vendors, enabling real-time data exchange that supports sales, services, financing, and customer experience workflows. At the core of this infrastructure is an ETL system that processes more than 100,000 files per day across 10,000+ dealerships.
CHALLENGE
The legacy ETL system presented significant engineering obstacles
Years of incremental enhancements had turned the platform into a complex network of legacy transformations built on multiple technologies. Thousands of rules were undocumented or inconsistently documented, making the system difficult to understand and risky to modify. Every new integration was getting harder, every change taking longer, and scaling the system safely was becoming a strategic challenge.
Traditional modernization approaches, whether full rewrites or incremental refactoring, were projected to take many months and involve high engineering effort and extensive QA cycles. In response to this situation, Authenticom launched a modernization initiative to move this environment to an up-to-date architecture.
OUR APPROACH
We began with an AI modernization workshop
Rather than starting a blind large-scale migration with unclear terms and results, we took a different path. We began with an AI modernization workshop. Business and engineering stakeholders aligned on shared goals: faster delivery, predictable timelines, full visibility into transformation logic, and reduced operational risk.
The workshop compared three realistic approaches—manual rewrite, partial automation, and an AI-agent-driven model—and helped determine the most efficient one.
We designed a disruptive multi-agent system grounded in an engineering mindset, in which AI was used to accelerate the process, but engineers retained full control over the architecture, validation, and design decisions.
KNOWLEDGE DISCOVERY AND EXTRACTION
The first step was to make the blurred legacy ETL understandable and analyzable
Existing mappings, scripts, and configurations were reviewed and processed to build a clearer picture of the transformation logic.
AI tools helped interpret complex or poorly documented components, infer schemas from sample data, and reconstruct business rules that had accumulated over many years. In this way, fragmented legacy logic became clear, structured knowledge that could be analyzed and transformed.
AGENTIC MODERNIZATION FRAMEWORK
This phase turned the fragmented legacy knowledge into a basis for modernization
To move from understanding to execution, we implemented an AI-assisted multi-agent framework. Rather than relying on a single model, we organized the process as a coordinated set of specialized functions, each focused on a specific task within the transformation pipeline. The agents communicated via structured prompts and shared intermediate state through a context layer, ensuring continuity and traceability across large rule sets.
GOVERNANCE
AI increased speed without compromising correctness, maintainability, or auditability
Speed mattered, but engineering discipline mattered more. Every AI-generated artifact was reviewed and validated by the project team.
Confidence levels were assessed, edge cases analyzed, and results tested against real production data. A controlled sandbox environment enabled safe iteration before any changes were promoted further.
The modern ETL modules were integrated into CI/CD pipelines, with automated unit and integration tests, regression harnesses, and centralized logging being used to ensure SOC 2 compliance and operational transparency.
Philipe Araujo
·
Authenticom
RESULTS AND IMPACT
The transformation delivered results far beyond traditional modernization projects
Rapid conversion of legacy integrations reduced manual engineering work, while structured documentation created a clear foundation where none had existed before.
The result is an award-winning modernization model built to scale and evolve alongside Authenticom’s business.
Faster
data-source onboarding
Lower
operational risk
Improved
test coverage
CONCLUSION
A new model for legacy transformation
This project proves that AI is not just for chatbots and analytics but can fundamentally change enterprise engineering workflows.
By embedding AI directly into the modernization lifecycle, organizations can transform even the most complex ETL environments into modern, scalable, future-ready platforms in a fraction of the time traditional methods would require.