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AI-driven ETL modernization: multi-agent framework for enterprise data platform
We transformed ETL modernization approach from manual rewrites into a governed, multi-agent AI system designed for scale, control, and long-term growth.
Client’s context
Winner of the 2025 Merit Gold Award for its work in transforming automotive data connectivity, Authenticom provides mission-critical data integration services for the automotive industry.
Its platform 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.
Client
Authenticom, Motive Retail
Country
USA
Domain
Automotive
Solution
Multi-agent AI migration framework
Service
Technologies
Challenge
The legacy ETL system presented significant engineering obstacles. Years of incremental enhancements had turned the platform into a web 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.
See also:
Solution Architecture Consulting
By examining organizational goals, processes, and pain points, our solution architects align technology with unique business strategies and create cohesive IT ecosystems within established time frames.
Our approach
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.
Why this approach is disruptive
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.
Generative AI delivers value when it focuses on tangible outcomes and solving real problems. Instead of chasing hype, modern teams prioritize results: measurable speedups, reduced manual effort, and audit‑ready operations. Read the full article to learn more.
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:
| Agent type | Role |
|---|---|
| Schema analysis agent | To inspect legacy payloads and target schemas to generate structural mappings. |
| Transformation reconstruction agent | To use LLM-assisted pattern analysis to infer undocumented rule logic. |
| Code synthesis agent | To generate modular Node.js/TypeScript ELT modules based on extracted semantics. |
| Validation agent | To run automated regression tests and ensure output fidelity against known input–output pairs |
| Documentation agent | To produce structured documentation, change logs, and audit trails. |
The agents communicated via structured prompts and shared intermediate state through a context layer, ensuring continuity and traceability across large rule sets.
Governance
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.
This governance model ensured that AI increased speed without compromising correctness, maintainability, or auditability.
Continuous integration and validation
The modern ELT modules were integrated into CI/CD pipelines, with automated unit and integration tests, regression harnesses, and centralized logging being used to ensure SOC2 compliance and operational transparency.
Results and impact
The transformation delivered results far beyond traditional modernization projects. These included:
- Rapid conversion of legacy integrations at unprecedented speed
- Massive reductions in manual engineering effort
- Dramatically improved test coverage
- Fully structured documentation where none existed before
- A predictable, repeatable modernization workflow
- Faster onboarding of new data sources
- Lower operational risk
Most importantly, Authenticom gained an award-winning modernization model that will evolve alongside its business.
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.
This is what we call AI transformation.
More than a vendor. A strategic partner.
EffectiveSoft quickly became more than a vendor. They were a strategic partner. Their team delivered stability and predictability at every stage of the project.
Philipe Araujo
Business Analyst & Quality Engineer, Authenticom