Automated AI migration script saves thousands of hours for NHA
Automating complex data mapping and content transformation to migrate legacy systems in record time.
NHA
NHA, one of the largest distance learning providers in the Netherlands, needed to migrate their extensive educational content from a legacy CMS to a modern platform. The project required precise mapping of thousands of unstructured data fields to a new schema without losing SEO value or content integrity.
Manual migration was estimated to take thousands of hours, risking significant human error and high operational costs. The legacy system contained inconsistent data structures that made traditional rule-based scripts ineffective. NHA needed a solution that could intelligently interpret content context to ensure every field landed in the correct destination within the new CMS architecture.
What SevenLab built
SevenLab developed a custom AI migration agent that used Large Language Models to autonomously analyse, map, and transform legacy data. By understanding the context of the content rather than just the field names, the script successfully automated the transition with minimal human oversight.
Intelligent field mapping
AI-driven analysis to match legacy data points to new CMS structures based on semantic meaning.
Automated content cleaning
The script identifies and fixes formatting inconsistencies and broken links during the transfer process.
Contextual validation
An AI reasoning layer verifies that migrated content remains coherent and logically placed in the new system.
High-speed execution
Batch processing capabilities that move thousands of records in minutes rather than months.
Measurable business impact
The AI migration script transformed a daunting multi-month manual task into a streamlined, automated process. By leveraging intelligent mapping, NHA successfully transitioned to their new CMS ahead of schedule with zero data loss and significantly reduced costs.
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