Automates the import of customer data files by intelligently mapping fields, cleaning data, and providing detailed migration results.
This workflow is designed for product and onboarding teams who want to simplify the often complex and error-prone process of migrating customer data into their platform. By automating field mapping and data validation, teams can dramatically reduce the time and technical expertise required for successful data imports.
The workflow begins when a customer uploads their existing data file (CSV, Excel, etc.). AI analyzes the file structure and automatically maps columns to the correct fields in your system, even when field names don't match perfectly. The system then cleans and standardizes the data before importing it in manageable batches, with error handling throughout the process.
By implementing this automated approach, companies typically reduce migration time from days to minutes and see a significant decrease in support tickets related to data import issues. Customers benefit from a smoother onboarding experience and faster time-to-value, as they can begin using the product with their data properly imported without requiring technical assistance.
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Use this example as a starting point, not a fixed recipe.
Copy the flow above as a prompt, markdown, skill, or mermaid diagrom. Adjust the steps, tools, agent and human involvement to fit your real workflow. Then test where users actually get value or drop off.
I have a data file with the following columns: {sourceColumns}. Our system has these fields: {targetFields}. Please create a mapping between the source columns and target fields, matching them based on semantic similarity and typical data migration patterns. For any source column that doesn't have a clear match, suggest the most likely target field or indicate that it should be skipped. Return the mapping as a structured JSON object.