AI Pre-Import Transform
What it does ?
AI Pre-Import Transform lets you write a plain-English instruction that automatically restructures uploaded files before they reach the column-mapping step. Instead of asking your users to clean up their files manually, the AI rewrites the data for them, renaming columns, flattening nested headers, reordering rows, splitting or merging columns, whatever the instruction describes.
How to enable it ?
Open your sheet's Settings
Go to the File Upload tab
Find AI Pre-Import Transform and set it to Enabled
Enter your instruction in the Prompt field
Save settings
Writing a prompt
The prompt is a plain-English description of what you want done to the uploaded file. Examples:
"Rename column 'Item Description' to 'Product Name' and 'Unit Cost' to 'Price'"
"Flatten the two-row merged header into a single header row"
"Split the 'Full Name' column into 'First Name' and 'Last Name'"
"Remove any rows where the Quantity column is empty"
"Add a column called 'Status' with the value 'Pending' for e
very row" Be specific about column names and the desired outcome. The AI follows the instruction exactly as written.
What happens during upload
When a user uploads a file, the AI reads the data, applies your instruction, and the transformed result is what the user sees in the column-mapping step. The original file is not stored — only the transformed output is processed.
If the AI cannot apply the transformation (e.g. the file structure does not match the instruction), the original data is shown unchanged.
Credits
AI Pre-Import Transform uses a separate credit pool from OCR credits.
AI processes a new file
Based on actual token usage (1 credit = 1,000 tokens)
Same file re-uploaded with no changes
1 flat credit
Monthly plan credits are used first each billing cycle. Purchased credits are used as overflow once the monthly quota is exhausted.
You will receive an email notification when you reach 90% and 100% of your credit limit.
Limitations
The prompt applies to every upload on that sheet — it cannot vary per user or per file
Very large files may use more credits due to higher token count
Complex transformations (e.g. multi-condition logic) may fall back to a direct AI call, which uses more tokens than a simple recipe
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