AI Import Column Mapping

Guess how an uploaded file's columns line up with the app's fields, then ask.

involved AI Safety & Trust

What it adds

A proposed mapping from uploaded columns to destination fields, shown with samples and confirmed before the import runs.

What your agent is told to do

1
Take the uploaded headers and a small sample of rows and propose a mapping to the destination fields. Match on exact and near-exact header names first and only consult the model for the columns that remain unresolved.
2
Constrain the candidate set to fields the current user and the importer are actually permitted to write. A mapping that targets a read-only, computed, or privileged field must never be offered, whatever the header says.
3
Show the proposal as a table: source column, sample values, destination field, confidence, and the transformation that will be applied. A user cannot approve a mapping they cannot see the effect of.
4
Require an explicit confirmation before any row is written, with low-confidence and unmapped columns called out for attention. Do not start the import on the strength of the suggestion alone.
5
This brief ends when the mapping is confirmed. Rows that then fail validation belong to AI Import Error Repair, and corrections to data already stored belong to AI Data Cleanup Suggestions; hand off rather than duplicating their review screens.

Edge cases it handles

Only fields the importer may write can be offered as targets. Filter the candidate list by permission before the suggestion is made, not after the user has chosen.
Every proposed mapping needs a confidence and a worked sample showing what a real value becomes after transformation, so a wrong date or currency assumption is visible before the import rather than after.
Two source columns silently pointing at one field will overwrite each other unpredictably. Detect the collision, refuse to proceed, and make the user resolve it.
Custom fields defined per workspace and headers in another language both need to map. Read the destination list at runtime rather than assuming a fixed schema, and match on meaning rather than English keywords alone.
No row is written until the user confirms. An import that begins while the mapping preview is still on screen cannot be taken back.
Columns the user leaves unmapped must be reported explicitly before confirmation, because silently dropped data is discovered weeks later.
Files with no header row, duplicate headers, or a header row that is actually data must be handled rather than mapped confidently to nonsense.
Send only headers and a bounded sample of rows to the model, never the whole file, and set a ceiling on that sample so a large upload cannot run up an unbounded cost.

Definition of done

Uploaded columns arrive with a proposed destination field, a confidence, and a sample transformation.
Only fields the importer and the current user may write appear as mapping targets.
Two columns mapped to the same field block confirmation until resolved.
Unmapped columns are listed explicitly before the user confirms.
No rows are written until the user confirms the mapping.
Workspace-specific custom fields and non-English headers map correctly.
Only headers and a bounded row sample are sent to the model.
The feature matches the existing design system.
No existing functionality is broken.

Related features

AI Audit Trail

AI PII Redaction

Model Fallback

How it works

1

Copy the link

Grab the Markdown instruction URL for this feature.
2

Give it to your AI

Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
3

It inspects, then implements

Your agent reads your existing app first, then adds the feature to fit it.

Works with your stack

These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.

Need it tighter than that? Customize the feature and tell it exactly what you're running.