AI Transform Suggestions
Generate a ready-to-run sequence of transform operations from your table metadata. Review each one, then apply the set to your pipeline.
AI transform suggestions read your table's metadata and return an ordered set of operations you can apply to your pipeline. Each suggestion names the operation, explains why it was suggested, and states whether it targets data quality or feature engineering. Nothing is applied until you approve the set.
What It Uses to Decide
Suggestions are generated from your table's governed metadata, not from your rows:
- Column names and data types from the connected integration
- Column descriptions generated and validated in AI Column Insights
- Data quality statistics per column from DQ profiling: null counts, distinct counts, and type compliance
- Operations already in your pipeline, in order, so suggestions build on what you have rather than repeating it
This is why suggestion quality tracks your AIR Score. A table with validated column descriptions produces operations aimed at what your data actually measures. A table with no descriptions produces generic cleanup.
The panel shows the source table's DQ score and AIR score before you generate, so you can judge suggestion quality in advance.
The Three Modes
Choose what the suggestion set should focus on.
Focuses on cleaning, null handling, type fixes, and deduplication.
Use this when your source table has known quality problems: blank values in key columns, dates stored as text, or duplicate rows from a repeated sync. Suggestions target the dimensions that move your DQ score.
No description field is shown in this mode. The suggestions come from the data quality statistics alone.
Engineers new columns, aggregations, and derived metrics.
Use this when the table is already clean and you want analysis-ready fields: a revenue tier derived from an amount column, a delivery duration from two timestamps, or a per-segment aggregate.
You can describe the features you want in 20 to 500 characters, or leave the field blank and let the AI infer the most useful flow from your table metadata.
Improves data quality first, then adds features.
Use this on a raw table you intend to take all the way to analysis. The returned set is ordered so cleaning operations come before the operations that depend on clean input.
The description field works the same as in Add Features: fill it in to steer the feature work, or leave it blank.
What a Suggestion Contains
Each suggestion in the returned set has four parts:
| Part | What it tells you |
|---|---|
| Order | Its position in the suggested sequence, starting at 1. Operations are meant to run in this order. |
| Purpose | Either data quality or feature engineering, so you can see at a glance what each step is for. |
| Description | Why this operation was suggested, what it does, and the expected data quality or feature benefit. |
| Operation | The operation and its full configuration, ready to run. This is what gets added to your pipeline when you apply the set. |
Every suggestion is validated against the operation schema before it reaches you. A suggestion that does not resolve to a real operation with valid parameters is rejected server-side, so the set you review is always runnable.
How to Generate Suggestions
Open Transform Setup
Open a transformation with a source integration and base table selected. The suggestion panel reads the table you have already chosen and loads its metadata.
Choose what the suggestions should do
Select Improve Data Quality, Add Features, or Improve Data Quality + Add Features.
Describe the features you want
For Add Features and the combined mode, describe what you want to engineer. The field accepts 20 to 500 characters.
Leave it blank to let the AI infer the best transform flow from your table metadata alone.
Select a model and context mode
Choose an AI provider available on your plan. Then choose a context mode, which controls what is sent to the provider: whether real table and column names are used or anonymized, whether data quality statistics and frequent values are included, and whether your user role is shared.
See Privacy and Context Modes for what each mode sends.
Generate
Generation consumes one analysis credit from your workspace pool. The panel shows progress while it prepares table context and builds the suggestion set.
If your workspace is out of analysis credits, generation is blocked before the request runs. Purchase an add-on pack to continue.
Review and apply
Read each suggested operation and its rationale. Applying the set appends the operations to your pipeline in the suggested order.
Applying does not run anything. Use Save & Preview on each operation to see its effect on your data and its DQ delta before you run or schedule the pipeline.
Duplicate Suggestions Are Filtered
Because your existing operations are sent with each request, the AI is working from what your pipeline already does. If a returned suggestion is configured identically to an operation already in your pipeline, it is filtered out before the set is shown, so you do not add the same step twice.
This comparison ignores internal identifiers and validation state, so a suggestion is treated as a duplicate when its operation and configuration match, regardless of when the existing one was added.
Starting Over and Exiting
Two controls manage a suggestion set in progress:
- Start Over clears the current stored suggestions so you can generate a fresh set, for example after changing the mode or the feature description. Generating again consumes another analysis credit.
- Exit suggestions leaves the flow and discards any stored suggestions that have not been applied.
Neither action affects operations already applied to your pipeline.
Plan Availability
AI transform suggestions are available on every plan, including the 14-day evaluation.
Two plan limits apply:
- Provider access follows your plan tier, the same as elsewhere in Edilitics. A provider not included in your plan cannot be selected for suggestions.
- Column limits follow your plan's transform column cap. A request covering more columns than your plan allows is blocked with an upgrade prompt.
Suggestions draw on the same analysis credit pool as AskEdi and dashboard Summarise.
Frequently Asked Questions
Related Docs
No-Code Operations
The 25 operations a suggestion set can draw from, with configuration reference for each.
AI Column Insights
Generate and validate the column descriptions that determine suggestion quality.
AI Readiness (AIR) Score
How column documentation and data quality combine into the score shown before you generate.
Analysis Credits
How the shared credit pool works across AskEdi, transform suggestions, and dashboard Summarise.
Need help? Email support@edilitics.com with your workspace, job ID, and context. We reply within one business day.
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