Confident failure is the expensive kind
Any model will turn prose into JSON. That is not the problem worth solving.
The problem arises when the text does not include the field. A model asked for a delivery date will produce one that looks like a real answer. One invented value entering a database costs more than fifty rows handled by hand because the fifty are visible and the one is not.
Extraction must be able to say nothing. Every field is either a value found in the source or an explicit empty marker. The structure is enforced after generation rather than trusted.
The output is two tables, not one. Rows that parsed cleanly, and rows that did not, each carrying the original text so a person can see what the extraction was looking at. A single table with quiet gaps is the outcome to avoid.
How the ActionFlow runs on the canvas
Text can arrive in three ways: pasted into Start, uploaded through Document Upload, or pulled from a PDF by PDF4me.
A Generate Object node performs extraction against a schema you define. Defining the shape up front is the point: the model fills a structure rather than inventing one, and any unsupported field from the source returns empty instead of imagined.
A Validate node enforces types and required fields independently of the model. This separation matters. The generation step is asked to be honest. The validation step does not rely on it.
A Filter node splits the result. Clean rows continue to their destination: Google Sheets, Airtable, PostgreSQL, or a Create Excel node when a file is needed. Rejected rows go to a separate sheet or table with the original text attached and the failed field named.
When the source is long and only one section is relevant, an Extract Data node sits before generation. This keeps the token count down and the extraction focused.
Because the row count is visible on both sides, the flow indicates its own accuracy over time. A rising rejection rate usually means the input format has changed, not that the model has gotten worse.
Nodes this ActionFlow uses
- Start: receives pasted or piped text. Document Upload (Data) takes a file instead.
- PDF4me (Data): extracts contents when the source is a PDF.
- Extract Data (Data): narrows a long source to the relevant section before generation.
- Generate Object (AI Core): fills your schema, returning empty for anything the source does not support.
- Validate (Data): enforces types and required fields independently of the model.
- Filter (Data): separates clean rows from rejected ones.
- Google Sheets (Integration): receives clean rows. Airtable, PostgreSQL, MySQL, and MongoDB fit the same slot.
- Create Excel (Data): produces a file when someone needs one rather than a table.
Frequently asked questions
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