AI Suggest Columns — Automatic Field Detection for Any Document
Most tools that call themselves "no-template" still make you define every extraction field by hand before you can process a single document. AI Suggest turns this around — it reads your document first, identifies the relevant fields automatically, and presents them to you.
5-10s per page · 99% accuracy on printed text · Auto field detection · Zero up-front setup
What AI Suggest Detects Automatically
Upload any document — the AI reads its content and suggests relevant fields without you typing a single column name. No templates, no configuration, no guessing what's inside the file.
AI reads each document's content and suggests these fields automatically — you don't type a single column name.
Supposedly 'No Setup' — But You Still Define Every Field by Hand
Removing templates was a leap forward. But even the most automated extraction tools ask you to do one thing before they start: type the column names. AI Suggest removes this final mile of manual setup.
What 'No Template' Tools Still Make You Do
You still name every field before the first upload. Even tools that advertise "no-template" or "zero-setup" require you to type column names — Invoice Number, Date, Total — before extraction begins. That's one step better than drawing zones, but it's still manual schema definition, and users on r/automation voice the same frustration: "don't want to spend hours configuring templates or training models." The bar has moved from "no templates" to "no configuration at all."
You need to know what's in the document before you process it. Defining columns means you must already know what fields exist on every document you're about to upload. When you're processing unfamiliar suppliers or ad-hoc requests, you often don't know — and the setup step forces you to guess. Wrong guess means reprocessing with corrected columns.
Each document type resets the setup cost. Switching from invoices to purchase orders to bank statements means building a new column list each time. The setup effort doesn't compound — it resets to zero for every format, even though the same AI engine could handle all of them in a single batch.
AI Suggest: Let the Document Tell You What's Extractable
Upload first, define later. The AI reads each document's visual content before you type anything, identifying every present field — dates, numbers, names, line items, totals — and presenting them as suggested column names. The direction reverses: the document tells you what's extractable, not the other way around.
The suggestion adapts to each document's actual content. An invoice surfaces Invoice Number, Vendor, Due Date, and Line Items. A bank statement surfaces Account Number, Transaction Date, Description, Debit, and Credit. A purchase order surfaces PO Number, Ship-to Address, Requested Delivery Date, and Itemized quantities. The suggestion set is driven by what's on the page, not by a predefined template.
Accept, edit, or ignore — the choice is yours. Suggestions are a starting point, not a locked schema. Accept the relevant fields, edit a name for clarity, add custom columns the AI didn't detect, or dismiss suggestions and type everything manually. The zero-set-up philosophy extends through the entire interaction: you only do as much configuration as you want.
Upload, Review, Export — Zero Configuration in Between
Upload Without Setup
A new supplier sends invoices in a format you've never seen. Instead of opening one to figure out what fields exist, then typing column names, you drop the PDF batch directly into the tool. No template selection, no field configuration, no training samples — just upload and proceed.
Review Suggested Fields
The AI scans the first document and surfaces detected fields: Invoice Number (INV-2026-3842), Date (2026-06-15), Vendor (Precision Parts Co.), Total Amount ($4,287.50), Tax ($342.98), Line Items (3), Payment Terms (Net 30). Each suggestion shows the detected value alongside the field name, so you can verify accuracy before committing.
Accept and Export
Accept the relevant suggestions, deselect fields you don't need (like Shipping Terms), and let the batch process — 5-10 seconds per page. The output is a single XLSX where rows are invoices and columns match exactly what you accepted. The entire workflow: upload, review, export. Zero up-front column definition.
When AI Suggest Works Best — and When to Define Fields Manually
Suggestions accelerate familiar patterns. Custom fields cover the edge cases. Both share the same extraction engine.
When It Works Best
Standard business fields on printed documents. Invoice numbers, dates, total amounts, vendor names, line items — these are consistently recognized and suggested across virtually any layout or format.
Common document types out of the box. Invoices, receipts, bank statements, purchase orders, contracts, and forms all produce useful field suggestions with no pre-training per type.
Ad-hoc and one-off documents. When you're processing a format you've never seen before, suggestions eliminate the need to inspect the document first and guess which fields to define.
When to Be Cautious
Highly specialized or non-standard field labels. Industry-specific field names (like "KPI-7A compliance code" or custom ERP field IDs) may not surface as suggestions. Add them as custom columns — the extraction engine still handles them accurately.
Documents with mixed or non-Latin scripts. Fields in Japanese, Arabic, or Cyrillic are detected and suggested, but the quality of suggestion labeling depends on the document's language density. Purely Latin-script documents yield the highest suggestion accuracy.
Very low quality source images. Sub-100 DPI scans, heavily compressed screenshots, or crumpled originals may produce incomplete suggestion sets. For these inputs, falling back to manual column definition ensures you extract exactly what you need.
Frequently Asked Questions
Does AI Suggest work on every document type, or only invoices and receipts?
It works on any document type — invoices, receipts, bank statements, purchase orders, contracts, screenshots, forms, and more. The AI reads each document's visual content first, then suggests fields based on what it finds. A bank statement surfaces Account Number, Transaction Date, Description, Debit, Credit, and Balance. An invoice surfaces Invoice Number, Vendor, Total, and Line Items. There is no pre-training per document type — suggestions adapt to whatever the document actually contains.
Can I add my own fields alongside the suggested ones?
Absolutely. AI Suggest is a starting point, not a locked schema. You can accept individual suggestions, edit field names, add your own columns alongside the suggested ones, or ignore suggestions entirely and type everything manually. The suggestions reduce your setup time, but you always keep full control over the final column list. The Custom Column Extraction engine works identically whether the field came from a suggestion or was typed by hand.
What happens if the AI suggests a field that's not on every document in my batch?
Each document in a batch is analyzed independently. If a field like Shipping Address appears on some documents but not others, it still appears as a suggestion. Documents that don't contain the field simply leave that cell empty in the output table. The result is a unified spreadsheet with consistent column headers where every row aligns — the AI fills what it finds and leaves blanks where a field genuinely doesn't exist, so you never get misaligned columns or shifted data.
How accurate are the suggested field names — will I need to rename them?
The suggested field names are derived from each document's own labels and semantic content. For common fields — dates, totals, names, numbers — the semantic match between what the document says and the suggested column name is near 100%. For less standard labels (e.g., a document that calls the total "Grand Sum Due" instead of "Total Amount"), you can edit the column name before processing. The suggestions are meant to eliminate the majority of typing, not to enforce rigid naming.
Does AI Suggest replace Custom Column Extraction, or work alongside it?
It works alongside it as an optional starting point. Custom Column Extraction lets you define any column name you want — AI Suggest accelerates the process by pre-filling the column list with what the AI finds on the document. You can use AI Suggest to get started (especially useful for unfamiliar document types), then switch to manual column definition once you know which fields matter for ongoing processing. The two modes share the same extraction engine and can be mixed within a single batch.
Read more: How to Use Custom Column Extraction — the manual way to define fields, which AI Suggest complements by auto-detecting them for you. · Template-Free AI Document Extraction — why no-template extraction matters and how AI Suggest takes it a step further. · Can AI Understand Invoice Fields? — how vision AI recognizes field semantics across different document layouts.