DodoForm: Collecting With a Form vs. Extracting From Existing Data

Updated 2026-10-02

DodoForm has two ways to get structured data in: Collect (send an AI form and let other people fill it in) and Extract (drop data you already have into the Extractor). Both end in the same place — clean, verified records with per-field confidence that can be routed into an ATS or CRM. The choice comes down to one question: is the data still in someone else's head, or is it already sitting in your inbox, files, and notes?

The two doors, side by side

	Collect with a form	Extract from your data
Where the data starts	With someone else — a candidate, client, lead, or vendor	With you — emails, resumes, briefs, notes, files, audio
How it comes in	You share a form; they type, talk, or paste a mess	You paste or upload the pile you already have
Who confirms it	The respondent confirms what the AI heard before submitting	You review low-confidence rows in a queue
Best for	New data you don't have yet	Data you already hold but haven't structured
Output	Clean records, not raw transcripts	Records mapped to your schema, with per-field confidence

When to use Collect

Use Collect when the information exists only in another person's head and you need it in a usable format. You share an AI form, and the respondent can answer by typing, talking, or pasting something messy — a forwarded email, a voice note, a screenshot.

The key difference from a normal form: the respondent confirms what the AI heard before it submits. So you receive verified records rather than raw transcripts, and low-confidence rows get routed to review instead of silently entering your system.

This fits scenarios like:

A recruiter sending a screening form to candidates
An agency gathering a client brief
An ops team collecting inbound lead details or vendor information

When to use Extract

Use Extract when you're already sitting on a pile of unstructured material — emails, resumes, notes, PDFs, images, or audio — and you want it pulled onto your own schema.

You drop the material into the Extractor, and DodoForm maps it to the fields you define. Two features matter here:

Per-field confidence scoring — each extracted value carries a confidence figure, so you can see which fields the AI is sure about.
A review queue for low-confidence rows — anything uncertain is flagged for a human to check rather than passed through.

The site's own example shows the shape of this: a messy follow-up email becomes structured fields (name, company, email, phone, team size, timeline, budget) with individual confidence percentages attached.

What both paths share

Whichever door you use, the output is the same kind of thing:

Structured records, not raw text or transcripts
Verification built in — either the respondent confirms, or you review flagged rows
Routing onward — clean records go straight into your ATS or CRM
Traceability — fields are traced back to their source

DodoForm frames this as "messy in, verified data out," and the site states that fields are traced to source, which is what makes the confidence scores meaningful rather than decorative.

How to decide

Ask one question: where does the data live right now?

In someone else's head or hands → Collect. Send a form.
Already in your inbox, files, or notes → Extract. Drop it into the Extractor.

If you have both situations — say, a backlog of old resumes plus a need to screen new candidates — the two paths feed the same structured output, so you don't have to pick one permanently.

Practical notes

DodoForm runs on the web and Android (available on Google Play), with 200+ templates.
There's a 14-day Pro trial with no card required, and a free plan that includes 100 AI credits a month and unlimited responses.
You can try the extraction engine without an account by pasting something messy into the demo on the site.

For teams whose data arrives as "candidate emails, client briefs, inbound leads, vendor forms — buried in inboxes, PDFs, and voice notes," the two-door setup maps cleanly onto the two ways that data actually shows up.