How DodoForm Verifies Data and Avoids Inventing Information

DodoForm keeps AI extraction honest by tracing every field back to the source text and attaching a confidence score, then routing anything uncertain to a human review queue instead of guessing. On the collection side, the person filling out the form confirms what the AI heard before submitting. The result is a clean, verified record rather than a raw transcript or a plausible-sounding fabrication. This applies to both ways DodoForm takes in data: collecting from others via AI forms, and extracting from files, emails, or notes you already have.

The core mechanism: source tracing plus confidence scoring

DodoForm's stated principle is "Messy in. Verified data out." Two design choices carry that promise:

• Per-field source tracing. Every extracted field is linked back to the part of the input it came from. The site frames this as "100% fields traced to source" — the point being that you can check where a value like a budget figure or a name actually appeared, rather than trusting the model's summary.
• Confidence percentages. Each field gets a score. In the site's own demo, a name is shown at 98%, a budget at 95%, and a timeline at 64%. The percentage is a signal of how sure the system is, not a guarantee of correctness — which is exactly why the low scores matter.

A field with high confidence and a clear source is something you can accept quickly. A field with a low score is the system telling you it isn't sure, and that's where review comes in.

What happens when the AI isn't confident

Low-confidence output doesn't get silently committed. DodoForm routes uncertain rows to a review queue for a human to confirm before they're treated as final. This is the key difference between "AI that doesn't invent your data" and a tool that fills every blank with its best guess: when the model can't ground a value, the value goes to a person, not into your records.

The same logic applies on the collection side. When someone answers a form by talking, pasting a messy note, or uploading an image, they confirm what the AI heard before it submits. So the person closest to the information catches errors at the moment of entry, and only confirmed responses become clean records.

The two paths, and how each stays verifiable

Both doors lead to the same output: structured records you can trust and route onward — for example, straight into an ATS or CRM.

A concrete example

Take the demo input on the site: a follow-up email from Jordan Avery at Northwind Logistics, mentioning a team of about 45, a move off spreadsheets by end of Q3, a budget of roughly $20k/yr, and a request for a call next Tuesday afternoon.

DodoForm's extractor pulls these onto a schema as separate fields. The name and budget come out with high confidence because they're stated plainly. The timeline ("by end of Q3") scores lower — 64% in the example — because "end of Q3" is vaguer than an exact date. That lower score is what sends the timeline to review rather than letting the system quietly decide Q3 means September 30.

What this means for you

If your concern is AI fabricating details in candidate emails, client briefs, or inbound leads, the relevant questions are:

• Can I see where each value came from? DodoForm traces fields to source, so yes.
• Does it flag its own uncertainty? Yes, via per-field confidence scores.
• Is there a human checkpoint? Yes — respondents confirm before submitting, and low-confidence rows go to a review queue.

The trade-off is that verification adds a step: you or a respondent review the uncertain fields instead of accepting everything automatically. For recruiting, agency, and ops workflows where a wrong email or budget figure causes real problems, that step is the feature, not the friction. You can test the extraction engine on the site without an account by pasting your own messy text, and the Pro trial runs 14 days with no card.