Verified, Not Hallucinated: How DodoForm Grounds Every AI Field

The problem with "the AI guessed"

Generic LLMs are eager to please. Ask one to pull a budget from an email and it will happily return a number — even if that number was never in the email. When you're routing candidate and client data into a CRM, a confident hallucination is more dangerous than a blank field, because nobody catches it.

DodoForm is built so that doesn't happen. Every value the AI produces is grounded in its source and checkable by a human.

The trust loop, step by step

1. Constrained extraction

We don't ask the model for a free-form blob. A constrained extractor maps messy text, files, images, or audio onto your exact schema — the specific fields you defined. The model fills your shape; it doesn't decide what to return.

2. Snippet guard

This is the core of it. Every extracted field must trace to an exact snippet in the source. If the model can't point to where a value came from, the value is dropped, not guessed. A missing field is honest. A fabricated field is a liability.

3. Confidence gating

Each field carries a confidence score, and you set the bar — say 70%. Confident fields auto-approve. Anything below your threshold can't auto-approve; it's held for a human.

4. Human review queue

Low-confidence fields land in a review queue for a one-click correction. The human stays in the loop exactly where the machine is unsure, and nowhere it isn't — so review effort goes only to the rows that need it.

5. Corrections that compound

Every correction your team makes is fed back to the extractor as guidance. The model gets better at your data over time, so the review queue shrinks as accuracy climbs.

Why this matters more than raw accuracy

Plenty of tools advertise extraction accuracy. The number that actually matters is trust: can you let a record sync to your CRM without a person eyeballing it? With snippet grounding and confidence gating, the answer is yes for the confident majority — and a fast human check for the uncertain minority. You get speed and a verifiable trail.

It's also a claims-and-reality stance we take seriously: we'd rather under-promise on a field and drop it than over-promise and pollute your database.

Where you see it

The same loop runs whether data comes through a form (the respondent even confirms what the AI heard before submitting) or the Extractor (you review low-confidence rows before they're trusted). Confidence and source travel with each field all the way to your CRM.