Cleaner Form Data: AI Input Validation and Drop-off Analytics
Most form tools stop at format validation and an aggregate completion rate. SiliForm adds an AI layer that catches plausible-but-fake answers, and question-level drop-off analytics that show exactly where respondents give up.
test@example.com or a sequential phone number — and separately tracks drop-off at the question level, so you know both whether your data is real and where people stopped answering.The problem: forms report completion, not quality
Every form tool tells you how many people submitted. Almost none tell you whether what they submitted was real, or exactly which question caused the people who didn't submit to leave. Both gaps quietly corrupt the same thing: the dataset you make decisions from.
- Lead forms fill up with
test@test.comand repeated digits that pass every regex check - Long surveys lose respondents at a specific question, but the only signal you get is a single completion percentage
- Low-effort answers — keyboard walks, copy-pasted placeholder text — sit in your export next to genuine responses, indistinguishable until someone reads every row
Layer 1 and 2: format and heuristic checks
Before anything reaches an AI model, SiliForm runs two fast, deterministic passes on every text-type answer:
- Format validation — email shape, phone digit count, name character set. Standard, and necessary, but it stops at "does this look like the right shape."
- Heuristic validation — rejects answers that are too short or too long for the question, repeated-character strings, a high ratio of special characters, or basic profanity.
These two layers catch obviously broken input instantly, with no API call and no latency. Most form tools stop here.
Layer 3: the LLM soft-check nobody else runs
Once an answer passes format and heuristic checks, SiliForm sends it to an LLM with one job: decide if it's plausible-but-fake — the kind of answer a bot, a rushed respondent, or someone gaming an incentive would type, that a regex can never catch because it's technically well-formed.
- Emails like
admin@,noreply@, or a disposable domain (mailinator, guerrillamail, yopmail) - Sequential or all-repeated phone numbers —
1234567890,5555555555 - Keyboard walks and gibberish —
asdfgh,qwerty - Lorem Ipsum or text that's clearly off-topic for the question asked
The check is deliberately conservative: it's instructed to flag only when suspicious, and to return a short, non-accusatory nudge rather than a hard rejection — "double-check that email" reads very differently from "invalid input." If the AI provider is unreachable, validation fails open so a slow API never blocks a real submission.
The other half: drop-off analytics at the question level
Bad data is one failure mode. The other is data you never receive at all, because someone started your form and left. Most tools report that as a single number — "62% completion" — with no way to act on it.
SiliForm saves in-progress answers as partial responses, tied to the exact question the respondent last reached. The drop-off view aggregates these by step, so instead of one completion percentage you get a chart of exactly where people stall:
- Which specific question has the largest drop between it and the next
- How many in-progress drafts exist right now, not just finished submissions
- Partial answers retained as usable data, instead of discarded on abandonment
Validation and drop-off are two views of the same problem
Input validation raises the floor on what counts as a real answer. Drop-off analytics shows where the form itself is the reason people don't finish. Run together, they answer the two questions that actually determine whether a dataset is worth acting on: is this response real, and if fewer people finished than expected, which question is responsible?
Where this matters most
- Lead generation — fewer fake emails and burner numbers reaching your CRM or ad-platform conversion events
- Research and surveys — catching straightlining and low-effort open-text answers that would otherwise dilute qualitative analysis; see why psychology surveys fail for the underlying methodology problem
- Long intake or application forms — pinpointing the exact question causing abandonment instead of guessing from a completion rate
A higher completion rate means nothing if a third of what completed was fake — and a completion rate alone can't tell you which question to fix.