Bots got better. Your quality checks need to match

What we're up against

Incentives always attracted cheats; generative AI made them scalable. Scripted completes, LLM-written open-ends, professional respondents cycling panels — assume a slice of raw sample traffic is bad and design accordingly. Anyone selling you sample without a fraud story is selling you noise.

Defense in layers

• Before the survey: Device, browser, and network fingerprinting at entry.
• During: Timing and behavior patterns that separate humans from scripts.
• Open-ends: Screening for LLM phrasing, template reuse, and fluent nonsense.
• Consistency: Traps, reverse-coded items, and cross-answer logic checks.
• After: Deduping respondents across projects to catch panel-hoppers.

Price it in, don't bolt it on

When comparing bids, score data-quality infrastructure like a line item. A cheap sample with 15% fraud is the most expensive option on the table — every decision downstream inherits the error.

How we run it

Blended sources so no single feed can poison a study, live screening with human review of edge cases, and termination and removal rates reported to the client. If your supplier won't share those numbers, ask why.