Field notes
Notes from the research engine.
Essays on sampling, data quality, and getting quant out the door — written by the team that does the work. No trend-chasing. Just how it actually gets done.
Why Data Quality Tools Don’t Agree (And Why That Matters)
We often multi-source when audiences are difficult—and let’s be honest, most audiences are challenging in one way or another. The question becomes: how can we be sure the data quality tools we rely on are actually doing their job?
Reaching audiences that only exist at the intersection of three screeners — and staying with the study until they show up.
Data quality What actually stops survey fraudDevice fingerprinting, in-survey traps, and identity verification — layered, and audited per audience instead of applied uniformly.
Spoiler-AI Why we code open-ends on offline modelsRunning AI analysis on dedicated GPUs we control — never piped through a public API — with a coding frame a human approves before it counts.
Operations The case for one team from build to deliveryWhat breaks when a study passes through three vendors — and what doesn't when the same team owns it end to end.
Feasibility Know before you pitchHow a free feasibility check in 24 to 48 hours changes the way you scope — and price — a study for your client.
Programming Two QA layers, every timeWhy we test every survey twice — independently — before a single real respondent ever sees a question.
Have a study that fits one of these?
Tell us the audience and the timeline. We'll tell you what's feasible.