Rigor and reproducibility, built in.
Transparent methodology, reviewable source quotes, and honest quality controls — not a black box. Built for researchers who need the study, data, and methods to hold up under review.
Most AI research tools are black boxes.
Proprietary AI-moderated tools promise speed but hide the method. Prompts, probing logic, and quality rules are invisible — which means studies aren't reproducible, and reviewers have no way to evaluate the methodology.
Academic research needs the opposite: transparent methods, inspectable settings, and data you can defend at peer review. Not a vendor's promise.
“Methods you can publish. Data you can defend.”
Why academic teams use it.
Transparent methodology
Every prompt, probe rule, and quality check is visible and exportable. Studies are reproducible by design, not by accident.
Reviewable source data
Raw responses, attention flags, and themed reports export together. Reviewers see the evidence, not just the conclusion.
Quality you can report
Attention check rates, completion times, and response quality scores — all captured automatically.
IRB-friendly workflows
Anonymity, consent flows, and data residency controls are built in. Configure once, apply to every study.
Built for academic teams.
Validation batteries, qualitative protocols, course evaluations — templates with transparent methods and exportable settings.
Course Evaluation: Teaching Effectiveness & Workload
Collects structured student feedback on instructor effectiveness, course materials, workload balance, and learning outcomes to inform end-of-term course improvements at the undergraduate or graduate level.
View templateAcademic Researcher Experience & Support Survey
Measures how graduate students, postdocs, and faculty actually spend their research time, and how well funding, mentorship, protected time, and collaboration support their work. An AI follow-up interview digs into the single biggest obstacle each respondent names, reconstructing a concrete recent example instead of a vague complaint. Built for research offices, deans, and PIs benchmarking research support.
View templateSocial Studies Course Evaluation Survey
Measures how students experienced a social studies course — material relevance, teaching clarity, discussion quality, and which units actually landed — for teachers, department heads, and curriculum teams reviewing a course after a term. An AI follow-up interview digs into the reasoning behind students' recommend score and topic preferences instead of stopping at the number.
View templateReproducibility in AI-Moderated Research: A Researcher Assessment
This survey explores researcher experiences with reproducibility, transparency, and data quality in AI-moderated research. It serves as a replication study template to understand current practices, identify barriers, and assess the role of platform transparency in enabling reproducible AI-moderated studies.
View templateResearch that holds up.
Run your first study free. Twenty responses on the house. No credit card.