AI & Technology

Assesses organizational readiness across edge AI governance, monitoring, risk, and MLOps practices. Designed for AI/ML leaders, DevOps, and compliance stakeholders to benchmark maturity and prioritize investment.

An operational audit survey for data labeling teams, measuring instruction clarity, bias mitigation practices, QA rigor, and workflow bottlenecks over the last 30 days. Designed for labelers, reviewers, and QA leads.

Measures consumer awareness, trust, acceptability, and behavioral intentions regarding AI content provenance watermarks, designed for technology policy researchers and platform designers evaluating labeling strategies.

Assess how content filter false positives affect developer productivity, workflow disruption, and tool adoption decisions. Designed for developer experience researchers and tooling teams seeking actionable improvement priorities from software practitioners.

Measures perceived realism, fit accuracy, and purchase confidence for augmented reality try-on features. Designed for e-commerce UX researchers seeking to identify AR experience gaps that drive returns and reduce conversion.

Measures AI tool adoption rates, usage barriers, quality-speed tradeoffs, and credit/disclosure norms among media creators across disciplines. Suitable for creative industry researchers and platform teams studying the creator-AI relationship.

Measures how users perceive the clarity, usefulness, and behavioral impact of AI product changelogs. Designed for product and developer experience teams seeking to optimize release communication and drive feature adoption.

Collects structured stakeholder feedback on red-team risk coverage, report quality, and remediation follow-through to identify actionable program improvements across security, engineering, and leadership functions.

An internal stakeholder survey evaluating scope clarity, decision fairness, and incentive effectiveness in your AI bug bounty program over the past 6 months to guide program improvements.

Measures how ML/AI practitioners engage with model cards, evaluate documented limitations, and how documentation quality shapes trust and adoption decisions across deployment contexts.

Measures organizational readiness across AI policy clarity, approval workflows, risk tiering, and control maturity. Designed for cross-functional teams involved in AI development, deployment, or oversight.

Measures user interest, perceived value, adoption barriers, and willingness to pay for AI-powered product features. Designed for SaaS product teams prioritizing their AI roadmap based on user feedback.

Measures consumer expectations for AI transparency across products and services, capturing preferred disclosure methods, acceptability thresholds, and trust drivers to inform product labeling and policy decisions.

Measures user adoption, satisfaction, trust, and pain points with AI-assisted product features. Use it to capture actionable feedback that informs product roadmap and feature prioritization decisions.

Measures user expectations for AI agent autonomy, preferred escalation and handoff mechanisms, permissible actions, spending thresholds, and risk concerns. Designed for UX researchers and product teams building agentic AI workflows.