We are looking for a small number of expert practitioners for a paid 30-60 minute product feedback interview. The client is building AI technology to help detect when people in high-stakes conversations may be relying on scripts, coaching, AI tools, generic answers, or overstated expertise.
This is not a job application. This is not for general industry opinions. We only want people who have personally evaluated authenticity, quality, credibility, compliance, provenance, or first-hand knowledge in one of the tracks below.
Please do not apply if your experience is mostly adjacent, junior, academic-only, agent-only, administrative, or theoretical. You should be able to give concrete examples from work you personally owned or reviewed.
Tracks covered by this shared form: Expert Network / Primary Research; Financial Data / Transcript Data; Investor Relations; Qualitative Research; Academic Research; Contact Centre Quality & Compliance.
Selected advisors may be invited to a short 10-15 minute fit call, then a paid 30-60 minute product feedback interview scheduled around them. Payment is confirmed on selection.
What you'll do
- Share first-hand product feedback from the exact track where you have direct practitioner experience.
- Explain how authenticity, credibility, quality, compliance, provenance, first-hand knowledge, scriptedness, AI assistance, or overstated expertise are evaluated in your field.
- Walk through concrete examples where you personally checked whether a person, answer, source, dataset, call, transcript, or claim was credible and first-hand.
- Describe the tools, workflows, scorecards, audit methods, quality signals, or review processes used today, including where they fail.
- Advise what a new AI detection or verification approach would need to prove before practitioners in your field would trust or act on its flags.
Must have
- Direct hands-on experience in one of the listed tracks, not merely adjacent exposure.
- Personal experience evaluating whether people, answers, sources, data, claims, or expertise were authentic, first-hand, scripted, AI-assisted, generic, or overstated.
- Concrete examples from work you personally owned, reviewed, QA'd, audited, moderated, investigated, or escalated.
- Enough seniority or ownership to explain real workflows, tools, failure modes, and decisions, not only individual task participation.
- Comfort with a short fit call and, if selected, one paid 30-60 minute product feedback interview.
Nice to have
- Ownership of QA, compliance, research, data quality, expert screening, investor communications, transcript review, fieldwork, or audit workflows.
- Experience with regulated, high-stakes, or decision-critical conversations where scriptedness, coaching, AI assistance, or overstated expertise creates risk.
- Experience using or designing scorecards, calibration workflows, evidence checks, panel validation, transcript QA, dataset lineage, reconciliation, disclosure review, or compliance audits.
- Ability to explain what signals would be useful, what would create false positives, and what proof would be needed before a team trusted the product.
