Your next great hire is hiding in plain sight.
Resumes pile up. Opinions diverge. The best candidate gets lost in the noise.
What if every qualification was argued, challenged, and judged — without bias?
Even great hiring teams face invisible challenges.
Research shows that even experienced evaluators are affected by cognitive biases. They're human, not a failure of skill — but they can be addressed with structure.
Anchoring bias
The first resume sets the bar. Everyone after is compared to that — not the job.
Recency effect
The last candidate feels freshest. Earlier ones fade regardless of qualifications.
Gut feeling
"I just have a good feeling about this one." Intuition is valuable — but works best backed by structured evidence.
Choose your format.
Both modes augment your evaluation process with the same research-backed structured comparison: AI advocates argue each candidate's case, an independent judge scores anonymized arguments across 5 dimensions.
Head-to-head comparison
Upload two resumes and watch AI advocates debate their merits across 3 adversarial rounds in real time.
- Live streaming debate view
- Scored verdict on 5 dimensions
- Downloadable PDF report
Tournament ranking
Upload up to 32 resumes. A tournament runs head-to-head debates across multiple rounds, with playoffs for the top 4.
- Swiss-system pairing (like chess)
- Live standings + match progress
- Semifinal + final playoff bracket
Built on peer-reviewed science.
The structured evaluation approach is grounded in published, peer-reviewed research from AI safety and structured hiring assessment.
“It is harder to lie than to refute a lie.”
— Irving, Christiano & Amodei
AI Safety via Debate, 2018 ↗
AI Debaters are More Persuasive when Arguing in Alignment with Their Own Beliefs
Carro, M. V., Spinelli, N. et al., 2025
Across 145 scenarios with four frontier models, debaters who argue for what they genuinely believe are more persuasive — even when opposing arguments appear stronger on the surface. The debate mechanism naturally surfaces truth.
Debating with More Persuasive LLMs Leads to More Truthful Answers
Khan, A. et al., 2024
Non-expert human judges achieved 88% accuracy when evaluating debate transcripts, compared to just 60% when reading a single expert opinion. Debate dramatically extends what a non-expert can reliably evaluate.
AI Debate Aids Assessment of Controversial Claims
Freedman, R. et al., 2025
Debate reduces harmful belief reversals from 22.9% to 8.6% — judges who start with the right answer are far less likely to be misled.
Ready to hire with evidence?
Give your hiring intuition the structured evidence it deserves.