Tenth Man offers an AI‑driven decision intelligence platform that uses a three‑agent adversarial architecture—Strategist, Skeptic, and Synthesizer—to deliberately generate dissent and surface hidden risks. The system produces a structured decision brief containing a recommendation, explicit assumptions, accepted risks, unresolved disagreements, and a confidence score, helping users avoid single‑point‑of‑failure bias in critical business choices.
Funding
Funding not disclosed
Founders
Product
Problem
Business decision processes often rely on single-model AI outputs that reinforce existing biases, leading to blind‑spot errors and overconfidence in recommendations. Without structured dissent, teams may miss critical risks and unresolved disagreements, compromising strategic outcomes.
Solution
Tenth Man delivers an adversarial decision‑intelligence platform that runs three specialized AI agents—a strategist, a skeptic, and a synthesizer—to generate purposeful conflict around each decision. The system produces a concise decision brief that includes a clear recommendation, explicitly listed risks, any unresolved disagreements, and a bounded confidence score. By enforcing mandatory dissent, explicit assumptions, and confidence caps, the platform surfaces hidden blind spots and reduces overreliance on any single model’s perspective. Users submit a decision prompt rather than engage in a chat, ensuring no hidden memory or invisible bias correction influences the analysis. The output is designed for transparent, auditable decision‑making, helping leaders make more balanced, evidence‑based choices.
Target Audience
Primary users are senior executives, product leaders, and investment decision‑makers who need rigorous, bias‑aware analysis of strategic choices such as fundraising, market entry, or product launches.
Features
- Three-agent architecture (strategist, skeptic, synthesizer) that creates structured conflict rather than blended consensus
- Automated generation of a decision brief with recommendation, accepted risks, unresolved disagreements, and confidence score
- Explicit assumption tracking and mandatory dissent to surface blind‑spot errors
- Confidence caps that bound the certainty of recommendations based on identified uncertainty
- No hidden memory or persistent bias; each analysis is independent and reproducible
- Simple prompt‑based interface that replaces traditional chat interactions for focused decision input