Our method
An agreeable answer is easy to get. A useful analysis has to survive a serious challenge. We combine adversarial AI with human collaboration to find the assumptions your decision depends on.
The engagement
The exact work is tailored to your project. This is the structure we use to shape it together.
01 / Frame
We clarify the decision, alternatives, constraints and standard of evidence with you. A useful question is specific enough to contest, and consequential enough that changing your mind would matter.
Your contribution: context, current beliefs and the information you can appropriately share.
02 / Challenge
AI-generated arguments take opposing positions, respond to objections and expose where the disagreement really sits. The aim is to test the thesis rather than simply ask a model to agree or disagree.
What we look for: missing alternatives, fragile assumptions and evidence that cuts against the initial view.
03 / Examine
We examine the reasoning with you, distinguish sourced evidence from assertions and identify claims needing further verification. A judge’s preference can be informative without being a finding of fact.
What matters: why a conclusion holds, where it fails and what remains unresolved.
04 / Synthesize
The agreed deliverable can bring together the competing cases, the decisive objections and the next questions to investigate. We work through the implications with you rather than leave you with an unexplained score.
The result: a clearer account of what would justify proceeding, changing course or gathering more evidence.
Decision-useful, not just readable
Depending on the agreed scope, an engagement may include an argument map, opposing cases, a decision brief, supporting transcripts or a discussion of the findings.
We agree the question, materials, deliverables, timing and fees before work begins. Sensitive information needs an agreed handling approach before you share it.
Limits, made explicit
Generated arguments can contain factual errors or invented citations. Important claims need independent verification before they carry a real-world decision.
Model judges can reward presentation, share blind spots or disagree. A winning argument is not proof that its conclusion is correct.
Research findings depend on the models, questions and evaluation used. They are not customer results or guarantees of financial, legal or commercial outcomes.
Research behind the approach
Compelle’s historical Bittensor research examined adversarial AI debate in a competitive setting. The published papers explain the experiments, findings and limitations. They provide research context, not a fixed specification for every client engagement.