Most AI agrees with you. Foyl doesn't.
Foyl dismantles your argument across five phases of structured debate — and rebuilds it into something no one can take apart.
Foyl structures every inquiry through a rigorous questioning process. You don't just get feedback. You go through the fire.
Foyl takes your idea seriously and traces its logic to where it contradicts itself. The dissolution comes from within, not from external objections.
Your premises traced to their breaking point. What survives carries forward.
Foyl strips your argument to its load-bearing claims. What's structural and what's decoration? What's conviction and what's habit? This phase forces precision.
Your argument reduced to what's actually load-bearing. Only this carries forward.
Foyl follows your logic to its conclusions. If your position is true, what else must be true? You may not like what your own argument implies.
A map of where your logic actually leads. Some of it you didn't intend.
Foyl reconstructs your argument using only what survived. The naive version is gone. What remains is smaller, but it holds.
The refined position. Smaller than what you started with. Harder to break.
Foyl outputs the stress-tested version of your idea as a standalone artifact. Not a conversation summary. A publishable piece that stands on its own.
The artifact. Built to last.
Test your thesis before publishing. Foyl finds the holes your editor won't and the objections your readers will.
Run your plan through an adversary before committing resources. Cheaper than learning from failure.
Your co-founder agrees. Your investors nod. Foyl tells you what they won't: where your pitch falls apart.
Simulate a hostile reviewer. Stress-test methodology, assumptions, and conclusions before you submit.
Most AI is trained to make you happy. Foyl is trained to make you right.
1 Perez et al., "Discovering Language Model Behaviors with Model-Written Evaluations," Anthropic, 2022. Demonstrated systematic sycophantic behavior in RLHF-aligned models across political, philosophical, and technical domains.
2 Sharma et al., "Towards Understanding Sycophancy in Language Models," ICLR 2024. Five state-of-the-art AI assistants consistently exhibited sycophancy across four varied free-form text-generation tasks.
Your ideas deserve an adversary. Not a captive audience.
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