Know which headline wins before you spend traffic on it.
Stage
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Based in
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Pricing model
Freemium
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Pricing
Free plan with 2 requests per day; Basic $15/mo (200 generations/month); Pro $49/mo (800 generations/month); Premium $99/mo (2,000 generations/month); API: $2.50 per million input tokens, $12 per million output and thinking tokens, rounded up to nearest cent with 1 cent minimum per request.
Free: $0/month with 5 rankings and 60 Claude-written options monthly. Creator: $19/month with 100 rankings and 2,500 options. Pro: $49/month with 600 rankings and 6,000 options. Agency: $149/month with 3,000 rankings and 20,000 options.
Key features
Distribution fine-tuning for natural writing output
Browser-based console for generating and rewriting
Server-to-server API with /v1/generate endpoint
Specialized agents for SEO, rewriting, and copyediting
Custom model training on company style
Generation history and public share links
AI advising for AI transformation and strategy
Lightweight metrics and checking tools in editor
Head-to-head comparison of up to 200 variants
Bradley-Terry model ranking with calibrated odds
Honest uncertainty verdicts: Clear, Lean, Too close to call
Brand profiles to calibrate accuracy on your audience
Works for YouTube titles, subject lines, ad headlines, and article headlines
Accuracy measured on 9,841 held-out A/B tests
What makes it different
Trained on human prose to eliminate repetitive AI patterns and 'LLM slop'
More creative and varied output than general-purpose AI assistants
Custom models trained on your company's specific writing style
Preserves author's intent and voice without hallucinations
Usage-based API pricing with no charges for failed generations
82% accuracy on clear calls, trained on thousands of real A/B tests never seen during tuning
Honest about uncertainty instead of always crowning a winner
56 times cheaper than Claude Sonnet while maintaining comparable accuracy
Custom calibration to your audience's behavior via brand profiles
Compare versions in both orders to cancel out model bias