Name the job before the model
Start with the user, the task and the result. “Drafts support replies from approved documentation” is easier to evaluate than a broad claim about an intelligent assistant. Mention the model only when it changes the buyer’s decision, technical requirements or data handling.
Create proof a visitor can inspect
Show the input, the output and the amount of human review required. Use real interface screenshots and a short example that represents normal use rather than a carefully selected edge case. Explain important limits before a user discovers them during onboarding.
Prepare trust answers
Document what data is sent to model providers, whether prompts are retained, how users can delete data and where automated output should be reviewed. Keep this information consistent across the product, privacy page and external listings. Do not make security or accuracy claims without evidence.
Launch in feedback-first order
Begin with a small audience that understands the workflow and can identify unclear positioning. Fix the first session, examples and trust copy before using a larger launch platform. Add focused AI and software directories only after the category and product description are stable.
Measure repeat use
Launch attention can produce many one-time experiments. Track whether people complete the core workflow, return to use it again and understand when the output is useful. Feedback about reliability and control is usually more actionable than reactions to the novelty of the model.
Platform rules and pricing change. Always check the current official guidance before submitting your product.