Understand the product
Start with the website, positioning, audience, goals, competitors, languages, and tone.
AI-powered content engine for products
Products should have a voice.
ProductVoice helps founders and teams turn product truth into better content: signals become ideas, ideas become drafts, posts become results, and results teach the next round what actually works.
Not a generic scheduler
Not a one-shot AI post generator
A feedback-driven communication system
How ProductVoice works
A loop for product communication, not a blank prompt box.
Collect real signals
Turn launches, feature work, metrics, customer quotes, roadmap notes, and market shifts into structured inputs.
Generate sharp ideas
Use signals and past performance to propose angles, hooks, stories, comparisons, and build-in-public moments.
Refine into drafts
Iterate with AI until the post fits the speaker, channel, language, CTA, and visual direction.
Publish and measure
Schedule or manually publish, then track snapshots for impressions, clicks, saves, comments, and engagement.
Learn what works
Feed results back into future generation so every product develops a stronger communication memory.
The core workflow
Product to Signals to Ideas to Drafts to Posts to Results to Learning.
The moat is the last step. Every post keeps its inputs and outputs together, so ProductVoice can learn which topics, hooks, speakers, languages, channels, CTAs, and visuals perform for a specific product.
What is coming first
The MVP is focused on better posts through learning.
ProductVoice starts with the foundations that make the loop useful: rich inputs, strategic ideas, draft iteration, publishing records, metrics, and reusable learning insights.
Product onboarding with website parsing and positioning summary
Signal inbox for launches, insights, metrics, fixes, and customer quotes
Idea Lab with scoring, refinement, and founder/product speaker modes
Draft editor with CTAs, hashtags, variants, translations, and image prompts
Weekly content calendar based on goals, channels, campaigns, and audience fatigue
Metrics snapshots and recommendations that improve the next content cycle
Learning loop
ProductVoice remembers the context behind every result.
Instead of only storing likes, it stores the whole recipe: source signals, speaker, channel, prompt, language, visual style, CTA, posting time, and the metrics snapshots that follow.
What it learns
Voice
Founder posts outperform brand posts for launch stories.
Format
Short hooks work best on X; narrative posts travel further on LinkedIn.
Visuals
UI screenshots beat generic AI art for feature education.
Language
Spanish posts can be adapted late, after the English draft is optimized.
Built for channels, speakers, and languages from the start.
The system separates product voice from founder voice, generates drafts for the right channel, and adapts language late so translations keep the culture and platform behavior intact.
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Early access