Hi HN,
I’m Afraz, an independent builder working on Vect AI.
One recurring issue I kept running into with AI-generated marketing content was that it often looked correct, polished, and on-brand — yet failed to resonate once published. This wasn’t a grammar or tone issue. It was a resonance problem.
Most AI tools are good at generating content, but they rarely answer a harder question upfront: Will this actually land with the intended audience? That gap led me to build what I call the Resonance Engine inside Vect AI.
Instead of publishing content and measuring engagement afterward, the system evaluates drafts before they ship by simulating a defined target audience and surfacing clarity, relevance, persuasion, and emotional alignment gaps early.
I’m sharing this mainly to discuss the idea itself — testing resonance pre-publish — rather than promoting a specific feature.
For those who like to inspect systems deeply, all public pages are accessible via a site operator:
site:vect.pro
You can explore the product directly here:
https://vect.pro
I’ve also documented the broader architecture, tools, and reasoning in detail here:
https://blog.vect.pro/vect-ai-bible-guide
Curious how others here think about:
Testing resonance before publishing
Audience simulation as a signal vs a trap
Where AI feedback becomes noise instead of insight
Happy to answer questions or discuss edge cases.
I’m Afraz, an independent builder working on Vect AI.
One recurring issue I kept running into with AI-generated marketing content was that it often looked correct, polished, and on-brand — yet failed to resonate once published. This wasn’t a grammar or tone issue. It was a resonance problem.
Most AI tools are good at generating content, but they rarely answer a harder question upfront: Will this actually land with the intended audience? That gap led me to build what I call the Resonance Engine inside Vect AI.
Instead of publishing content and measuring engagement afterward, the system evaluates drafts before they ship by simulating a defined target audience and surfacing clarity, relevance, persuasion, and emotional alignment gaps early.
I’m sharing this mainly to discuss the idea itself — testing resonance pre-publish — rather than promoting a specific feature.
For those who like to inspect systems deeply, all public pages are accessible via a site operator: site:vect.pro
You can explore the product directly here: https://vect.pro
I’ve also documented the broader architecture, tools, and reasoning in detail here: https://blog.vect.pro/vect-ai-bible-guide
Curious how others here think about:
Testing resonance before publishing
Audience simulation as a signal vs a trap
Where AI feedback becomes noise instead of insight
Happy to answer questions or discuss edge cases.