I’m Afraz, an independent builder working on Vect AI.

One problem I kept running into with AI-generated content was that it often looked correct, polished, and on-brand — yet failed once it reached real users. This wasn’t a grammar or tone issue. It was a resonance problem.

Most tools help generate content, but they don’t answer a harder question upfront: Will this actually land with the intended audience?

To solve this, I built the Resonance Engine inside Vect AI.

Instead of publishing content and measuring engagement later, the Resonance Engine evaluates drafts before they ship by simulating a defined target audience and surfacing clarity, relevance, persuasion, and emotional alignment gaps early.

At a high level:

Brand context and audience profile are defined once

Draft content is evaluated against that audience, not generic writing rules

Feedback highlights what feels unclear, generic, or misaligned

The goal is to expose weak assumptions before real users see the content

This has been most useful when:

Content sounds polished but lacks a strong hook

Messaging fits the brand but not the audience’s real concerns

AI-generated copy feels technically right but emotionally flat

I’m not trying to replace human judgment. The aim is to reduce blind spots earlier in the workflow, when decisions are still cheap.

For transparency, all public pages can be inspected here: https://www.google.com/search?q=site:vect.pro

You can try the Resonance Engine directly here (deep link into the tool): https://vect.pro/#/signup?continue=%2Fapp%2Ftools%3Ftool%3DR...

I’ve also documented the broader system and reasoning behind Vect AI here: https://blog.vect.pro/vect-ai-bible-guide

Curious how others here think about testing resonance before publishing, audience simulation as a signal, and where AI feedback becomes noise instead of insight. Happy to answer questions or discuss edge cases.