I built a system I call a Resonance Engine to answer a problem I kept running into: content performance is usually judged after publishing, when it’s already too late to change direction.

The Resonance Engine is designed to simulate how content might land with different audience contexts before it goes live. Instead of guessing tone, clarity, or intent alignment, the system evaluates messaging against multiple audience lenses and scores it on factors like clarity, relevance, emotional alignment, and friction.

This isn’t traditional A/B testing or analytics. There’s no traffic involved. The goal is pre-publication feedback that helps decide what to publish, not just how to optimize after the fact.

Some ideas behind the system:

Treat audience reaction as a system that can be modeled, not intuition

Evaluate resonance before distribution, not after engagement drops

Focus on messaging clarity and intent alignment, not vanity metrics

Use simulation to reduce wasted content cycles

The Resonance Engine is part of a broader marketing OS I’m building called Vect AI, but this component started as a standalone experiment to reduce guesswork in content and campaign creation.

Sharing this here to get feedback from others working on content systems, decision-support tools, or alternative approaches to testing messaging before it reaches real users.