afrazullal

We found that 90% of marketing overhead is just "human latency"—waiting for copy, waiting for designs, waiting for approval.

We replaced that entire layer with an autonomous state-machine we call Vect AI. It’s not a "writing tool"; it’s an autonomous operating system that:

Researches market signals (finding "blue ocean" opportunities). Architects entire campaigns (landing pages + ads + emails). Executes the assets (using specialized agents for video/code/text). Validates quality against a psychological "Gatekeeper" node before you ever see it. The result isn't just "content"—it's an Infinite Content Glitch. We are producing agency-level output at 100x the speed for 1/1000th the cost.

If you are a solo founder or running a lean team, this is the asymmetric advantage you’ve been looking for. Stop managing people. Start managing compute.

This post is about a different approach: validating social posts before you publish them.

What the Social Media Post tool does (job first)

Accepts a draft or brief and evaluates it against a saved brand profile and audience.

Produces a pre-publish score for hook, clarity, relevance, and expected engagement.

Flags weak hooks, generic phrasing, and CTA problems and suggests focused A/B variations.

Outputs concrete next steps (refine hook, split test two angles, recommended copy variations) and saves the draft as a named project ready for distribution.

Why this matters for buyers

Removes guesswork before ad spend or distribution.

Converts creative debate into measurable go/no-go signals.

Shortens review loops for teams and agencies that must justify spend.

Who this is for Founders, growth operators, content leads, and agencies who run paid or high-impact organic campaigns and need predictable outcomes — not experimentation.

How it fits into a workflow

Draft a post or import copy.

Run the pre-publish analysis → get a score + precise edits.

Save as a project and decide: ship, schedule, or iterate. This is intentionally designed for teams that will onboard and pay for predictable performance improvements.

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