Science &amp; Journal

A Checklist for Reviewing Confidential Client Ads with AI

Check permissions, processors, storage, retention and sharing before uploading confidential ads. Includes SaliencyLab disclosures and a checklist.

How to Check an AI Creative-Testing Claim

Evaluate AI ad-testing claims by model, outcome, holdout and uncertainty. Includes research sources, SaliencyLab examples and a buyer checklist.

What Is Pre-Spend Creative Intelligence? The Pre-Post Gap Explained

Pre-spend creative intelligence evaluates ad creative before budget commits, inside the Pre-Post Gap where the ad is still editable. Here is the definition, and what the category is not.

Three layers of scoring: from raw signals to KPI families to creative verdict

A single AI score for an ad is useless. RoastIQ scores in three layers: raw perception signals, sub-KPI families, and the 5 main KPIs with weighted composite.

Visual attention prediction vs. eye tracking: what we can and cannot claim

RoastIQ uses TranSalNet to predict where the eye is likely to look. This is not eye tracking. Here is exactly what the model does and where it breaks down.

From RoastIQ score to buyer objection: how context makes synthetic interviews sharper

BuyerLens never starts from scratch. Every interview begins with the RoastIQ result. This context transforms generic AI interviews into focused pressure tests.

When a BuyerLens study is strong enough to act on

We do not show a single accuracy percentage because that would be misleading. Instead, we show coverage indicators that help you judge study strength.

From RoastIQ to BuyerLens to brief: the decision loop in one workspace

The creative decision loop has four steps: score, understand, fix, rerun. In SaliencyLab, all four happen inside one study with context flowing automatically.

Company News

SaliencyLab selected for GITEX Africa 2026 as part of Morocco 300

SaliencyLab has been selected to attend GITEX Africa 2026 in Marrakech as part of the Morocco 300 cohort, recognized among the top innovative startups in the country.

Product Updates

RoastIQ v3: Sub-KPI breakdown and 3-layer scoring

The latest RoastIQ update introduces sub-KPI components for Get Noticed, Sell Proposition, and Build Brand, giving creative teams granular insight into what drives each score.

Product Updates

Meet Hugo: your autonomous creative intelligence agent

Hugo is an AI agent that runs the full diagnostic pipeline from a single conversation. Upload a video, ask a question, and Hugo handles everything.

Company News

Benchmark pool reaches 500 ads across Meta, TikTok, and YouTube

Our benchmark pool now covers 500+ scored ads from ad transparency APIs across Instagram Reels, TikTok, YouTube Shorts, and Facebook Feed.

Ad Attention Heatmaps Explained: How AI Predicts Where People Look

An attention heatmap predicts where the eye is likely to land. It is a model output trained on aggregated fixation data, not a recording of anyone looking at your ad.

AI Ad Creative Scoring vs A/B Testing: Which One and When

Scoring predicts before you spend. A/B testing measures after you have. They answer different questions, and the useful move is sequencing them rather than picking one.

How to Interpret RoastIQ KPI Scores

A practical guide to reading RoastIQ KPI scores, benchmark labels, and the next action they support.

Benchmark Interpretation Guide for Creative Teams

How to use benchmark context correctly without letting norms replace strategy or judgment.

When to Use BuyerLens After RoastIQ

BuyerLens works best when RoastIQ has exposed a tension the team still cannot resolve.

How to Reduce Wasted Ad Spend with Pre-Spend Creative Testing

Most creative-driven waste is spent discovering structural problems that were visible in the asset before launch. Screening removes that category of loss.

How to Test Ad Creative Before Launch

Four ways to test creative while it is still editable, compared honestly on cost, speed, and evidence. Then the decision side: the review order, what usually means revise, and the checklist to clear before budget moves.

Why Your Ad Gets Skipped in the First Five Seconds

The most common first-seconds attention failures and how to fix them before spend goes live.

How to Read a RoastIQ Report

Read the decision cue, the KPI pattern, the benchmark context, and the evidence in the right order.

Platform-Specific Creative Best Practices for Meta, TikTok, and YouTube

A practical guide to adapting hooks, brand cues, and proposition timing across the major paid video platforms.

Coca-Cola Demo Report Walkthrough

A guided walkthrough of the public Coca-Cola demo report and what teams should learn from it.

6 Ad Creative Testing Tools Compared for 2026

Compare Neurons, Kantar LINK AI, System1, Zappi, Motion and SaliencyLab. See their evidence, use cases and what to ask before choosing an ad testing tool.

Synthetic Users for Marketing Research: Benefits, Risks, and Best Practices

AI-generated research participants are fast and cheap, which makes them useful for early exploration. They are not real people, so their output is directional input to validate, never final evidence.

Synthetic Consumers vs. Real Consumer Panels

Synthetic wins on speed, cost, and iteration. Real panels remain necessary for validation, novel categories, and representative sampling. The best results come from sequencing them.

What Makes a Good AI Creative Testing Tool?

Nine criteria for evaluating vendors, starting with the one most avoid: what were your predictions validated against, and at what sample size?

Synthetic User Platforms: 3 Options Compared for 2026

Compare Synthetic Users, Evidenza and BuyerLens for interviews, market research and ad feedback. Learn what to check and when you need real participants.

AI Marketing Research Tools: Which One Should You Choose?

Choose by the decision at stake and how expensive it is to get wrong. Cheap, reversible decisions suit fast AI tools. Irreversible ones still need real customers.

Synthetic Users for Ad Testing: A Practical Guide

A six-step workflow for testing ad clarity, audience fit, and objections before launch, plus an honest account of where the method breaks.