ImagePredict
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Creative experimentation for performance marketing

Predict which ad images will perform before you spend.

We generate, test, and measure advertising images using real products. Every image is a controlled experiment. Performance data compounds into prediction.

Apply for beta See how it works
Real products in studio Fully instrumented images Performance-linked learning No consumer PII stored
What you get
A simple loop: create variants, test them, learn fast, then predict.
Faster iteration

Generate consistent, on-brand variants tailored to placements and audiences.

Clear answers

See which visual choices work for which audiences, and why.

Less wasted spend

Promote winners sooner and kill losers faster.

Prediction over time

Each campaign improves the next. Learning becomes an asset.

Built for every high-spend category on Meta — consumer brands, apps, services, and political or advocacy campaigns.

How it works

We turn creative uncertainty into measurable learning, then into prediction.

1
Photograph real productsControlled studio capture for texture, drape, reflectivity, and category-specific lighting.
2
Generate targeted variantsCreate demographic-, placement-, and channel-specific creative from a single product shoot.
3
Test in real campaignsRun live tests with real spend. The market is the lab.
4
Measure performanceResults tie directly to image attributes, product details, and audience context.
5
Predict before scalingRank future creatives before spend and scale only high-confidence variants.

Generate. Measure. Learn. Repeat.

We create controlled creative variants, measure lift, and compound what works across products and audiences.

Controlled variation, not guesswork

For each product, we generate families of near-identical creatives where only a few variables change at a time. That enables clean experiments and clear attribution: which visual decisions actually move metrics.

Visual analysis layer

Extract measurable attributes from every asset: composition, lighting cues, color palette, product prominence, and subject signals.

Variant generation

Text-to-image and image-to-video pipelines produce targeted A/B/n sets quickly while keeping everything else constant.

Experiment design

Variants are tested in real campaigns with real spend so results are comparable and decision-grade.

Compounding learning

Every test updates the system so future generations start smarter and waste less spend.

Public transparency signals

We use public transparency data to understand market-level spend patterns and creative volume. It’s not performance data — but it’s a useful lens on what advertisers run and how activity shifts over time.

Explore the dataset
Explore Meta Ad Library spend data
Use it to spot creative “category patterns,” ad volume shifts, and market-level campaign themes.

Agentic creative workflow

Our dashboard connects concept ideation, automated prompt planning, generation execution, and a campaign CMS that organizes images, videos, and copy by objective, channel, and audience.

  • No manual prompt writing: humans set intent and constraints; the system generates a structured prompt plan.
  • Human in the loop: choose how many variants to produce and which variables to explore.
  • Images to video: transform variant families into short-form clips for Reels, Stories, and ads.

What we measure (and why it matters)

We track the variables that drive performance so creative stops being subjective.

Product truth

SKU, category, material, price band, colorway. Enables apples-to-apples learning across similar products.

Capture decisions

Lens, lighting setup, framing, background, crop. Reveals which visual treatments actually convert.

Model attributes

Stored as broad categories (age range, gender presentation, body type). Shows what resonates with different audiences.

Channel context

Platform, placement, geo, audience segment, spend window. Separates creative impact from media effects.

Outcomes

CTR, conversion proxies, CPA signals, client-defined success metrics. Ties creative to business results.

Learning over time

Normalization + experiment tracking turns performance into retained knowledge that improves future creative.

Why ImagePredict improves over time

Most creative workflows produce files. We produce measurable learning that accumulates.

What brands do today
Fast production, but learning resets.
A
StudiosDeliver assets. Performance feedback rarely returns to production.
B
AgenciesOptimize campaigns, but creative fundamentals remain guesswork.
C
AI toolsGenerate images without consistent provenance or performance memory.
What ImagePredict does
Every campaign produces retained knowledge.
1
Instrument the imageWe record what you controlled: product details, capture choices, model attributes, context.
2
Link to outcomesPerformance metrics tie back to the exact creative configuration.
3
Improve future creativeLearning compounds into a prediction layer that reduces waste over time.
Outcome: higher confidence creative, faster iteration, less wasted spend.

Privacy and compliance

Designed to be enterprise-ready: aggregated signals only, no consumer identity data.

No consumer PII stored

We do not collect names, emails, device IDs, or personal identifiers. We store aggregated performance metrics.

Categorical descriptors only

Model attributes are stored as broad categories for analysis, not identifying traits.

Policy-aligned workflows

Designed to operate within major ad platform policies and support client retention and compliance requirements.

See what actually works

Tell us what you sell and where you advertise. We will propose a pilot focused on learning, not volume.

Best fit: fashion, accessories, jewelry, and watch brands actively spending on paid media.