white to blue background gradient Commerce-Native AI for Merchandising and Personalization | Fast Simon Inc.
Commerce-Native AI

AI That Thinks Like Commerce

Fast Simon's Commerce-Native AI model understands commerce dynamics: inventory, new arrivals, brand, intent, velocity, and price. Merchants define the strategy. The model optimizes that strategy.
Fast Simon's Commerce-Native AI model understands commerce dynamics: inventory, new arrivals, brand, intent, velocity, and price. Merchants define the strategy. The model optimizes that strategy.
Revenue Uplift | Brand Expression | Merchandising Control
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Every Merchandising Decision Comes Down to Three Questions

How Do You Inspire a Shopper to Notice a Product They May Want to Buy Now?

How Do You Keep the Brand Expression Consistent as You Scale?

How Do You Satisfy All The Commerce Constraints While Maintaining Brand Expression and Business Goals?

One Shopper, One Product, One Moment

E-commerce requires trade-offs and variables for every product position, from inventory and margin to newness and brand story. Personalization multiplies these across millions of possible storefronts. But every product a shopper sees has to get three things right at once.
One Shopper, One Product, One Moment

Getting All Three Right Takes AI Built for Commerce, Not a Generalist LLM

Generalist LLMs fall short on two counts. They need enormous traffic, suffer from data sparsity, and require months of training to work, a luxury most brands cannot afford. And they have no built-in understanding of branded commerce: what's new, what's in stock, what's on promotion, and what the business needs to sell.

Merchants Define the Strategy. Commerce-Native AI Optimizes That Strategy.

Human Expression. Machine Intelligence. The future is not autonomous merchandising.
Your Merchants Author
Commerce-Native AI Delivers
Brand story and campaigns
Measurable revenue increase
Visual merchandising, promo tiles, badges
Personalization for every shopper
Rules, curation, product relationships
Eliminates substantial manual and rule merchandising
Guardrails and business goals
Continuous learning and improvement

Understand. Optimize. Control. Prove.

Understand

Catalog + Shopper Intelligence
Combines deep product-catalog understanding with shopper behavior and intent to improve relevance.
Unified Commerce Intelligence
Uses a shared shopper and commerce intelligence layer across collections, recommendations, search, and shopping-agent experiences, so signals from one journey immediately improve the others.
Real-Time & Continuous Learning
Learns from every shopper interaction and commerce outcome, continuously adapting ranking and personalization within sessions and across sessions.
New Products & Collections Cold Start
Intelligently introduces and learns from new products and collections without requiring historical shopper interaction data.

Optimize

Multi-Objective Commerce Optimization
Optimizes ranking and discovery toward merchant-defined outcomes including conversion, revenue, AOV, margin, inventory objectives and other business priorities.
Inventory & Promotion Awareness
Incorporates inventory availability and active promotions when ranking, recommending and personalizing products.
Merchandising-Aware Personalization
Personalizes shopper experiences while respecting merchandising strategy, product priorities and business rules.
First-Visit & Anonymous Personalization
Personalizes from the shopper's first interaction without requiring login or prior purchase history, then continuously adapts as intent develops.

Control

AI Strength & Blending
Control how shopper intent, AI ranking, merchandising strategy and business objectives are blended across discovery experiences.
Brand & Business Guardrails
Applies brand, merchandising and business constraints so AI-driven experiences stay aligned with merchant policy.
Personalization Simulation & Preview
Simulates ranking and previews outcomes following shopper interaction and choices.

Prove

Explainability
Explains why products are ranked or recommended, including the influence of shopper intent, relevance, merchandising rules and commerce signals.
Impact Measurement
Measures the incremental impact of Commerce-Native AI on engagement, conversion, AOV, revenue and other business outcomes through controlled testing.

Not a Demo. An A/B Test.

Measuring the impact of Commerce-Native AI
Every Commerce-Native AI engagement starts with a live A/B test on your store, so you see the impact in your own numbers.

A True Head-to-Head Test

Your shoppers are randomly split. Half see your collections as they work today. Half see collections ranked by the Commerce-Native AI model, within the guardrails you set. Both groups get the same traffic and the same measurement. The only difference is Commerce-Native AI.

Clear Rules, Clear Results

We agree on the goal before the test starts, usually revenue per visitor, and run it for at least 14 days. No early calls. When it's done, you see the full results and whether the lift is statistically significant, directional, or not yet conclusive.

Brand Expression Plus Machine Intelligence

Commerce-Native AI works at every shopper touchpoint, and what it learns at one improves the others.

Collections and Category Pages

Merchandise a brand experience, not simply product ranking. Visual Merchandising. Display Merchandising. Dynamic Badging.

Recommendations and Complete the Look

Site-learned matching, merchant guardrails, and an easy way to add the whole look: commerce intelligence applied to product relationships and basket building.

Search and Conversational Search

Natural-language query, AI summary, curated products and follow-up prompt embedded in the storefront. Real multi-turn conversaions.

AI Shopping Agents

Product and collection page sessions inspire shoppers to move from browsing and search into guided assistance.
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What Our Customers Say

FAQs

What is Commerce-Native AI?

Fast Simon's AI model, purpose-built for e-commerce. It combines shopper intent with catalog structure, newness, inventory, availability, price, velocity, promotions, visual storytelling, merchandising policy, and business goals, then personalizes product rankings, recommendations, and search results in real time, based on each shopper's intent and the merchant's goals.

Will it replace our merchandisers or override our rules?

No. Your team sets the strategy, rules, and guardrails, and decides how much AI ranking to blend with them. The model optimizes your strategy without stepping outside it.

How is this different from Fast Simon AI Personalization?

AI Personalization adapts the experience to shopper behavior. Commerce-Native Personalization adds catalog, merchandising, inventory, promotion, and business context.

Does it work for first-time, anonymous visitors?

Yes. It personalizes from the first interaction, with no login or purchase history, and sharpens as intent develops.

How does Fast Simon measure the impact of Commerce-Native AI?

With a live A/B test on your store. Shoppers are randomly split into two groups. One group sees your collections as they work today. The other sees collections ranked by the Commerce-Native AI model, within the guardrails you set. Before the test starts, we agree on the goal, usually revenue per visitor, and a minimum run of 14 days. When it ends, you get the full results and whether the lift is statistically significant (95% confidence or higher), directional (90% to 95%), or inconclusive.

How Do You Inspire a Shopper to Notice a Product They Want to Buy Now?

Your merchants' expertise and Commerce-Native AI, working together.
Your merchants' expertise and Commerce-Native AI, working together.
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