Visual Merchandising Tool

Part of the worlds-biggest-software-project initiative.

An AI-native, open-source visual merchandising platform for AI-driven category page layout optimization.

The Visual Merchandising Tool helps ecommerce teams arrange and rank products on category and collection pages using AI ranking, rules-based overrides, and a no-code visual editor. It targets merchandising managers, category managers, and digital experience teams at mid-to-large retailers who today face a forced choice between expensive enterprise platforms and feature-thin point tools.

Why Visual Merchandising Tool?

Enterprise incumbents such as Bloomreach ($4,000–$36,000/month) and Adobe Commerce charge enterprise prices and require professional services to implement, putting them out of reach for many mid-market retailers.
Affordable tools (Tagalys from $149/month, Searchspring from $599/month) are mid-market accessible but offer limited ML sophistication, advanced segmentation, or scale.
Most incumbents are platform-locked: Depict.ai is Shopify-only, Tagalys is Shopify Plus exclusive, and several tools focus on search rather than category layout.
Existing tools rely on global rules or simple AI ranking; reinforcement learning that balances revenue, margin, inventory clearance, and newness in real time is not available.
Merchandiser-facing explainability is missing across the market — tools do not surface why a product was ranked at a given position, leaving black-box decisions.

Key Features

Rules-Based Merchandising and Visual Editing

Boost, bury, pin, and hide actions on category and collection pages with a no-code editor
Drag-and-drop visual layout editor for product grid arrangement
Rules combining any product attributes, with optional manual override of AI ranking
Mobile and desktop layout preview during editing
Mobile-responsive output meeting Core Web Vitals (LCP, CLS, INP)

AI-Powered Ranking and Personalization

Behavioral-signal ranking using scroll depth, hover time, and click patterns as the base layer before merchant rules apply
Segment-based merchandising rules so different audiences see different strategies
1:1 personalized category ordering rather than only global rules
Real-time reranking informed by inventory, margin, and trend signals
Per-product, per-placement performance scoring

Testing, Campaigns, and Analytics

A/B testing of merchandising strategies with statistical confidence reporting
Multivariate testing of three or more variants simultaneously with rapid feedback
Automatic A/B winner selection and routing to reduce ongoing management
Campaign scheduling and set-and-forget rule activation
Analytics for views, CTR, conversions, and AOV per product and per placement

Catalog Quality and Explainability

Automated detection of poor-quality images, incomplete attributes, and stale inventory
Dynamic hero image and colorway selection per segment
Natural-language merchandising briefs that explain why the AI ranked a page a particular way
Reinforcement learning to balance revenue, margin, inventory clearance, and newness without manual rule conflicts

Platform Integrations

Native integrations targeting Shopify and Magento for MVP coverage of mid-market ecommerce
BigCommerce integration as a follow-on
API access for programmatic rule creation and management

AI-Native Advantage

Unlike incumbents that bolt AI onto rule engines, this project treats AI as the default ranking layer. Reinforcement learning continuously balances competing objectives (revenue, margin, inventory clearance, newness) in real time. Behavioral signals drive per-visitor grid ordering rather than global rules. AI catalog hygiene flags low-quality images and incomplete attributes that degrade category pages, and natural-language explanations turn ranking decisions from black boxes into actionable merchandising insight.

Tech Stack & Deployment

The project targets a self-hostable, API-first architecture with no-code visual editors for merchandisers and SDK / API access for engineering teams. Initial integrations target Shopify and Magento (covering roughly 70% of mid-market ecommerce), with BigCommerce planned. Layouts are designed to meet W3C Core Web Vitals (LCP, CLS, INP) and WCAG 2.2 accessibility guidelines, and to consume Schema.org Product markup and Google Merchant Center product feeds for attribute scoring.

Market Context

The broader AI-in-ecommerce market is projected to grow from roughly $6 billion in 2024 to $40–$96 billion by 2030, with the overlapping search and discovery software category valued at around $2 billion in 2025 (research.md). Incumbent pricing ranges from $149–$599/month for lightweight tools (Tagalys, Searchspring), $449–$1,000/month for mid-tier (Klevu), to $50,000–$430,000+ annually for enterprise platforms such as Bloomreach and Constructor.io. Primary buyers are ecommerce merchandising and category managers at mid-to-large retailers and digital experience directors at fashion, home, and beauty brands.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Candidate metadata: Category — E-Commerce & Retail. Complexity 6/10. Domain availability: Low. Demand: Medium.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.