fabric AI is an innovative business assistant that analyzes real-time data, identifies issues, and offers tailored solutions to provide actionable insights for retailers to streamline operations, optimize workflows, and improve decision-making.

fabric AI’s natural language interface in Copilot allows users to ask their own questions, or select from prompts, such as Which locations could be impacting my margins? or Which products need improved SEO descriptions? to uncover and address potential issues before they escalate.

Transforming Workflows

fabric AI’s interface is accessible directly from Copilot’s homepage. After navigating to modules such as Product Catalog, Orders, or Inventory, fabric AI shifts to the right panel. With fabric AI as an assistant through every phase of operational workflows, users can:

  • Interact with operational data: Access and explore data to gain insights and implement changes directly within the system.

  • Analyze and optimize workflows: Gain actionable insights to improve fulfillment performance, product content, order, and inventory management.

  • Automate repetitive tasks: Reduce manual effort with recommendations and content generation.

  • Respond in real time: Address issues and make informed decisions quickly, using real-time data and insights to minimize disruptions and improve outcomes.

Use Case

A home furnishings retailer uses fabric to manage their product catalog, inventory, and fulfillment operations across its network of stores and distribution centers.

The retailer specializes in high-quality furniture and decor. Their business success depends on optimizing product visibility, maintaining efficient inventory levels, and ensuring seamless order fulfillment to meet customer expectations.

Identifying challenges

During a weekly review, the merchandising team notes lagging SEO performance for several products, affecting site traffic and sales. At the same time, the fulfillment team observes an uptick in canceled orders and delayed deliveries, particularly in one region.

Using fabric AI

The teams begin by using Product Catalog AI to analyze the SEO performance of their products by asking Which products need SEO improvements? Product Catalog AI evaluates their products and identifies a list of under-performing products with issues such as generic descriptions, missing keywords, and inconsistent metadata.

Next, they use Fulfillment AI to investigate the causes behind fulfillment inefficiencies by asking What’s driving fulfillment issues this week? Fulfillment AI pinpoints a cluster of stores in one region contributing to high bounce rates due to inventory shortages and performance inconsistencies.

fabric AI provides tailored recommendations for both areas:

  • Catalog optimization: Product Catalog AI uncovers a popular dining table SKU identified as having poor SEO. It generates an optimized description aligned with the retailer’s branding and provides actionable metadata corrections to improve visibility. These updates are approved and published immediately.

  • Fulfillment rule updates: Fulfillment AI suggests creating a new inventory rule to prioritize high-performing locations and adjusting order orchestration rules to prevent delays. The new rule is applied with a single click, ensuring orders are routed more efficiently.

Unlocking new opportunities

With the immediate challenges addressed, fabric AI proactively identifies additional opportunities for growth. For example, it suggests expanding the product offering by adding matching chairs to the dining table category. It also recommends ongoing monitoring of fulfillment performance metrics to ensure continued optimization and identify future improvements.

It also helps the retailer overcome operational hurdles while unlocking strategic opportunities. By combining insights from Catalog AI and Fulfillment AI, the teams improve product visibility, enhance fulfillment efficiency, and ensure customer satisfaction.

These capabilities enable the retailer to maintain seamless operations, boost revenue, and uncover new growth opportunities—all while saving time and resources through AI-driven automation.

fabric AI Rules

fabric AI uses a rules-based framework to govern operational processes such as inventory allocation, fulfillment logic, and product data optimization. By leveraging AI-driven insights and predefined logic, fabric AI dynamically updates rules to improve efficiency, reduce errors, and enhance performance.

The fabric AI rules framework operates through a structured process:

  • Data Analysis: fabric AI collects and evaluates operational data to identify inefficiencies and opportunities for improvement. Metrics and sub-metrics are tailored to each domain, such as fulfillment performance or product content quality.

  • Rule Evaluation and Suggestion: Existing rules are assessed against operational performance and predefined thresholds. fabric AI provides actionable updates to enhance efficiency and maintain consistency.

  • Implementation: Users can review and apply fabric AI’s suggestions directly in Copilot. Changes are validated for accuracy and implemented in real time, ensuring seamless integration into workflows.