AI-First Interface Strategy

Exposing the merchandising platform as an intelligent backend — an MCP + skills layer — so enterprise AI assistants can drive merchandising decisions in natural language instead of clicking through screens.

Problem

Powerful platforms still bottleneck on the UI. Every workflow means training users on screens, and the value is gated behind knowing where to click.

Approach

Treat the platform as a set of well-described tools (MCP servers + a skills layer) that an AI assistant can call. The interface becomes conversational; the platform’s algorithms stay the source of truth.

Outcome

Clients can ask for merchandising decisions in plain language, and the AI orchestrates the underlying platform — turning a deep feature set into something you can just talk to.

Design principles

• The tools carry the semantics. An MCP tool description is now product surface. Writing what a tool is for, when to use it and what it returns is the new UX design.
• Skills encode the workflows. Tools are verbs; skills are the sentences: the encoded judgment of how an experienced merchandiser sequences a decision.
• The platform stays authoritative. The assistant never invents a number. It routes intent to algorithms that were trusted before AI arrived.

A merchandiser can ask, “rebalance next month’s OTB for the stores that under-sold this range”, and the assistant calls the right tools in the right order, with the platform’s own optimization doing the heavy lifting.

Why it matters

It decouples the platform’s value from its screens. Clients can bring their own assistant, and years of merchandising algorithms become something you can just talk to.