Most retail teams already have their most critical systems in place. The challenge is getting the latest, most granular business information into the conversation when a decision needs to be made.

That’s where Model Context Protocol (MCP) comes in. MCP is an open standard that enables AI applications to connect with external data and tools.

The Remi MCP server brings Remi, invent.ai’s retail merchandising intelligence AI agent, into AI environments like Claude, giving teams access to the retail context they need to answer questions and make decisions.

Instead of starting with a dashboard and searching for the right information, teams can start with a business question, bring the relevant retail signals into view and continue exploring without constantly switching between systems.

And the opportunity goes beyond Remi alone. MCP can allow multiple tools to contribute to the same conversation, bringing together the information teams already rely on to understand a business problem and decide what to do next.

Connecting AI to retail systems

An AI model can generate an answer based on the information available to it. But enterprise decisions require more than a model’s existing knowledge. They require access to up-to-date, business-specific and granular information.

MCP provides the connection. Rather than building a separate integration for every AI application and every external system, MCP creates a standardized way for AI applications to interact with external tools and data sources.

For retailers, that means an AI application can connect to the systems that contain the information behind everyday decisions, from inventory and sales to product and operational data.

MCP doesn’t replace systems. It creates a way for AI to work with them.

Where Remi fits in

Remi brings the retail context needed to make that information useful for decision-making.

Through the Remi MCP server, AI environments such as Claude can connect with the retail intelligence and capabilities available through Remi.

That means users don't need to know which dashboard contains the answer before they start. They can begin with the business question.

For example:

• A supplier’s lead time just doubled. Which store-SKUs are at risk first, and what should I do?
• Marketing is planning to promote this SKU across 300 stores next month. Will existing inventory cover the uplift, or will we stock out?
• My DC only has 90% of the inventory stores are requesting for our top item. How should I allocate it to minimize lost sales?

These questions require more than a single data point. They require forecast, inventory, supply and location-level signals to be considered together.

That is where retail-specific intelligence matters. Remi can bring those signals together, helping users understand what is happening and what to consider doing next.

From a question to a decision

A retail question rarely exists in isolation. A stockout question might require looking at what is on hand, what is selling and what is inbound. A promotion question could require understanding forecasted demand, inventory and the expected uplift. A transfer decision could depend on inventory positions across multiple locations.

The Remi MCP server makes the relevant retail intelligence available within the AI conversation.

A merchandising leader could start by asking, “Which products are likely to stock out in the next seven days?”

Then continue:

• Which stores are most exposed?
• Do we have inventory elsewhere that could cover the shortfall?
• What should we do first?

Each question builds on the last. Instead of opening another report or moving into another system, the conversation stays focused on the business problem.

And when the question changes, the conversation can change with it. A leader could ask, “How did our outerwear category perform last month?” and bring the relevant sales and inventory context into view.

Bringing more business context into the conversation

The value of MCP isn’t just connecting one AI application to one source of information. Multiple tools can contribute to the same conversation.

For example, a retailer could use Remi to identify products at risk of stocking out, then bring in information from a CRM or BI platform to understand the customer, account or margin context.

Instead of exporting information, reconciling it and switching between screens, each tool can contribute the context it is best positioned to provide.

That creates a more connected path from question to context to decision.

Security and data governance

For enterprise retailers, connecting AI to business systems also raises an important question: how is access to business data controlled?

The Remi MCP server provides a controlled and secure connection between authorized AI environments and Remi’s retail intelligence, so retailers can bring AI into their existing workflows while maintaining the access and governance controls around their business information. Tenant isolation is enforced at every layer, keeping your private retail data and AI connections completely separated from other organizations.

The goal isn’t to make retail data broadly available to AI applications. It’s to make the right retail intelligence available in the right context, within the retailer’s existing security and governance requirements.

Bring retail intelligence into decision-making

Retail teams already spend enough time moving between systems, reconciling information and preparing data before a decision can even begin.

The Remi MCP server brings Remi’s retail intelligence into the AI environments teams already use, so they can start with a business question, bring the right context into the conversation and move toward a decision.

The result is a simpler way to start with the business question, bring the right context into the conversation and move toward a decision.

Ready to bring retail intelligence into your AI workflow? Talk to invent.ai.