Agentic Commerce

Make commerce data agent-ready.

Path Analytics gives AI agents the operational context they need to answer merchant questions, recommend next best actions, and stay inside clear human approval boundaries.

Make your commerce data agent-ready See the agent interface →

An agent asks Path Analytics why sell-through dropped for footwear in the Southeast; the response retrieves inventory, demand, pricing, and fulfillment context, diagnoses the cause, and proposes a recommended action awaiting human approval. Built for Claude, ChatGPT, Gemini, MCP, and REST.

The Problem

Agents need more than sales history.

An AI agent answering commerce questions needs operational context most retailers cannot expose cleanly. Reading sales history tells you what happened. It does not explain what should have happened given customer engagement, catalog metadata, pricing, inventory position, channel mix, local demand, or comparable locations.

That missing context matters. A useful commerce agent needs expected performance, source freshness, inventory availability, customer-impacting signals, pricing and catalog quality, comparable demand, revenue leakage signals, and the reasoning behind a recommended action.

Integrate Once

One interface for agent access to commerce context.

Without a context layer, each agent integrates separately with POS, ecommerce, ERP, WMS, marketplaces, and warehouse systems. With Path Analytics, agents query one interface.

Agents and retail teams Claude, Gemini, ChatGPT, custom enterprise agents, analysts, and operators

Path Analytics Commerce context, decision intelligence, next best actions

Operational systems POS, ecommerce, inventory, ERP, fulfillment, marketplaces

What Agents Can Query

Operational context exposed in a form agents can use.

Path Analytics Commerce
Intelligence Context in.
Reasoned action out.

01Expected performanceStore, channel, SKU

02Inventory positionAvailability and placement

03Customer engagementBehavior and friction

04Catalog metadataAttributes and content

05Pricing contextMargin and variance

06Revenue leakageConversion and fulfillment

07Comparable demandMarkets and peer locations

08Reasoned actionsOwner, checkpoint, approval

Governance Boundary

Path Analytics detects and recommends. It does not execute.

No writeback to OMS. No automated stock movements. No autonomous pricing, replenishment, fulfillment, or merchandising changes.

Actions require human approval and are auditable. Path Analytics can show what should be reviewed, why it matters, what evidence supports it, and how progress should be tracked after approval.

Model Flexibility

Works with the agent stack you choose.

Path Analytics supports Claude, Gemini, ChatGPT, Microsoft Copilot, Amazon Bedrock agents, Llama-based agents, Mistral, Cohere, DeepSeek, and custom enterprise agents through MCP and REST interfaces.