Posts

AI-Powered SEO Copilot — A Sitecore Marketplace App

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Marketplace App AI-Powered SEO Copilot  - Inside the Sitecore Pages Editor How we embedded GPT-4-driven SEO intelligence directly into the content authoring workflow — without breaking decoupled architecture principles. By Team Sitecore Strikers — Santosh Kumar, Shrikant Sahu & Divya Singh SEO is usually the last thing on a content editor's mind — and the first thing a marketing lead complains about after a page goes live. Editors juggle deadlines, approvals, and brand voice; "is this meta description under 160 characters" is rarely top of mind. So we asked a simple question for this year's Sitecore Hackathon: what if SEO best practices showed up right where content is created, instead of after it's published? That question became the AI-Powered SEO Copilot — a Sitecore Marketplace app that lives inside the Pages Editor, analyzes content in real time, and uses Azure OpenAI (GPT-4) to generate optimized meta titles, desc...
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Building an Event-Driven Webhook Pipeline in SitecoreAI - An Architectural Deep Dive

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SitecoreAI Webhooks Azure Functions Azure Service Bus Event-Driven Architecture Every publish event is a signal. Here's how to build the infrastructure that actually listens. Here's a scenario most Sitecore teams have lived through. A content editor publishes an article, then walks over to a developer and says "the page hasn't updated yet." The developer checks the CDN — cached. Purges it manually. Five minutes later, another editor. Same story. The root cause isn't the CDN. It's that nothing in the system is listening for the publish event and reacting to it in real time. Content goes from the CM to Experience Edge, and then silence. Downstream systems — the Next.js frontend, the Sitecore Search index, the Content Hub asset record — have no idea something changed. SitecoreAI (the platform formerly known as XM Cloud, now part of the broader Sitecore AI product family) ships wit...
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From Data to Decisions: AI-Powered Reporting in Sitecore Content Hub

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Introduction In our Sitecore Content Hub implementation, the initial focus was on content onboarding, workflows, and integrations. However, once the platform became operational, stakeholders began asking: How many assets are being uploaded over time? Where are workflow delays occurring? Which teams are actively contributing? 👉 The data existed—but deriving insights required manual effort. The Problem Manual data exports from Content Hub Processing in spreadsheets Static reporting This approach was: Time-consuming Error-prone Not scalable Objective Automate data extraction Structure data for reporting Enable dashboards Reduce manual effort Architecture Overview Below is the architecture we implemented: End-to-End Flow 1. Data Source M.Asset → Assets M.Content / M.State → Workflow Audit Logs → User activity ...
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From Prompt to Production: Agentic Sitecore Workflows with MCP

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Two practical paths: Option A (Marketer MCP, no code) and Option B (Community MCP server, extensible). We’ll go from a natural‑language prompt to a published landing page. Previously on the blog This article continues my earlier post, MCP‑Powered AI Development Workflow in Sitecore XM Cloud — a quick conceptual primer. Why MCP (in one paragraph) MCP → Safe, typed tools Model Context Protocol (MCP) turns natural language into governed, typed tool calls. In Sitecore, Marketer MCP exposes tools like create page , add component , and upload asset that map to the Agent API so agents can take real actions with guardrails and auditing. The agentic workflow: how it works When you chat with Claude (which is already connected to your Marketer MCP), Claude acts as an AI agent — it calls the Sitecore APIs on your behalf, fetches IDs, makes decisions, and creates items without you needing to open Sitecore at all. Architecture Overview Prer...
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