;Resize,width=660)
Turn credit evaluation into a collaborative process powered by AI agents—automating analysis, integrating internal and external data, and consolidating insights across corporate credit underwriting.
AI CMS Editor
Create content, optimize SEO, and manage multi-market rollouts in Sitecore through agentic AI workflows and visual editing.
#EnterpriseAI #ContentAutomation #DigitalExperience #AgenticAI #Marketing
Business Challenge
Enterprise content teams are expected to produce more content, faster, across multiple channels, markets, and languages — while maintaining brand consistency, SEO performance, accessibility compliance, and editorial quality. Yet traditional CMS workflows remain fragmented and manual. Briefing, page creation, asset search, copywriting, localization, review, approval, and publishing often happen across disconnected tools and teams. This slows production, increases rework, and makes global rollouts difficult to control.
Teams need a faster, more governed way to create, adapt, review, and publish content at scale.
Solution Overview
AI CMS Editor extends Sitecore with an agentic AI visual editor that brings content creation, collaboration, localization, and workflow automation into a single editorial workspace. Editors can create and modify pages in a full-screen WYSIWYG environment, preview changes in real time, collaborate with stakeholders, and use AI to search, generate, optimize, and adapt content without leaving the CMS.
At the core of the solution, configurable AI agents support multi-step workflows for page generation, briefing creation, asset sourcing and tagging, SEO/GEO checks, accessibility validation, social content creation, localization, and rollout execution — based on predefined templates and governance rules.
The solution’s main capabilities include:
AI-Assisted Content Creation & Visual Editing
Create, update, and preview pages through a WYSIWYG editor, while AI agents support page generation, layout suggestions, copy creation, image sourcing, and bulk content updates.Agent-Supported Collaboration & Governance
Review, comment, suggest changes, and approve content directly in context. AI agents propose content improvements, surface quality issues, and generate publishing suggestions that remain inactive until explicitly approved.Localization & Multi-Market Rollout
Generate market-ready page variants, adapt tone and cultural context, trigger bulk localization, and track rollout progress across countries, languages, and content streams.
Through the Agent Builder, teams can configure custom agents by combining predefined skills, tools, and functions, or activate ready-to-use agents already provided with the solution — including a Create Agent for pages, components, layouts, text, and images; a Content Finder Agent for asset discovery, analysis, tagging, recommendation, and new asset creation; a Briefing Creator Agent for structured content and campaign briefs; and a Campaign Creator Agent to generate new campaigns from existing campaigns or provided briefings. Additional predefined agents support SEO/GEO checks, social media content creation, and accessibility validation.
A visual Agent Taskboard provides visibility into how agents create, organize, and execute tasks, while editors and stakeholders retain control at key checkpoints through review, approval, and human-in-the-loop validation. The solution also supports flexible AI model connectivity, including external or privately hosted models, with usage monitoring and configurable cost limits to keep automation transparent, governed, and controlled.
Technical Implementation
The solution integrates directly with Sitecore content, assets, page components, workflows, and publishing processes, allowing AI-assisted actions to operate within the editor’s standard workspace and governance model.
A central orchestration layer manages agent execution, task sequencing, context sharing, and workflow state. Each agent is designed around a specific role or task — such as content generation, asset retrieval, SEO validation, accessibility checks, or localization — and uses approved skills, tools, prompts, and business rules to complete its actions in a controlled way.
Agents can be triggered by user actions, predefined templates, or rollout workflows. They can work independently on specific tasks or collaborate as part of multi-step processes, passing context and outputs from one step to the next. Human-in-the-loop checkpoints ensure that sensitive changes are reviewed before being applied or published.
The architecture is model-agnostic and supports external, enterprise-hosted, or privately deployed LLMs. Usage monitoring, cost limits, auditability, and role-based controls help ensure that AI automation remains transparent, secure, and aligned with enterprise governance requirements.
;Resize,width=660)
;Resize,width=660)
;Resize,width=660)
;Resize,width=660)
;Resize,width=660)
;Resize,width=660)
;Resize,width=660)