AI-Powered Editorial Toolkit for Enhanced Publishing Workflows

A well-established news and publication company with millions of unique monthly website visitors and a workforce of 100-500 employees across various divisions.

Develop an AI-powered Editorial Toolkit to streamline editorial processes, expedite tasks, and leverage advanced analytics for user insights.

Article Editing: Assist editors in refining and polishing articles.

Headline Suggestions: Generate engaging and SEO-friendly headlines.

Keyword Extraction: Identify and highlight relevant keywords for better content optimization.

Automatic Article Generation: Create initial drafts or article templates based on given topics or keywords.

Summarization: Provide concise summaries of articles for quick understanding.

Customization Options: Allow users to tailor the toolkit's features to their specific needs.

NLP: GPT3.5, GPT4, Azure OpenAI, Huggingface, SentenceTransformers, GPT Embeddings, Azure Synapse, Azure ML Studio, Azure Cognitive Search.

Backend: Azure Synapse, Azure Functions, PowerApps, Python, Docker, Azure VMs.

Frameworks: Langchain, Azure ML Studio, FAISS Vector Index.

Data Availability:

The project leverages a comprehensive dataset of historical articles and relevant content, along with pipelines for user metrics, site analysis, SEO rankings, and other analytical data.

Deployed and iteratively refined LLM pipelines in Azure based on internal testing.

Fine-tuned modules using internal and custom datasets for enhanced performance.

Integrated the toolkit into Microsoft Teams for seamless usage by the editorial team.

Editorial Toolkit: In active use internally, undergoing subsequent development.

Recommendation Engines: Pilot phase, serving as a proof of concept.

QA Engines: Proof of concept stage, undergoing exploration.