**Newslaundry’s AI Chatbot Evolves Beyond Query Responses to Embrace Content Writing** This title captures the core message of the article while highlighting how the chatbot’s capabilities extend beyond simple query handling, positioning it as a content

news organization demonstrates how Artificial Intelligence can boost team productivity adn enhance user experience, moving beyond AI hype to deliver practical solutions.">

september 22, 2025

A leading, fully-subscriber-funded news organization has seen significant improvements in team efficiency and user satisfaction through the implementation of an internally developed Artificial Intelligence assistant. The tool, named Ask Birubala, is designed to handle routine inquiries and documentation, freeing up staff to focus on higher-level tasks.

From Troubleshooting to Data Analysis: The Impact of AI Automation

The implementation of Ask Birubala has shifted the focus of the support team from repetitive problem-solving to more analytical roles. The team now dedicates more time to data analysis and campaign management. This strategic shift illustrates a growing trend among media companies to leverage artificial Intelligence not as a replacement for human expertise, but as a tool to amplify it.

A Reader-First Philosophy Drives product Advancement

The organization’s commitment to its subscription model has deeply influenced its product development strategy. Unlike many conventional news outlets preoccupied with click-through rates and impressions, this outlet prioritizes user engagement, loyalty, and the amount of time subscribers spend consuming content. This reader-centric approach extends to prioritizing privacy and ease of use within its app.

Mobile Optimization: Meeting Readers Where They Are

Recognizing that 95% of its readership accesses content via mobile devices, the organization has prioritized a smooth and efficient user experience on smartphones. This contrasts with newsrooms that often invest heavily in visually appealing but cumbersome desktop designs. Their custom podcast player, created after moving podcasts behind a paywall, is a prime example of addressing a specific user pain point with a focused solution.

Ask Birubala: A Pragmatic Application of Retrieval-Augmented Generation

Ask Birubala represents a departure from the often-hyped applications of AI in newsrooms. It wasn’t conceived as an experimental feature, but as a solution to concrete operational challenges. Utilizing Retrieval-Augmented Generation (RAG) technology, the chatbot serves as a troubleshooting guide, a knowledge base for new employees, and a shortcut to essential documentation.

The system is trained on ancient support communications, frequently asked questions, and internal technical documentation, becoming a centralized repository of details regarding subscription workflows and technical processes. Users can submit a subscriber’s email, and the system generates a tailored response, detailing the problem and outlining the necessary steps.

Technical Implementation

The tool was built using a modern technology stack, including Next.js, React, Node.js, Langchain, OpenAI, and Pinecone. Internal documentation is generated using Scribehow, which transforms screen recordings into easily shareable PDFs.These documents are then segmented, vectorized, and stored in Pinecone, a vector database, with metadata for efficient retrieval.

Component	Technology	Function
Frontend	Next.js, React	User Interface and Interaction
Backend	Node.js	Server-Side logic
AI Engine	Langchain, OpenAI	Natural Language Processing and Response Generation
Vector Database	pinecone	Storage and Retrieval of Document Chunks
Documentation	Scribehow	Creation of PDF Documentation

Developing Ask Birubala wasn’t without its hurdles. The team faced challenges related to a lack of thorough documentation and the need to rapidly acquire expertise in AI concepts. Ongoing iterations have included improvements to metadata classification, in-interface editing capabilities, and a user feedback mechanism for assessing response quality.

Quantifiable Results: Over 10 Hours Saved weekly

The implementation of Ask Birubala has yielded measurable results, saving senior developers and product managers over 10 hours per week by automating responses to routine queries. Support staff, now unburdened by basic troubleshooting, have been able to shift their focus to tasks that add greater long-term value. “Did You Know?” AI-powered customer service is projected to reach a $17.17 billion market value by 2027, according to recent reports from Grand View Research. This marks a substantial increase from $6.83 billion in 2020.

“Pro Tip:” When considering AI implementation, focus on solving existing problems rather than chasing the latest technological trends.

The Future of AI in News and Media

The success of Ask Birubala highlights a broader shift in the media industry toward practical applications of Artificial Intelligence. As AI technology matures, we can expect to see even more innovative implementations that enhance the user experience, improve operational efficiency, and ultimately strengthen the relationship between news organizations and their audiences. Effective integration relies on a deep understanding of the specific challenges within a newsroom and a commitment to building solutions that genuinely meet user needs.

Frequently Asked Questions about AI in Newsrooms

• What is Retrieval-Augmented Generation (RAG)? RAG is an AI technique that combines pre-trained language models with information retrieved from an external knowledge source, allowing for more accurate and contextually relevant responses.
• How can Artificial Intelligence improve user engagement? AI can personalize content recommendations, optimize reading experiences, and provide instant support, all of which contribute to increased user engagement.
• Is AI a threat to jobs in the news industry? While AI may automate certain tasks, it primarily serves to augment human capabilities, allowing journalists and othre media professionals to focus on more complex and creative work.
• What are some of the challenges of implementing AI in a newsroom? Challenges include the need for robust data infrastructure, specialized expertise, and careful consideration of ethical implications.
• How does AI help with subscription-based models? AI can definitely help understand subscriber behavior, personalize offers, and improve customer support, leading to increased retention and revenue.
• What are the benefits of a mobile-first approach to news products? A mobile-first approach ensures accessibility and a smooth user experience for the vast majority of readers who access content on their smartphones.
• How can news organizations build trust with their audience? Prioritizing user privacy,transparency,and providing high-quality content are key to building trust with readers.

What are your thoughts on the role of AI in the future of news? Share your comments below!

How does Newslaundry’s human-in-the-loop approach ensure journalistic integrity when utilizing AI-generated content?

newslaundry’s AI Chatbot Evolves Beyond Query Responses to Embrace Content Writing

Newslaundry, a leading Indian independent media institution, has significantly upgraded its AI chatbot capabilities.Initially designed for answering user queries and providing support, the chatbot now demonstrably functions as a content writer, generating articles, scripts, and social media copy. This evolution marks a pivotal shift in how news organizations leverage artificial intelligence, moving beyond automation of simple tasks to active content creation. This article explores the specifics of this advancement, its implications for the media landscape, and potential future developments.

From FAQ to Feature Article: The Chatbot’s Journey

The change wasn’t overnight. Newslaundry’s initial foray into AI chatbots focused on improving customer service and providing swift answers to frequently asked questions about subscriptions, content access, and editorial policies. Though, recognizing the potential of large language models (LLMs), the team began experimenting with more complex prompts.

* Phase 1 (early 2024): Basic query resolution – handling subscription issues, providing links to articles.

* Phase 2 (Mid 2024): Summarization of articles – providing concise summaries of long-form content.

* Phase 3 (Late 2024 – Present): Content generation – drafting articles, social media posts, and even initial scripts for video explainers.

This progression highlights a deliberate strategy of incremental betterment, building upon existing functionality to unlock new capabilities. The current iteration utilizes a proprietary blend of LLMs, fine-tuned on Newslaundry’s extensive archive of journalistic content. This ensures the AI’s output aligns with the organization’s established voice and editorial standards.

How Newslaundry’s AI Content writer Works

The core of the system relies on sophisticated prompt engineering.Unlike simple chatbots that respond to keywords, newslaundry’s AI requires detailed briefs outlining the desired article’s:

* Topic: Specific subject matter.

* Target Audience: Who the article is intended for.

* Tone: Formal, informal, analytical, etc.

* Keywords: Relevant search terms for SEO. (e.g., Indian politics, media bias, digital journalism)

* Length: Approximate word count.

* Sources: Suggested sources for fact-checking and context.

The AI then generates a draft, which is later reviewed and edited by human journalists. This human-in-the-loop approach is crucial for maintaining journalistic integrity and ensuring accuracy. The AI doesn’t replace journalists; it augments their capabilities.

Content Types Now Generated by the AI

The range of content the AI can now produce is surprisingly broad:

* News Summaries: Concise overviews of breaking news events.

* Explainers: In-depth analyses of complex topics. (e.g., The implications of the Digital India Act)

* Social Media Copy: Engaging posts for platforms like Twitter, Facebook, and Instagram.

* Script Outlines: Initial drafts for video explainers and podcasts.

* Short-Form Articles: News briefs and quick takes on current events.

* SEO-Optimized Blog Posts: Content designed to attract organic traffic. (Utilizing keywords like independent media, fact-checking, news analysis)

Benefits of AI-Assisted Content Creation for Newslaundry

Implementing AI-driven content creation offers several key advantages:

* Increased Output: The AI allows Newslaundry to publish more content with the same resources.

* Faster Turnaround Times: Articles can be drafted and published more quickly, especially for breaking news.

* Cost Efficiency: Automating certain content creation tasks reduces labor costs.

* SEO Performance: AI can assist in identifying and incorporating relevant keywords, improving search engine rankings.

* Exploration of Niche Topics: The AI can efficiently cover a wider range of topics, including those that might not be feasible with a purely human-driven approach.

Real-World Examples & Case Studies

While Newslaundry is understandably cautious about revealing the full extent of its AI integration, several examples demonstrate its impact. During the recent state elections,the AI was used to generate real-time updates on vote counts and analyze early trends. These updates were published on Newslaundry’s social media channels and website, providing readers with timely information.

Moreover, the AI has been instrumental in creating a series of explainers on complex policy issues, such as the new broadcasting regulations. These explainers have received significant engagement and positive feedback from readers. A recent internal analysis showed a 20% increase in article output since the full implementation of the AI content writing tools.

Challenges and Considerations

Despite the benefits, integrating AI into content creation isn’t without its challenges:

* Maintaining Accuracy: AI-generated content requires rigorous fact-