AI Cuts Newsletter Production Time from 3.5 Hours to 40 Minutes | MBlock Company
- SmileShark Team
- Nov 11
- 8 min read
AI doesn't replace editors — it makes them more efficient.
- Seong-ah Jeon, Manager at Mblock Company
[ 💡 Summary ]
1. Mblock Company cut newsletter editing time by 81% - reducing a 3.5-hour workflow to just 40 minutes through full AI-driven automation.
2. Google Spreadsheet-based intuitive automation - by integrating the Naver News API, Google APIs, and the Amazon Bedrock, the system automatically collects, analyzes, and ranks about 100 news articles each day, adding two related articles per item to enrich the content
3. SmileShark, the AI innovation partner behind Mblock's transformation - provided technical expertise for multi-API integration and workflow design, enabling Mblock's media operations to achieve digital transformation through rapid communication and professional execution.
Company Overview
N a m e Mblock Company Co.,Ltd.
A r e a s Blockchain Data Validation・NFT・Media・Conferences
Founded April 2022
Website https://m-block.io/
Mblock Company, founded in 2022 by the Maeil Business Newspaper Group — one of South Korea's leading media organizations — is a blockchain-focused subsidiary. As a blockchain and digital-asset media outlet, it publishes Mblock Letter, a newsletter distributed every Wednesday and Friday to approximately 10,000 subscribers.
Since introducing its AI-powered newsletter automation system in 2025, Mblock Company has reduced editing time by 81%, achieving measurable efficiency gains.
As Jeon Seong-ah describes it, "With just one click, the article appears in three seconds." That moment captured how automation directly translated into tangible productivity improvements.
Inside the 24/7 Digital0Asset Market — A Day in Life of an Editor
Every morning at 9 a.m., Mblock Company's manager Seong-ah Jeon begins her day reviewing news from the overnight digital-asset market.
In this 24-hour industry, information shifts rapidly; what's trending in the morning can be outdated by the time the newsletter draft is ready. During major events — such as the market volatility surrounding Donald Trump's election — the domestic and global digital-asset landscape would change hour by hour as regulatory discussions intensified.
Within Mblock's newsroom, a "daily news-clipping" culture exists: five key articles are summarized and shared with links each morning. However, beyond articles, faster updates flow through X (formerly Twitter), Telegram, and Discord, forcing the team to spend over 10 hours a week just tracking market movements.
"Honestly, it fels like I was wandering around like a hyena looking for newsletter topics."
Her remark captures the reality faced by editors in the fast-moving digital-aasset market — constant monitoring, manual curation, and unrelenting time pressure
AI Adoption — But New Challenges Ahead
While Mblock Letter was oamong the first in its field to experiment with AI, existing tools such as ChatGPT, Gemini, and Perplexity quickly exposed critical limitations.
The biggest issue aws accurate article retrieval. When asked to"find five Korean digital-asset news articles publiched between October 7 and October 14, 2025," only two or three results were actually valid. Many links led to unreliable sources, promotional blog posts, or outdated contect—sometimes from the previous year—demonstrating how hard it was to achieve precision.
Tone and consistency were also problematic. Even when prompted to use a formal "-입니다" style, the AI would randomly shift to conversational tone halfway through. Hallucinated links were another recurring problem. When an AI-generated newsletter needed post-publication edits, locating the original prompts or data sources became extremely time-consuming due to the conversational interface.
The result? More time spent entering prompts, adjusting parameters, and waiting for responses. As Jeon put it, "At some point, I couldn't tell whether I was training the AI or working as its assistant." It became clear that Mblock needed a structured, system-based approach aligned with its editorial standards—beyond one-off chat-based tools.
The Decision to Automate
Q. We heard you initially tried building automation yourself.
Yes. After seeing similar examples shared in a marketing community, I tried building one on my own for two weeks using the ChatGPT API and Google Sheets. But due to my lack of development background, I kept running into error. Eventually, I realized this was beyond what I could handle alone.
Q. What made you decide to pursue full automation?
After that failed attempt, I was ready to give up and just wait for aI technology to mature. But then, through our AWS Partner Manager, I was introduced to SmileShark, a team that could provide both infrastructure and technical support. That's when the real development started.
Building the System with SmileShark
When Jeon shared her initial project brief with SmileShark—including all the pain points from her earlier attempts—she received precise feedback. The SmileShark team explained which parts were technically feasible, which were not, and why, allowing Mblock to refine the entire plan.
They began by testing various integration methods. The first prototype used Slack as the vase, but after comparing multiple environments, Google Sheets proved most effective for archiving and collaboration. Whenever an experiment failed, SmileShark immediately proposed alternatives, enabling the project to move forward without delay.
One of the key breakthroughs was solving the filtering problem that Jeon couldn't overcome alone. For example, searching for "coin" used to return irrelevant results such as "K-pop idols appearing at the Coin Festival."
SmileShark helped formalize Mblock's filtering standards into a News Clipping Criteria Table—categorizing sources by media credibility, relevance to digital assets, and timeliness. This system allowed the AI to automatically assign scores and rank trustworthy sources across both traditional and emerging media outlets.
The Core Principle: ;From One Click to Full Draft'
At the heart of the completed system lies simplicity — "from one click to the full article"
During the news-clipping process, editorial conditions defined by Mblock are automatically applied: publication recency, media credibility, and whether the source is an official Naver News content provider. Each article receives a weighted score on a 100-point scale, and about 100 articles per day are automatically collected and ranked by score.
The system then enriches each main article with two related articles to generate summaries and links automatically, reducing duplication and copyright risk.
Editors simply review the list, check the boxes for the articles they approve, and the system instantly generates the body text in three styles — newsroom, conversational, and formal.
"Now all I do is review and poliush the content", Jeon said. "What used to take hours is now literally one click.
After Implementation — What Changed
Q. What was the most memorable moment?
When I clicked the checkbox for the first time and saw the article appear on screen about three seconds later, that was the moment I realized — I'll never have to wrestle with ChatGPT again.
Traditional AI tools like ChatGOT or Claude could generate one article at a time, each requiring long waiting periods. In contrast, the new system processes multiple selections at once.
"All I do now is decide which pieces to include," she added. "The system does the rest."
Q. What did you learn form the process?
The biggest lesson was that there's no such thing as a perfect first attempt. We started with a Slack-based version, switched to Google Sheets, and adjusted our filtering criteria several times. Every failure led us closer to a better system.
Another thing I noticed during live operations, was that AI-selected articles often got slightly higher engagement than those picked by our editors. That really showed how far AI has come in understanding what readers care about.
From 3.5 Hours to 40 Minutes — An 81% Reduction
The numbers tell the story best. Before automation, each Mblock Letter issue took about 3.5 hours to produce. Immediately after implementation, that dropped to 1.5 hours, and now — including image preparation and platform upload — the entire process takes just 40 minutes.
That's an 81% reduction in production time.
But the most signmificant improvement wasn't just time saved — it was the psychological relief that came with it.
Before automation, Jeon handled the daily morning news clipping manually. When workloads piled up, she sometimes missed deadlines — not because of external pressure, but from the sense of personal responsibility.
Now with automated clipping, I can wrap up that task in under 10 minutes. It gives me more time and evergy to focus on higher-value work. That's the biggest change — not just efficiency, but mental clarity.
Practical Tips for AI Newsletter Automation
"AI doesn't replace editors — it makes them more efficient."
Jeon offered several actionable tips for other editors and media teams exploring automation:
Define your internal standards clearly. Mblock built a 100-point evaluation model based on media credibility, publication recency, and whether the outlet is a Naver News content provider. Converting subjective quality into measurable metrics was key to system reliability.
Keep your tools simple. After testing multiple approaches, Google Sheets proved to be the best for record-keeping and collaboration. "Simplicity beats complexity every time," Jeon noted.
Design for a one-click experience. Automation should feel effrotless — a single checkbox should trigger a full draft. "The less friction editors feel, the more likely they';; embrace the system."
Work with a specialized partner. Multi-API integration and workflow design require technical depth. Collaborating with an experienced partner like SmileShark was crucial to the project's success.
Create a separate development account. Using dedicated development credentials simplifies collaboration and enhances security.
Dont aim for perfection — start first. We're still improving the system every week, but the key is to start. In an industry changing this fast, hesitation costs more than imperfection.
Scalability Beyond Mblock — Expanding the Newsletter Model
The automation framework Mblock built can easily be applied to other newsletters with only minor keyword adjustments. Jeon believes that industries such as finance and economics, which rely heavily on news clipping and summarization, could adopt a similar model to achieve the same level of efficiency.
She emphasized that the system Mblock created can be scaled across multiple domains. If companies can clearly define their selection logic — such as how to score or filter reliable news sources — the rest of the process can be fully automated.
The system's modular structure makes it flexible enough to handle new datasets, APIs, or workflows, providing a foundation for future growth across different media verticals.
More Than a Technical Partner — A True AI Innovation Ally
Q. What stood out the most about working with SmileShark?
The communication speed. In the past, working with freelance developers often meant waiting weeks between feature requests and deliverables.
But with SmileShark, communication was incredibly fast. I still remember — our assigned Solution Architect was coordinating with us the same day he came back from military reserve training.
What also impressed me was how SmileShark handled limitations. When something wasn't technically feasible, they didn't just say no — they clearly explained why and offered alternativeness. That made the entire collaboration process stress-free. Honestly, this project wouldn't have been possible without our SA, Byung-joo. He truly played a major role in making our workflow automation a success.
Q. How would you recommend SmileShark to other companies?
Many startups don't have in-house developers. But if you know what you want and can communicate your goals clearly, SmileShark can turn that vision into an actual tool.
When our AWS Partner Manager asked how the collaboration went, we told them we'd absolutely recommend SmileShark — especially Manager Jun-hong, who was amazing to work with, and the SA team for their responsiveness and expertise. They're a partner we truly trust and recommend to others.
Empowering Media Companies to Focus on What Matters
SmileShark helps media organizations like Mblock Company technical barriers and stay focused on innovation.
