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Content at Scale Deep Research Explained & Reviewed

Content at Scale just released a new feature called Deep Research. Here’s everything you need to know about it, including my thoughts after using the platform.
Updated April 17, 2024
A robot holding a “Writer For Hire” sign, generated with Midjourney
A robot holding a “Writer For Hire” sign, generated with Midjourney

Since generative AI was introduced, bloggers rushed to implement its capabilities to their websites. And who could blame them? The ability to create well-written content in just a few seconds — who wouldn’t want that?

But the issue with tools like ChatGPT and Claude is that they don’t have a knowledge base with real-time data. Not only that but they also lack the personalization skills of dedicated SEO AI writers. 

Fortunately, we have Content at Scale for that. And now, with their latest update, they look to be better than ever. In this article, I’ll be walking you through and reviewing Content at Scale’s Deep Research update.

What is Content at Scale?

Content at Scale, as the name suggests, is an AI-powered content generation platform that helps bloggers, businesses, and creators produce large volumes of high-quality content at once. Some of its key features include:

  • AI-Powered Content Generation: Content at Scale uses advanced LLMs and other technologies to generate (or optimize) written content, including articles, blog posts, social media posts, product descriptions, and more. The AI is trained on vast amounts of data to produce human-like, contextual content.
  • Efficient Content Creation: With Content at Scale, all you need is to input prompts, topic ideas, or outlines, and the platform will generate the full content piece. 
  • Content Optimization: The platform offers features like content optimization, multi-language support, content repurposing, and analytics to help users maximize the impact of their content and increase visibility.
  • AI Detection: Content at Scale also offers a robust AI detection model, which we’ve reviewed in the past here.

So whether you're looking to streamline your content workflow or expand your production, Content at Scale is definitely worth exploring. It just might be the content generation solution you've been searching for.

But if you’ve clicked this article, you likely already know what C@S is. So, enough teasers, and let’s go into the meat of the discussion.

What is Content At Scale’s Deep Research?

Unlike most new features, Deep Research doesn’t come with a fancy button — it’s just something that’s integrated to Content at Scale’s AI SEO writer. So, how does it work?

When Content at Scale reworked their website back in November, one of their biggest additions was RankWell. They utilized it by using its features to make the platform’s keyword research, optimization, and briefs better. With RankWell, Content at Scale was able to create better long-form content from scratch.

Deep Research is the evolution of that. Now, Content at Scale will be able to:

  • Research better. Content at Scale can scrape the web for information in more depth and analyze them to create top-ranking articles. It now has better access and understanding of news articles and social content.
  • Implement a knowledge database. Each blog content can access a custom database filled with information relevant to your topic or keyword. 
  • Write undetectable articles. Content at Scale stacks multiple generative models to create well-written content that reads human.

Content at Scale’s Deep Research: Output Quality

I don’t know about you, but I’ve heard enough to convince me. The next step is to review their output. So, I’ll use the keyword “Midjourney” to write an article. Here’s what Content at Scale gives me:

As expected, Content at Scale’s article is well-written and researched. But if I had to go in-depth, here’s my full review of the output:

The article is free of grammar or punctuation mistakes. Its tone is friendly and not too overly formal, a little like presenting a project to your college class. Most importantly, the research is truly more in-depth this time around.

However, if I were to nitpick its issues, here are some that I’ve noticed:

  • Fluff. There are some headers that don't deserve their own section. For instance, do we really need to rehash Midjourney’s unique features in the comparison section if we already have an overview of it in earlier parts?
  • Common AI words. There are still some telltale AI words present in the article like “revolutionize,” “streamline,” “stunning,” and more.
  • Wrong information. The output talks about only being able to access Midjourney through Discord, when they’ve already gradually rolled out the alpha version of their web application to most users.

Can Its Output Bypass AI Detection?

One of Deep Research’s promises is to create undetectable, human-like articles. That means bypassing AI detection tools. Using some of the best AI detectors we tested this year, here are the detection results of the output above:

Sapling AI

Copyleaks

GPTZero

Content at Scale

The Bottom Line

Content at Scale proves that not all updates have to be flashy. Sometimes, the results speak for themselves.

Deep Research is definitely a step up from Content at Scale’s previous versions. However, it’s still far from perfect. Apart from fluff and incorrect information, the fact that it only passed two of four AI detectors (one of which is theirs) isn’t a good sign. That said, I’m completely confident that the team behind the platform can fix this in the future.

Would I recommend Content at Scale? Absolutely, especially with Deep Research. If you want to learn more about this incredible tool, you can read our full review here or our tutorial here. Good luck!

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Written by John Angelo Yap
Hi, I'm Angelo. I'm currently an undergraduate student studying Software Engineering. Now, you might be wondering, what is a computer science student doing writing for Gold Penguin? I took up studying computer science because it was practical and because I was good at it. But, if I had the chance, I'd be writing for a career. Building worlds and adjectivizing nouns for no other reason other than they sound good. And that's why I'm here.
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