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    <title>DEV Community: Apollo</title>
    <description>The latest articles on DEV Community by Apollo (@useapolloapi).</description>
    <link>https://dev.to/useapolloapi</link>
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      <title>DEV Community: Apollo</title>
      <link>https://dev.to/useapolloapi</link>
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    <item>
      <title>How we reduced our review time by 90%</title>
      <dc:creator>Adrian Brown</dc:creator>
      <pubDate>Thu, 11 May 2023 21:30:47 +0000</pubDate>
      <link>https://dev.to/useapolloapi/how-we-reduced-our-review-time-by-90-3hnc</link>
      <guid>https://dev.to/useapolloapi/how-we-reduced-our-review-time-by-90-3hnc</guid>
      <description>&lt;p&gt;At &lt;a href="https://apolloapi.io"&gt;Apollo&lt;/a&gt;, we use a no-code automation platform to allow users to manage their integrations &amp;amp; tasks. There are many debates over whether automating content moderation is a one-size-fits-all approach, it's not. AI powered content moderation has become table stakes in protecting and enhancing user experience at scale, but for us, visibility into other areas of enterprise/personal decision making led us to use tools like automation rules &amp;amp; workflow platforms. &lt;/p&gt;

&lt;h2&gt;
  
  
  TLDR;
&lt;/h2&gt;

&lt;p&gt;One case study for content moderation from &lt;a href="https://yikyak.com/"&gt;YikYak&lt;/a&gt;, we migrated from &lt;a href="https://retool.com/"&gt;Retool&lt;/a&gt;. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://media.dev.to/cdn-cgi/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F24ry4zjl0f19sok7moqf.png" class="article-body-image-wrapper"&gt;&lt;img src="https://media.dev.to/cdn-cgi/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F24ry4zjl0f19sok7moqf.png" alt="Image description" width="800" height="329"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;h2&gt;
  
  
  The bigger, the slower
&lt;/h2&gt;

&lt;p&gt;With all the advantages, there are a few drawbacks to using retool and a standalone automation system for task monitoring/completion. We noticed a particular drawback when scaling the number of users and amount of activity with each one: The time it takes to detect bad actors accurately and then investigate. So a typical task for a investigation would run anywhere between 3 minutes to 4 days. And that's for each time an automated flag was created or a user report was sent.&lt;/p&gt;

&lt;p&gt;More than that, truly having accurate data was a nightmare. Standalone automation systems (environments with one AI model) would score correctly only a fraction of the time.&lt;/p&gt;

&lt;p&gt;This amount of time spent investigating and analyzing to make an informed decision reduced the moderator efficiency and wasted collectively so much talented people's time. Being a social app dedicated to curating safer community, this was unacceptable.&lt;/p&gt;

&lt;h2&gt;
  
  
  Debugging the slowest tasks
&lt;/h2&gt;

&lt;p&gt;Inspecting a typical 4 day case/support ticket, it was clear that two specific steps took almost 70-80% of the overall time:&lt;/p&gt;

&lt;ul&gt;
&lt;li&gt;Gathering metadata from existing platform policy, account information and community guidelines: 4 Hours&lt;/li&gt;
&lt;li&gt;Connecting historical data to recent events: ~1 day&lt;/li&gt;
&lt;/ul&gt;

&lt;h2&gt;
  
  
  Migrating from Standalone Automation Systems to Integrated Automation System
&lt;/h2&gt;

&lt;p&gt;Retool coupled with our standalone automation system provided a fast, efficient infrastructure as a service toolkit, and from some of the benchmarks, there was a massive improvement in active response time when migrated off of a independent automation system to an integrated automation system like &lt;a href="https://apolloapi.io"&gt;Apollo&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;Moving from Retool + Standalone automation took around 2 minutes, the migration to integrated automation (&lt;a href="https://apolloapi.io"&gt;Apollo&lt;/a&gt;) was effortless: Just adding metadata about your internal databases and a click of a button, that's all. The aggregation and integration for each database/api resource were efficiently synced, in... wait for it... just milliseconds! And that's without any code at all! After integrated automation tools like &lt;a href="https://apolloapi.io"&gt;Apollo&lt;/a&gt; increases the accuracy of the scored content, it takes less than 40 seconds to deploy and integrate multiple AI models under one API.&lt;/p&gt;

&lt;p&gt;Reducing ~3 minutes to 13 seconds from the standalone's system time for every investigation and accuracy for moderators is a HUGE win. &lt;/p&gt;

&lt;h2&gt;
  
  
  Summary
&lt;/h2&gt;

&lt;p&gt;Reducing our active response times from 3+ minutes to around 13 seconds significantly impacts the user experience of our community. It also reduces the overhead incurred by regulatory violations or non-compliance, which can run a bill up to $400k a month! You can checkout our beta on our Github repository.&lt;/p&gt;

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      <category>opensource</category>
      <category>webdev</category>
      <category>python</category>
      <category>ai</category>
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