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Co-authored-by: Marco Otte-Witte <[email protected]>

src/cases/kisters.md

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   <h3 class="case-study__heading">The challenge</h3>
     <div class="case-study__text">
       <p>To be able to <strong>handle large traffic spikes efficiently without risking astronomic cloud expenses, KISTERS needed the ability to dynamically move computation</strong> between traditional servers and edge functions, or offloading to end user's devices.</p>
-      <p>Why? Well, the data KISTERS obtains from their sensor devices needs processing to become useful for analysis. The concrete processing steps taken, such as filtering, grouping, and post-processing, depend on dynamic inputs reflecting the needs of KISTERS’ clients. Therefore, they need to be carried out on user demand.</p>
+      <p>The large amounts os data KISTERS obtains from sensor devices needs processing to become useful for analysis. Filtering, grouping, and post-processing, depend on dynamic inputs and need to be carried out on demand.</p>
       <p>That in and of itself warrants a well-optimized design and use of performant technologies, but in times of calamity, <strong>the amount of users requesting information may rise quickly</strong>: residents of endangered areas can use KISTERS’ software to obtain real-time information regarding their situation.</p>
-      <p><strong>To handle large demand efficiently</strong>, without risking astronomic cloud expenses, KISTERS’ sought to employ a design that would allow them to <strong>quickly deploy their data processing to edge functions</strong> and, in case of extreme traffic surges, allow the processing steps to be <strong>run within the browser</strong> on end user’s devices.</p>
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