<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Dograh on René Zander | AI Automation Consultant</title><link>https://renezander.com/tags/dograh/</link><description>Recent content in Dograh on René Zander | AI Automation Consultant</description><generator>Hugo</generator><language>en</language><lastBuildDate>Thu, 21 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://renezander.com/tags/dograh/index.xml" rel="self" type="application/rss+xml"/><item><title>Production Self-Hosted Voice AI Platform For Data-Residency-Sensitive Teams</title><link>https://renezander.com/case-studies/voice-ai-self-hosted-eu-vpc/</link><pubDate>Thu, 21 May 2026 00:00:00 +0000</pubDate><guid>https://renezander.com/case-studies/voice-ai-self-hosted-eu-vpc/</guid><description>&lt;h2 id="the-problem">The problem&lt;/h2>
&lt;p>Voice AI platforms like Parloa, Cognigy, Vapi, and Retell are useful, but enterprise teams often need more control than a hosted voice SaaS gives them. The recurring concerns:&lt;/p>
&lt;ul>
&lt;li>Where does call data go?&lt;/li>
&lt;li>Can the system run in a trusted VPC?&lt;/li>
&lt;li>Can STT, LLM, and TTS providers be swapped?&lt;/li>
&lt;li>Can costs be controlled at scale?&lt;/li>
&lt;li>Can learnings from calls feed back into the business?&lt;/li>
&lt;li>Can the workflow be reviewed before changes reach production?&lt;/li>
&lt;/ul>
&lt;h2 id="the-solution">The solution&lt;/h2>
&lt;p>A production-oriented self-hosted voice AI setup with operational control as the design centre:&lt;/p></description></item></channel></rss>