Changes between Version 2 and Version 3 of Public/AiTechnical

Timestamp:

Jul 20, 2026, 1:57:32 PM (29 hours ago)

Author:

Boris Horner

Comment:

—

Public/AiTechnical

@@ -146 +146 @@
 <ul>
 <li><p>We explained a simple RAG example on this page. The user interface is just an input field and an output field for result and one for the references. Call the API endpoint with the id of the process and the same input string from the user and get the same results. You can integrate this into any web or desktop application or backend process you like, for example, to implement RAG in your Content Delivery Portal.</p></li>
-<li><p>Talking about Content Delivery Portals: <b>i2ai / i2store</b> API has all the building blocks for a CDP. It stores content, assigns user permissions, exposes four-axes search and ranking, manages renditions and relations and fetches content on demand. No logic needs to be implemented, just a web frontend matching your corporate design.</p></li></ul>
-<li><p>With one API service in your network, you can make sure that the same function, wherever you use it, is identical. For example, you might have a process verifying adherence to authoring guidelines in a DITA topic. This function would make sense both in an editor context while the author is writing and as a precondition when lifecycle state is about to be changed to approved. With <b>i2ai / i2store</b>, you can call the identical function in both places, with a few lines of code each.</p></li></ul>
+<li><p>Talking about Content Delivery Portals: <b>i2ai / i2store</b> API has all the building blocks for a CDP. It stores content, assigns user permissions, exposes four-axes search and ranking, manages renditions and relations and fetches content on demand. No logic needs to be implemented, just a web frontend matching your corporate design.</p></li>
+<li><p>With one API service in your network, you can make sure that the same function, wherever you use it, is identical. For example, you might have a process verifying adherence to authoring guidelines in a DITA topic. This function would make sense both in an editor context while the author is writing and as a precondition when lifecycle state is about to be changed to approved. With <b>i2ai / i2store</b>, you can call the identical function in both places, with a few lines of code each.</p></li>
 <li><p>The API does not depend on how many users are logged into your applications. You can have multiple web frontend instances, but they don't overload your AI infrastructure with numerous, simultaneous requests. <b>i2ai / i2store</b> API manages a job queue with detailed configuration options whether and how jobs of the same type can run concurrently.</p></li></ul>
 }}}
 
+<h1 style="margin-left: 0;">Summary</h1>
+<p><b>i2ai / i2store</b> is your scalable platform for integration of AI, data and services of any kind, with applications of any kind. And you have full control.</p>
+<ul>
+<li><p>Data is stored inside <b>i2ai / i2store</b> or in their native information systems. You know and you control where this is. It can be somewhere in the cloud, or it can be air gapped on the local machine. Or anywhere in between, as the application requires. You don't need to expose data by Model Context Protocol and trust the AI vendor (even though you can), but your process builders decide and define where data gets sent.</p></li>
+<li><p>The same is true for AI models. For processing of data that's not highly confidential (like maintenance manuals that are publicly available or widely distributed), cheap and fast cloud models can be used. But you can also run models on a rented server in a defined location or even on a local, air-gapped machine.</p></li>
+<li><p>Prompts and processes are not black boxes, but they can be explicitly edited by your power users. Processes are visualized as a diagram.</p></li>
+<li><p>Everything users do is subject to detailed, group- or user-based permissions.</p></li>
+</ul>
 
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