AI4RA Tip of the Week #12

What is MindRouter? 
MindRouter is an AI gateway developed by Luke Sheneman at the University of Idaho’s Research Computing and Data Services (RCDS). It is freely available for you to install and use under an open-source software license. It sits as a middle layer between tools like Vandalizer and the AI models that power them, routing requests across a cluster of GPU servers and providing a single, reliable access point for running large language models on university-controlled infrastructure. 

For end users, MindRouter offers a built-in chat interface, document upload, and API access compatible with OpenAI, Ollama, and Anthropic formats. Behind the scenes, it manages fair access across user groups, handles server failures automatically, and logs every interaction for compliance purposes. 

Why This Matters for Research Administrators 
AI tools like Vandalizer are only as good as the AI and Optical Character Recognition (OCR) models powering them. A workflow that performs well on proper infrastructure can produce poor or unreliable results when running on an underpowered laptop with a small model and no OCR capability. This is not a minor concern; it is the difference between a tool that transforms your work and one that disappoints and gets abandoned, or worse, provides inaccurate information or incomplete responses. 

When institutions deploy Vandalizer (or any AI solution) without the needed underlying infrastructure, several things can go wrong: scanned PDFs may not be readable without a configured OCR endpoint, model outputs may be inconsistent or incomplete due to limited context windows, and results may vary significantly depending on the model used, even when running the same workflow. We want to be transparent: when we describe a workflow as tested or verified, that testing was done against specific models and OCR configurations. Outputs from different setups may differ. 

How to Use This Resource 
This tip is especially relevant for IT staff and research computing personnel at institutions considering a Vandalizer deployment. A well-functioning setup requires decisions about GPU hardware, inference engines, model selection, quantization, context window configuration, and OCR endpoints, not just the Vandalizer application itself. 

MindRouter is one part of that solution. The AI4RA team is developing a hardware and configuration guide for institutions looking to deploy LLMs and OCR at various scales. In the meantime:  

1. If your institution already has MindRouter or a compatible inference layer, connect Vandalizer to it rather than running against a local or laptop-based model.
2. Ensure an OCR endpoint is configured before processing PDFs. AI needs machine readable content, and OCR is necessary to convert messy images-based PDFs into a form which is readable.  
3. When evaluating workflow results, ask which model and OCR configuration was used. Workflow performance is tied to the specific models tested against.
4. IT staff interested in self-hosting can find full MindRouter documentation at https://mindrouter.ai/documentation.html. Reach out to the AI4RA team if your institution needs guidance on infrastructure planning. 

How can I learn more about this topic?  
MindRouter – https://mindrouter.ai 
AI4RA Technical Office Hour – An Office Hour geared specifically toward IT professionals: August 4th at 11:00AM PDT 
AI4RA Office Hours – Occurs every fourth Tuesday at 11 AM PST and includes live Q&A and demos (https://ai4ra.uidaho.edu/#events).  

Share your questions or success stories  
Post them to the AI4RA community forum (https://groups.ai4ra.uidaho.edu/g/main/topics). Your tips help the whole research‑admin community thrive.