Build 'ScholarGuard AI'—a governed AI inference gateway specifically for higher education. This platform acts as a middleware between university LMS (Learning Management Systems) and foundational model providers. It ingests the latest government safety guidelines and 'slow-roll' restrictions in real-time, dynamically filtering model outputs and restricting access to specific model capabilities based on the user's role (e.g., Undergraduate vs. PhD Researcher). It provides a 'Federal Compliance Log' for every query, ensuring the university can prove to auditors that no prohibited model features were accessed by students.

Market Catalyst & News Trigger

"The White House is asking OpenAI to slow roll the release of its new model over safety concerns"

The Workflow Friction

The federal government is actively intervening in AI release cycles, creating a bottleneck for universities and research institutions that rely on cutting-edge models for curriculum and research. When models are 'slow-rolled' or restricted, academic programs face immediate obsolescence in their teaching materials. Furthermore, using restricted models in a classroom setting exposes universities to federal compliance risks if the model generates harmful content or violates new executive orders. There is no 'Academic Safe Zone' infrastructure that allows universities to access advanced AI capabilities while guaranteeing strict adherence to government safety mandates.

Problem Summary

Real-world problem signal validation.

One-Shot MVP Builder Blueprint (48 Hours)

Construct a proxy server that sits between a generic LMS and an LLM API. The MVP dashboard must allow an administrator to upload a text file of 'Restricted Topics' (simulating a White House memo) and demonstrate the system blocking student queries that trigger those topics while allowing PhD-level researchers with elevated credentials to bypass specific filters with a logged justification.

Recommended Developer Tech Stack

• Go
• Kubernetes
• LangChain
• React
• Neo4j