How mentorAI Integrates with Microsoft
mentorAI launches as a one-click Azure Marketplace app, runs its APIs on AKS, and routes prompts to Azure OpenAI Service models like GPT-4o, GPT-4 Turbo, GPT-3.5 Turbo, and Phi-3—letting universities tap enterprise LLMs without owning GPUs. Traffic and data stay inside each tenant’s VNet with Entra ID SSO, Azure Content Safety filtering, AKS auto-scaling, and full Azure Monitor telemetry, so campuses meet FERPA-level privacy while paying only per token and compute they actually use.
mentorAI is available as a one‑click deployment on the [Microsoft Azure Marketplace](https://azuremarketplace.microsoft.com/en-us/marketplace/apps/iblai.mentorai?tab=Overview) where universities can launch a fully managed instance inside their own subscription. Once deployed, mentorAI relies on Azure OpenAI Service for large‑language models, AKS (or Container Apps) for its microservices, and the wider Azure stack—identity, data, and monitoring—to deliver secure, FERPA‑compliant generative AI at campus scale.
Key Azure Building Blocks
- Azure OpenAI Service – direct access to all of OpenAI's latest models; mentorAI chooses the best model per query while Azure handles GPU capacity.
- Azure AI Studio & Content Safety – fine‑tune or ground models on university data, and apply Microsoft safety filters before answers reach students.
- Azure Kubernetes Service (AKS) – container host for mentorAI APIs, orchestration engine, and background workers; scales automatically during finals season.
- Azure SQL / Cosmos DB – relational or NoSQL store for user profiles, transcripts, and analytics. Isolation can be per‑schema or per‑database to satisfy strict data policies.
- Azure Storage – durable object storage for lecture uploads, embeddings, and backups, partitioned by tenant folder or container.
- Azure Virtual Network + Private Endpoints – traffic stays on Microsoft’s backbone; each tenant can run in its own VNet with subnet‑level segmentation.
- Microsoft Entra ID (Azure AD) – SSO for students and faculty; role‑based access control maps to tenant IDs for least‑privilege data access.
- Azure Monitor & Application Insights – unified logs, metrics, and distributed traces power dashboards and auto‑scaling triggers.
How mentorAI Uses Azure Day‑to‑Day
1. User query arrives. An Application Gateway routes HTTPS traffic to AKS pods running the mentorAI API. 2. Model selection. The orchestration layer calls Azure OpenAI, picking GPT‑4o for rich tutoring or GPT‑3.5 Turbo for quick FAQ queues. 3. Context enrichment. Course PDFs in Azure Storage are chunked, embedded via Azure AI Search, and injected into the prompt. 4. Response & telemetry. The answer returns in <1 s; tokens, latency, and cost stream to Azure Monitor. Role‑based logs are stamped with TenantID for audit.Why Azure Matters to Universities
- Enterprise‑grade compliance – Azure certifications (FERPA, HIPAA, FedRAMP High) and Private Link keep student data locked down.
- Deep Microsoft ecosystem – native hooks into Teams, Outlook, and OneDrive streamline faculty workflows.
- Elastic scale, predictable cost – AKS autoscaling and pay‑per‑token OpenAI pricing prevent budget surprises.
- Granular identity & RBAC – Entra ID ties AI access to existing campus roles; conditional access policies add extra safeguards.
- Innovation runway – as Microsoft releases new models or new AI Safety features, mentorAI adopts them with a config toggle.
Related Articles
How mentorAI Integrates with Blackboard
mentorAI integrates with Blackboard Learn using LTI 1.3 Advantage, so every click on a mentorAI link triggers an OIDC launch that passes a signed JWT containing the user’s ID, role, and course context—providing seamless single-sign-on with no extra passwords or roster uploads. Leveraging the Names & Roles Provisioning Service, Deep Linking, and the Assignment & Grade Services, the tool auto-syncs class lists, lets instructors drop AI activities straight into modules, and pushes rubric-aligned scores back to Grade Center in real time.
How mentorAI Integrates with Brightspace
mentorAI plugs into Brightspace via LTI 1.3 Advantage, letting the LMS issue an OIDC-signed JWT at launch so every student or instructor is auto-authenticated with their exact course, role, and context—no extra passwords or roster uploads. Thanks to the Names & Roles Provisioning Service, Deep Linking, and the Assignments & Grades Service, rosters stay in sync, AI activities drop straight into content modules, and rubric-aligned scores flow back to the Brightspace gradebook in real time.
How mentorAI Integrates with Google Cloud Platform
mentorAI deploys its micro-services on GKE Autopilot and streams student queries through Vertex AI Model Garden, letting campuses route each request to Gemini 2.0 Flash, Gemini 1.5 Pro, or other models with up to 2 M-token multimodal context—all without owning GPUs and while maintaining sub-second latency for real-time tutoring. Tenant data stays inside VPC Service Controls perimeters, usage and latency feed Cloud Monitoring dashboards for cost governance, and faculty can fine-tune open-weight Gemma or Llama 3 right in Model Garden—making the integration FERPA-aligned, transparent, and future-proof with a simple config switch.
How mentorAI Integrates with Groq
mentorAI plugs into Groq’s OpenAI-compatible LPU API so universities can route any mentor to ultra-fast models like Llama 4 Maverick or Gemma 2 9B that stream ~185 tokens per second with deterministic sub-100 ms latency. Admins simply swap the base URL or point at an on-prem GroqRack, while mentorAI enforces LlamaGuard safety and quota tracking across cloud or self-hosted endpoints such as Bedrock, Vertex, and Azure—no code rewrites.