How mentorAI Integrates with Amazon Web Services
mentorAI runs natively on AWS: it taps Amazon Bedrock’s fully managed API to access Titan, Claude, Llama and other foundation models without universities having to manage GPUs, while its containerized micro-services auto-scale on ECS Fargate to keep response times steady during peak weeks and store tenant-segregated transcripts in RDS Postgres/Aurora silos or schemas protected by VPC/IAM boundaries. This architecture lets campuses spin up pilots or university-wide deployments, maintain FERPA/GDPR data sovereignty, and adopt any new Bedrock model with a simple config switch.
mentorAI rides on Amazon Web Services to give universities state‑of‑the‑art generative AI without the operational overhead of running GPUs or complex infrastructure. Amazon Bedrock supplies the foundation models, while core application services run on AWS’s secure, elastic cloud stack. The result is a turnkey platform that scales from a pilot course to an entire campus and still meets strict data‑privacy mandates.
Key AWS Building Blocks
- Amazon Bedrock – managed gateway to Anthropic Claude, Amazon Titan, Meta Llama, Mistral, and more. mentorAI calls a single API and lets Bedrock handle model hosting, scaling, and prompt‑routing.
- Amazon ECS (Fargate) – container orchestration for mentorAI’s microservices (API, orchestration engine, background workers). Autoscaling keeps response times steady during exam rushes.
- Amazon RDS (Postgres/Aurora) – multi‑tenant relational store for user data and transcripts. Isolation can be per‑schema (bridge) or per‑instance (silo) to satisfy campus compliance rules.
- VPC + IAM – private networking and fine‑grained roles ensure each university’s traffic and data stay fenced off. Optionally, each tenant runs in its own AWS account under AWS Organizations.
- Amazon S3 – durable storage for files, lecture uploads, and model artifacts, partitioned by tenant prefix or bucket.
- Amazon CloudWatch – logs, metrics, and alarms feed dashboards and auto‑scaling policies so ops teams spot issues before students do.
How mentorAI Uses AWS Day‑to‑Day
1. Student query arrives. An API container in ECS receives the request and forwards it to the orchestration layer. 2. Model selection. The orchestration layer hits Amazon Bedrock, choosing (or letting Bedrock automatically route to) the best model for that prompt—Claude for deep reasoning, Titan for embeddings, Llama for open‑source parity, etc. 3. Context fetch. Relevant course docs are pulled from RDS/S3 and injected into the Bedrock prompt. If the task is complex, Bedrock Agents spin up sub‑tasks automatically. 4. Response & audit. The answer returns to the student in <1 s, while logs and metrics stream to CloudWatch. Usage counts against the tenant’s quota for cost tracking.Why AWS Matters to Universities
- Instant access to multiple LLMs without GPU budgets.
- Elastic scale – handles finals‑week traffic, shrinks after.
- Data sovereignty – per‑tenant VPCs, encryption, FERPA/GDPR alignment.
- Lower ops burden – AWS manages compute, Bedrock manages models; campus IT focuses on pedagogy, not patches.
- Future‑proof – drop‑in new models or switch regions with a config change.
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