ibl.ai 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. ibl.ai calls a single API and lets Bedrock handle model hosting, scaling, and prompt‑routing.
Amazon ECS (Fargate) – container orchestration for ibl.ai’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 ibl.ai 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.
By pairing AWS’s managed AI and cloud stack with ibl.ai’s education‑first tooling, universities get a secure, cost‑efficient path to bring generative AI into every classroom.
Learn more at ibl.ai