Architecture Advanced¶
How production AI platforms are really designed — at scale, with security, cost and failure modes on the table. Builds on the free Architecture notes.
What's inside¶
Multi-tenant RAG platform
Per-customer isolation, permission-aware retrieval, incremental ingestion of millions of documents, hybrid search and re-ranking.
TenancyACL filteringIngestion queues
LLM gateway
One entry point for every model: routing, fallbacks between providers, token-based rate limits, semantic caching and cost per team.
RoutingFallbacksCost control
Enterprise agent platform
Tool registry with permissions, human approval for risky actions, sandboxed execution, step budgets and full tracing.
Tool governanceApprovalsTracing
Real-time voice AI agent
WebRTC audio, streaming speech-to-text → LLM → text-to-speech, interruptions (barge-in) and a sub-second latency budget.
WebRTCStreamingLatency budget
Document intelligence pipeline
OCR, layout and table extraction, schema-validated output, confidence scoring and a human review queue for edge cases.
OCRStructured outputHuman-in-the-loop
LLM evaluation & observability
Golden datasets, LLM-as-judge with calibration, production tracing, dashboards and regression gates in CI.
LLMOpsEvalsCI gates
GraphRAG & knowledge graphs
Entity and relation extraction, combining graph traversal with vector search for multi-hop questions.
Knowledge graphMulti-hopHybrid retrieval
Guardrails & safety layer
Prompt-injection defence, PII redaction, output validation and a policy engine shared by every AI feature.
SecurityPIIPolicy
Every architecture includes¶
- Requirements and scale estimates (users, documents, requests, tokens)
- Component diagrams for the offline and online paths
- Trade-off tables — what to choose, and when not to
- Failure modes, security review and cost model
- The interview questions it answers, with model answers
Get notified when the Architecture track launches — early-bird price for the waitlist.