📄️ 5-layer pipeline deep dive
Complete internal architecture of Aucert's 5-layer AI quality pipeline with MCP protocol, code paths, and model assignments
📄️ Knowledge Graph internals
Internal architecture of the Aucert Knowledge Graph engine — data model, storage, ingestion pipeline, and query patterns
📄️ Service registry
Codename → what it is. The canonical map of Aucert's services, agents, personas, and domains.
📄️ Device Twin architecture
Internal architecture of the AI Device Twin — calibration, prediction, and confidence adjustment for emulator-to-device divergence
📄️ Validation Graph — design walkthrough
Interactive walkthrough of the Validation Graph design (SPEC-035) — two-graph knowledge layer, substrate, ACL, conditions, embeddings, deployment, tech choice
📄️ Validation graph — implementation architecture
How the merged validation graph (SPEC-035) works in code — the append-only claim model, the storage engine's read/write/traversal/bitemporal paths, registries, ACL, vector search, and the cross-cutting RLS, DI, and open-core patterns.
📄️ Verification Cascade
4-stage confidence-gated verification system for minimizing false positives with cost-aware escalation
📄️ Model orchestration
How Aucert routes AI workloads across model tiers for cost optimization and quality balancing
📄️ Azure AI Foundry architecture
Why Aucert has no LLM models running in AKS — and where they actually run