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| Pillar | Technical Goal | Business Impact | |--------|----------------|-----------------| | | Deploy a dynamic, AI‑driven path selection engine capable of reallocating bandwidth in milliseconds, using reinforcement learning to anticipate congestion. | Reduce average packet loss from 0.72 % to <0.15 %, enabling smoother video‑streaming and IoT telemetry. | | B. Zero‑Trust Revamp | Replace the legacy TLS 1.0/1.1 stack with TLS 1.3 + post‑quantum cryptography (PQC) hybrid keys and embed mutual attestation for every node. | Harden the network against emerging quantum threats and satisfy enterprise compliance (PCI‑DSS, GDPR‑R). | | C. Edge‑First Telemetry | Introduce eBPF‑based observability at every edge node, feeding a real‑time analytics pipeline into the NebulaNet console. | Cut incident detection time from 12 minutes to under 30 seconds, giving operators a decisive edge. | 3. The Development Journey 3.1. The AI Routing Engine The routing overhaul began as a research prototype in LumenCore’s Quantum‑Edge Lab . Lead data scientist Dr. Maya Patel trained a deep reinforcement learning model on synthetic traffic patterns that mimicked the “flash‑crowd” behavior of large‑scale live events. After six months of simulation, the model was distilled into a lightweight inference service that could run on commodity edge hardware.
