Inside the Case
Problem
Data fragmentation and analytics latency delayed critical decision-making processes.
Solution
- Built a unified data ingestion and streaming pipeline via Kafka.
- Implemented auto-scaling inference services within a Kubernetes cluster (HPA).
- Integrated LLM Agents and a rules-engine to generate instant decisions.
- Set up a continuous delivery pipeline: GitHub Actions → ArgoCD → Kubernetes.
Result
- System response time is under 1 second.
- AI provides actionable systemic recommendations, not just raw metrics.
- The platform automatically scales under heavy loads.
Kafka StreamsLLM AgentsAuto-scaling Inference
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