Model Serving
Designing and deploying local and private cloud model serving infrastructure with vLLM, unified LiteLLM API gateways, spending controls, and Keycloak authentication.
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Intelligence Engineering
Architecting sovereign on-premise and private cloud AI systems, covering private model serving, hybrid knowledge retrieval, Model Context Protocol (MCP) tooling, and stateful multi-agent orchestration.
Technical Overview
Intelligence engineering encompasses the architectural design, deployment, and operationalization of local and private artificial intelligence systems in enterprise environments. Rather than relying on black-box commercial APIs, this discipline focuses on verifiable, self-hosted machine learning components and deterministic software architectures.
Core architectural priorities include data sovereignty, predictable inference latency, and robust integration with existing software systems. The discipline structures AI workflows into five core engineering modules:
Composable Modules
- Model Serving: Deployment and configuration of private inference runtimes (vLLM, TensorRT-LLM) and API routing gateways with authentication and rate limiting.
- Knowledge Retrieval: Hybrid retrieval pipelines pairing vector search (Qdrant) with property knowledge graphs (Neo4j) and structured session memory.
- Tool Integration: Connecting language models to databases, enterprise APIs, and local runtimes using the Model Context Protocol (MCP) and schema validation.
- Agentic Orchestration: Multi-agent coordination graphs with explicit state management (LangGraph), conditional routing, and human-in-the-loop checkpoints.
- System Observability: Distributed tracing, latency and token metrics, continuous evaluation benchmarks, and interactive UI steering interfaces.
Guiding Principles
- Data Sovereignty: Execution of open-weight models on dedicated enterprise infrastructure without external data egress.
- Deterministic Grounding: Hybrid retrieval combining dense vector similarity with property graph traversal to minimize hallucinations.
- Protocol Standardization: Standardized integration of external data sources and execution tools via the Model Context Protocol.
- Stateful Control: Multi-agent coordination using explicit state machines, persistent checkpointing, and execution bounds.
