Local inference
LLMs running on-premise or inside the client perimeter. Right-sizing the model, hardware optimization, OpenAI-compatible APIs.
Technical capabilities · Why it works
The competencies that make the method possible.
The method isn't a philosophy. It lives on a precise technical stack: local inference, hybrid retrieval, vector databases, containers, Kubernetes, observability, security, CI/CD.
People who install two containers don't solve problems. People who know the architecture solve the right ones.
Eight key competencies
The technical stack behind the method
RAG and hybrid retrieval
Vector + keyword + re-ranking. Semantic chunking calibrated to the corpus, verifiable citations, confidence gates.
Vector databases
Qdrant, Weaviate, Milvus, Chroma, pgvector. Sized and optimized to the use case. Multi-tenancy and data isolation.
Containers and Kubernetes
Containerized stack, hardened K8s deployments, backup, monitoring. Enterprise and Italian PA experience.
Observability and tracing
Langfuse, OpenTelemetry, AI pipeline metrics: latency, token cost, output quality, model drift.
Security and governance
Container hardening, network segregation, secrets, prompt audit. GDPR, NIS2, AI Act compliance.
CI/CD for AI systems
Deployment pipelines for AI stacks. Deterministic gate tests, regression on model swaps, golden sets in CI.
Systems thinking
Architectures that decouple model, application and process. Fallbacks, graceful degradation, human control at critical points.
Want to test capabilities against a real case?
A short technical assessment verifies whether our competencies fit your actual problem.