Data quality or AI failure
AI-ready data isn’t optional. Quality, lineage, semantics, and bias mitigation come first or AI projects stall. Gartner expects 30% of GenAI projects to fail from poor data and risk controls—avoid that fate by engineering trust into the data.
Catalogs and embeddings
Adopt catalogs, labeling, enrichment, and embedding readiness so models consume reliable signals. Vector stores without quality and governance just embed bad data faster.
Governance as foundation
Governance and security are pillars of AI readiness. Access controls, lineage, and auditability keep AI from becoming a liability. Make them default settings, not afterthoughts.
sys3(a)i POV: We approach critical systems work by stress-testing architectures, integrating observability and governance from day one, and designing sovereign or edge footprints where independence and continuity matter most.