Loading…
Blog · AI & ML
Writing on the operating layer that decides whether enterprise AI ships. Evaluation discipline, retrieval architecture, governance, cost ceilings, and the failure modes we see most often in the field.
Browse other tracks
16 editorial tracks
16 tracks · scroll to browse
Posts
82% of enterprise AI projects never reach production. The reason isn't model quality; it's the operating substrate: eval, governance, and deployment topology.
What separates a RAG demo from a production-grade system. Twelve concrete items, in order, with the failure mode each one catches.
Models change every quarter. The dataset that tells you whether the new model is better than the old one for your workload is the asset that compounds. Most teams haven't started.