Kyvos turned the semantic layer into the AI layer.
What's running under it?

The framing on kyvosinsights.com is clear: "A universal semantic layer for AI and BI" — sub-second analytics on petabyte data in Snowflake, Databricks, BigQuery, Synapse, and Redshift. Pre-aggregated cubes, governed metric definitions, the same semantic model feeding both dashboards and LLMs.

That puts Kyvos in the path of every customer's most sensitive workflow: an LLM, a copilot, or an agent asking questions of their enterprise data. The cube is the answer. The question — and what comes back — needs a runtime that handles cost, identity, PII, and latency before it ever hits a model provider. That's where Cloudflare slots in.

The primitives that map cleanly to where Kyvos is going:

AI Gateway — under every text-to-cube and copilot call. Caching, fallback routing, PII redaction, per-tenant spend caps before the model provider sees the prompt.
Workers AI — for inference paths where the customer doesn't want their semantic dictionary or query patterns sitting in a third-party model log.
Vectorize — for the embeddings index over metric definitions, hierarchies, and business glossaries that grounds RAG against the cube.
R2 — egress-free object store for cube materializations, exports, and snapshots. Complements Snowflake/Databricks, doesn't compete with them.
Durable Objects — per-tenant, per-session state for long-running analytical conversations and shared exploration.
Workers — semantic API and embedded dashboards served from 330+ POPs, close to whichever office or region is querying.