Fewer Tokens. Fewer Wrong Answers

Yes. Direct connections cover hundreds of enterprise systems, and an on-premise reverse tunnel reaches the ones that were never meant to be public—no inbound firewall holes required.

Caching absorbs AI query load so slow or rate-limited APIs don’t stall requests—and so you aren’t billed twice for calls already made. It replaces the separate ETL pipeline you’d otherwise build, not your source of truth—queries stay live against your systems, and the cache is refreshed from them, never a stale copy.

No. Connectors understand your custom objects, fields, and relationships out of the box. Data modeling lets you define what matters once—an improvement on a working system, not a prerequisite.

Joins, aggregation, and filtering happen in the connectivity layer, so the model receives the pre-answered result instead of the raw dataset—up to 60% lower token cost on large dataset analysis. Large files are handled outside the context window entirely.

Both. Every connector supports read and write, so an agent can update a record, create an order, or log an activity in the same governed layer it queries through—with the same policy and lineage applied.

Yes. Every data model carries full lineage, so an answer is traceable back to the fields and systems it came from—for the AI, and for whoever has to sign off on it.