RALO sits between your agents and the models they call — slashing time-to-first-token from 1200ms to 80ms, cutting model spend, and routing every task to the right model in milliseconds.
Every RALO deployment runs on the same battle-tested layer that powers latency-critical agent workloads.
Semantic-level caching for large-scale vector retrieval. Identical and near-identical queries resolve from the edge, dramatically reducing redundant model API consumption.
Millisecond-level task dispatch across models like Claude 3.5 Sonnet and GPT-4o. RALO balances cost against intelligence on every single call, automatically.
Turn legacy relational databases — MySQL, MariaDB — into high-concurrency streaming pipelines that agents can query semantically, with zero schema migration.
RALO is currently available to a curated set of teams. Request access to join the whitelist — login and provisioning unlock once your workspace is approved.
