Oliver is built for analytical workloads where the data is enormous, the window is measured in milliseconds, and agents need complete, current evidence instead of a sampled or stale view.
Score transactions and flag anomalies against the full historical record in milliseconds — not the sampled, batched view most fraud stacks settle for.
Run thousands of competing trading hypotheses in parallel, sharing context to check each other for bias — not just the concurrency modern data platforms already solved.
Resolve bid decisions and attribution against high-cardinality event streams at the latency real-time bidding demands.
Query logs, metrics, and traces at full resolution and give AI SREs the evidence and reasoning history to resolve incidents faster.
Run competing hypotheses in parallel across large experimental datasets, and let the swarm converge on the best-supported one.