Secondary TemporalStore Workload

Feature serving is a secondary workload on the same time-aware model.

TemporalStore's primary story is LLM context management and prompt engineering. The same time-aware model can also serve secondary workloads that depend on recent events, time windows, filters, counters, long sequences, and replayable state.

Where it fits

Fraud, ads, recommendation, marketplace, risk, and personalization systems often need the same shape of state: latest values, high-cardinality aggregates, event sequences, filtered counts, frequency caps, and freshness checks. TemporalStore keeps that state queryable in the online path while preserving durable history for replay and recovery.

Events and batch updates
clicks, views, orders, labels, tool results TemporalStore
windows, counters, sequences, latest state Online serving
ranking, risk, personalization, policy

Useful patterns

Serve latest features and profile-adjacent temporal state at request time.
Compute rolling windows, filtered aggregates, distinct counts, and frequency caps near entity state.
Read long behavior sequences without forcing every workload through offline materialization first.
Replay the state behind a serving decision for debugging, evaluation, and governance.
Use MatrixDB beside TemporalStore when the workload is mostly hot Redis-compatible profile or hash KV.