Stream Processing converts raw movement into usable customer intelligence.
Stream processing is where business changes and behavior events become clean, joined and time-aware facts. It handles deduplication, windows, tags, cohorts and feature-ready outputs without pretending to be the AI decision engine itself.
Cleans, dedupes, joins, computes windows, tags, cohorts, intent signals and feature-ready outputs.
The value is operational, not just technical.
Agency AI gets current features instead of raw event noise. It can reason from recent intent, lifecycle state, product affinity and eligibility signals that are already cleaned and governed.
Cleans and deduplicates high-volume retail events before serving.
Joins behavior signals with business state in real time.
Computes windows, tags, cohorts and feature-ready facts continuously.
Keeps AI-serving data fresh without forcing models to parse raw streams.
What this layer is responsible for
Reads business and behavior topics from the event bus.
Applies validation, enrichment, deduplication and time-window logic.
Writes governed detail, aggregate and feature-ready outputs to the serving layer.
Keeps processing responsibilities separate from journey orchestration and AI policy.
What it makes usable
This is how the layer improves AI decisions.
Without stream processing, AI either consumes noisy raw events or stale batch tables. Both lead to low-confidence actions, duplicate triggers and poor customer timing.
A customer who abandons a cart can enter a fresh cohort within seconds.
A repeated product view can raise intent without waiting for nightly scoring.
A duplicate SDK event can be removed before it affects AI context.