Understand behaviour
while it can still change the outcome.
Turn live interactions from web, native apps, transactions, service and operations into current, governed intelligence. Collection, meaning, policy and delivery remain one managed operation.
Start with one journey or operational decision and its existing data sources.
From interaction to intelligence
One live understanding across customer and operational behaviour.
Capture what happened, connect it to current context and make the permitted insight or action available while it remains useful.
Capture with meaning
Describe web, native app, server, transaction and operational events in business language.
Control while it moves
Check quality, identity, consent and purpose before an event is stored, shared or used.
Current context for agents
Give apps and approved AI the same permitted state and action boundaries as people.
Several destinations
Serve warehouses, lakes, applications, inboxes and agents from one governed stream.
The operating difference
Bring the core behavioural-data jobs into one runtime boundary.
The jobs remain separately understandable. Your organisation does not need to operate a separate customer-facing service for every handover.
Collection and meaning
Composed stack
Channel SDKs, tracking plans and a separate schema registry must stay aligned.
Datastreams runtime
The stream definition connects capture, meaning and validation.
Enrichment and identity
Composed stack
Processors and warehouse jobs add context after the interaction.
Datastreams runtime
Lookups, state and permitted identity context are applied while the event is useful.
Consent and delivery
Composed stack
Permission sits elsewhere and each destination needs another loader or reverse pipeline.
Datastreams runtime
Purpose and permission determine the destination-specific output before delivery.
Operations
Composed stack
Teams monitor several services, failure queues and provider contracts.
Datastreams runtime
One serviced runtime exposes the active definition, exceptions, delivery and evidence.
One accountable runtime
Keep the operation close to the data in motion.
Capture, meaning, control and delivery execute inside one managed boundary sized for the agreed workload. Every extra provider remains explicit, including its responsibility, data movement and cost.
Compare it with your stackFewer handovers
Reduce collectors, brokers, workers and reverse pipelines where the stream covers their job.
Compute in motion
Apply validation, enrichment and rules before the event loses its business value.
Visible workload
Size and price the service against representative volume, state and processing needs.
Where the delay disappears
Use the same governed context across the journey.
The practical value appears when insight, permission and action meet before the customer or operational moment has passed.
Questions before you change the stack
Assess the complete operation, not one endpoint.
Can this replace a behavioural data platform?
It can replace the covered collection, validation, enrichment, identity, policy and delivery jobs. We first map the sources, destinations and operating responsibilities before any component is retired.
What happens to our warehouse or lake?
It can remain a destination. The stream makes accepted data useful before the load, so operational services do not need to wait for another batch and reverse pipeline.
Can AI agents use the stream directly?
Approved agents can read current context through the stream API and remain bound by the same purpose, authority and permitted-action rules as other actors.
How do we compare costs?
Compare the complete workload: event volume, processing, state, data movement, destinations, availability and the people and provider services required to operate it.