Your business defines the operation.
Datastreams keeps it running.
Make your agreements executable in one independent runtime. Define how source data and current business context become events, records and actions, how backend results update state, and which collection, sharing and retention rules apply. Every permitted service receives its own API or outcome without rebuilding that logic.
The running definition remains inspectable by the business, testable before release and auditable after every relevant event.
Deterministic by design
Business context changes. Your rules remain explicit.
A decision can change when the facts, context or approved rules change. You can inspect which inputs and rule version produced the result, and test the conditions before approving a change.
Understand
Connect source data with the actor, time, purpose, current state and destination requirements that give it business meaning.
Decide consistently
The same input, context and approved rule version produce the same rule-based result. AI suggestions pass through these rules before an action is allowed.
Stay accountable
Define who may act, when approval is needed and which evidence explains the outcome. Test changes before they go live.
Separate questions, complete answers
Go deeper without repeating the complete platform story.
The operating model stays the same. Continue with the business case or with the APIs and interfaces that use the controlled operation.
Value and operating cost
Measure integration effort, coordination, software, runtime responsibility and evidence preparation around one real process.
Explore the business caseAPIs and tailored interfaces
Let web, mobile, portals and agents use the permitted capabilities of a stream without copying its rules into every front end.
Explore APPSSAI-supported authoring
Make engineering simple enough for AI to assist, without handing AI control.
AI can translate an approved business description into a configuration proposal because the runtime model is explicit. The proposal remains readable, comparable, testable and subject to the same identity and approval gates as a human change.
- 01
Describe
A person or approved AI assistant describes the source, business meaning, context, rules and required outcome.
- 02
Model
The declarative model language turns that intent into an explicit, versioned configuration proposal.
- 03
Test
Contracts, references, permissions and representative events are validated before the live operation changes.
- 04
Approve
The responsible person or policy gate reviews the diff, processing purpose, legal basis and permitted actions.
- 05
Publish
Only an approved version is activated on the runtime, with its configuration and authority evidence retained.
AI can accelerate the change; it cannot approve itself.
An approved assistant can describe, generate, explain, validate and test a proposed change. Identity, policy, review and accountable approval still determine whether a version becomes active.
One runtime, many operations
Hundreds of recipes can remain independently understandable.
A runtime can host many separately versioned stream configurations, subject to the agreed workload and capacity. Each recipe uses the same model language and control lifecycle without becoming a separate pipeline estate.
Purpose and legal basis
See which declared processing purpose and legal basis apply to a personal-data operation. The accountable organisation supplies and approves that basis; AI does not invent it.
Processing behaviour
Inspect accepted, rejected and recovered events, processing time, active state, retries and downstream delivery per configuration.
Decision evidence
Trace the active version, input context, policy result, actor or agent authority, action and resulting destination.
recipe → active version → processing purpose → legal basis → policy result → action → evidence
This makes the operation auditable while it runs, rather than requiring teams to reconstruct its meaning from source code, cloud consoles and logs afterwards.
How it works
Explore each capability without losing the complete operation.
Every capability returns to the same question: what should happen to this data now, under which rules, and what evidence must remain?
AI Data Operations
Let approved coding agents use live context through MCP or the Datastreams CLI
Open pageStart with the business result, then verify exactly how it remains under control.
Bring the question, connected sources and required outcome. Datastreams makes the processing rules, permissions and evidence visible before the operation goes live.
Explore Streaming Intelligence