A data change. A clear next step.

Signal Flows. Investigate changes in your data, bring a proposed response to review, and follow it through to a confirmed result.

Design a Signal Flow

42-second product walkthrough with synthetic Orders data.

Signal Flows: from a schema change to an approved proposal. Pascal product walkthrough. — Product walkthrough
Read the transcript and visual descriptions

Signal Flows. Six starting points for the signals your team handles.

Schema Drift Repair

A source schema changes. Investigate the affected relationship and bring a cited mapping proposal to review. Saving the proposal leaves the live mapping unchanged.

Quality Anomaly Investigator

A quality check flags an anomaly. Gather the supporting evidence and return a cited diagnosis for the team, without changing data.

Materialization Triage

A refresh fails. Investigate the failure and propose a reviewed dry run or live materialization run. Keep the confirmed run attached to the investigation.

Object Set Watcher

Records enter or leave an object set. Investigate the change and propose running an allowed Workflow, with its scope and review requirements attached.

Model Monitor Analyst

A model monitor raises a signal. Inspect the retained production evidence and propose an Evaluation run to investigate the behavior.

Destination Sync Guard

A destination sync fails. Investigate the failure and prepare a writeback proposal for review. Sending the change to the external system remains a separate step.

Explore Pascal

Follow one signal all the way through.

A field rename puts a customer mapping at risk. Follow the evidence into review and the confirmation that an approved proposal was saved.

Orders schema guard watches the source, brings the schema and customer mapping into the investigation, and requires a data operator to review any proposal. Version 3 is confirmed live in this example.

Set the response before the signal arrives. Native product view with illustrative data.

One signal, one connected response

A field changes. The customer relationship needs attention.

An alert tells you something changed. Signal Flows brings the source, investigation, and proposed response into the same run, so the team can decide with the evidence in view.

In this Orders example, a data operator approves a mapping proposal. The run ends when the mapping service confirms it saved that proposal.

Explore people and agents at work

Source event · Orders

The source field was renamed.

Beforecustomer_id

Aftercustomer_reference

Investigate with source evidence

Proposed response · Human review

Update the customer mapping.

Point the customer relationship at customer_reference.

3 source references2 checks

Approved proposal saved

The live mapping and source records stay unchanged. Applying the proposal is a separate action.

Synthetic example

Signal Flows in your operation.

Connect it to the rest of your work.

Bring source events and agent investigations into one record. Use the findings to propose a mapping change, refresh a dataset, start a Workflow, evaluate a model, or prepare a destination update.

What you work with

Signal Flows

Source streams, object sets, materializations, model monitors, and destinations
Saved source evidence, released agent deployments, allowed actions, and review policies
Immutable flow versions, run histories, review decisions, and owner confirmations

Stay in control as the work moves.

Choose the source, agent, allowed actions, and reviewers. Test a draft without live changes, then publish and activate it. Each run keeps its evidence and decisions, including any result that still needs confirmation.

Where you work