The same pattern, by role and by industry: the graph your data hides is rebuilt, the outcome is predicted, and a governed agent takes the action. Everything below is illustrative, run on synthetic data, so we can show the mechanics without anyone's real records.
Flat transaction tables hide the ring. The graph does not.
| Ring | Accounts | Agent action |
|---|---|---|
| RING-204 | 7 | Quarantined |
| RING-118 | 4 | Quarantined |
| RING-377 | 3 | Routed to review |
| ACC-90142 | 1 | Cleared |
agent quarantined 7 accounts · 02:11:54Z · policy mule-v2 · hash 3c9d…a7 · reversible · funds held pending review
How the agent loop shows up for the people who answer for the outcome. Illustrative personas, not named customers. See the full solutions by role →
"I don't need another alert queue. I need the ring stopped before payout."
Seven scattered alerts become one quarantined ring, before the money leaves.
"By the time a churn report lands, the customer is already gone."
The play runs while the customer is still here, not in next month's campaign.
"I won't let software act unless I can explain and reverse every move."
Autonomy you can take to a regulator, not a dashboard you have to trust.
"No more brittle feature pipelines, and nothing leaves our environment."
One platform on your data, instead of a pipeline per use case.
"Detection is useless if the harm is already live."
Coordinated abuse is actioned in the moment, not left in a backlog.
"Every decline has to be explainable and fair."
A decision with its reasoning attached, and an audit trail for every one.
Sense the engagement and social graph. Predict a subscriber about to leave. Act: the agent launches the matched retention offer before the porting request lands.
Sense device and login relationships. Predict a takeover in progress. Act: the agent steps up authentication and holds high-risk transfers, with a human notified.
Sense the demand and supply graph. Predict a stockout by region. Act: the agent places the replenishment order inside the buyer's guardrails.
Sense the product usage graph. Predict an account ready to expand. Act: the agent opens the play in the CRM and alerts the owner with the evidence.
Sense claimant and provider links. Predict a staged-claim ring. Act: the agent holds the claims and opens a single investigation across the ring.
Sense the longitudinal care graph. Predict a high readmission risk. Act: the agent prompts the follow-up outreach and routes to the care team.
Scenarios are illustrative and run on synthetic data. Reproducible benchmark results are reported separately on the research page, and we never relabel synthetic results as client outcomes.
Bring a labelled table or use a bundled sample. We will surface the graph, predict, and run a governed action end to end, with the full audit in front of you.
We will reach out within one business day.