Every flagged case arrives with its reasoning and a suggested next step.
Every decision is traced through a cause-and-effect graph, not produced as a number from a probability score.
Every decision, explanation, override, and outcome is preserved with full replay context.
Banks, insurers, hospitals, logistics, public-sector. Anywhere a decision has to be defended.
Model changes are proposed, reviewed, and promoted under explicit human control.
Every decision comes with the reasoning behind it. Not a number, but a chain of cause and effect your team can read, your customers can hear, and your regulator can audit.
For every flagged case, the platform suggests the most useful next action. Not 'investigate further', but a specific next step ranked with confidence.
Independent verifiers score every decision in parallel. If they disagree with our reasoning, we surface it instead of pretending to be sure.
Outbound shipment held. Inventory variance plus a vendor reliability dip on this lane.
BEFORE · Old system: 'Hold for review.'
WITH CAUSAL OS · Causal OS: most likely cause is a transfer-out that wasn't logged before the count window closed. Suggested action: confirm the transfer entry, then release if vendor SLA stays above 92%. Confidence 84%.
Small bakery. Borderline working-capital application. System recommends decline.
BEFORE · Old system: 'Decline. Risk score 0.42.'
WITH CAUSAL OS · Causal OS: declined because debt-to-income is 0.61. Smallest change that flips the outcome: DTI below 0.45 or eight more months of revenue history.
Elevated risk score with no breakdown.
BEFORE · Old system: a number on the chart.
WITH CAUSAL OS · Causal OS: top driver is HbA1c at 7.2, above target by 0.7. If brought to 6.5, estimated risk drops to 45%. First action: review medication adherence.
Want to see how one decision moves through the runtime? Follow the live decision flow from intake to explanation, verifier checks, and final outcome.
View how it worksFrom a raw SQL dump or spreadsheet to a live governed domain in your workspace, with your team approving every step.
SQL dump, spreadsheet, or live database. Point us at it. The Dataset Builder profiles your data, suggests the right grain, and proposes the signals that matter.
The platform drafts the causal graph. You review the candidates, choose the variant, and set the thresholds before anything goes live.
Once approved, your domain joins the live workspace on the same decisioning engine as every built-in domain.
Want to see that path more clearly? View the onboarding workflow page to watch how raw data becomes a live governed domain.
View onboarding workflowSign in to walk through the workspace, or read how the platform turns raw data into a governed decision domain.