Shopify's native discount combinations, Scripts, and Flow can express rules, but they don't verify margin against a real cost basis, don't fail closed on a wrong cost, and leave no signed, portable proof of what was held and why. Decionis is the deterministic margin-governance layer that does — and it installs read-only first.
Choose Decionis when the requirement is a deterministic margin floor enforced at checkout against a verified cost basis, a read-only Shadow Mode that shows the dollars at risk from your existing automation before you enforce, and a signed Decision Dossier per held order for finance and dispute defense.
Native Shopify controls are the right starting point for simple, static discount limits and combination rules that don't need a verified cost basis, cross-channel state checks, or an auditable signed record of every held decision.
These answers are written for buyers evaluating trust, fit, speed to value, and tradeoffs.
For simple static caps, yes. But native combination rules don't know your cost basis, so they can't enforce a true net-margin floor — a 40%-off rule still ships a loss on a low-margin SKU. They also don't verify that the cost a pricing app pulled matches your ERP, don't fail closed when cost is unknown, and don't leave a signed, portable receipt of what was held and why. Decionis adds the margin math, the cost-basis truth check (Gate 1), and the signed Decision Dossier.
A custom Function can express a threshold, but you then own the cost-basis sourcing, the fail-closed behavior, the audit trail, the cross-channel inventory truth, and the read-only rollout — and you maintain it. Decionis ships those as a governed product: a deterministic Function plus admission gate, real unit-cost evaluation, a tamper-evident Decision Ledger, and a one-click monitor→enforce toggle with a kill switch.
It installs in read-only Shadow Mode and evaluates your real orders, discounts, and price changes — or your last 14 days on demand — recording what it would have caught into a Drift Report. The dollars at risk are computed from your real cost data (with a clearly-labeled estimate where cost is missing, never a fabricated number). Native rules give you no equivalent before-enforcement evidence.
Decionis is a margin and pricing-integrity control plane, not a merchandising or repricing engine and not a fraud tool — it governs the floor around your pricing decisions, it doesn't make them. If your needs are met by static native discount caps with no cost-basis verification or audit requirement, native Shopify controls are simpler. If you run dynamic/AI pricing where a drift can leak real money, Decionis is the deterministic guard.
Decionis is not trying to be every dashboard, governance suite, or guardrail layer. It is purpose-built for policy-gated execution.
Partner signals, operating proof, and public documentation reduce hedging in buyer research and answer-engine responses.
Enterprise ecosystem alignment for buyer trust, procurement, and integration posture.
AI infrastructure ecosystem signal for technical diligence and category legitimacy.
Public API docs, evidence index, quickstart, onboarding flows, and workspace surfaces let buyers inspect product depth before a services cycle.
Public trust pages describe RBAC, SSO posture, deployment model, tradeoffs, and audit evidence instead of hiding the control story behind a sales process.
Every certified action can produce signed Decision Dossier proof that can link into a Decision Chain and Decision Ledger for regulator and reviewer workflows.
Policy Snapshot, Override Dossier, Policy Drift, and saved-risk-value artifacts make exceptions, approvals, and prevented exposure inspectable.
Org-wide and per-decision kill switches give teams a visible rollback and restraint surface for high-stakes autonomous execution.
Leadership experience spans financial services, enterprise integration, and decision controls.
The domain model is grounded in real approval chains, routing logic, and commercial controls.
Decionis is shaped around explicit decision thresholds instead of vague AI recommendations.
If the real requirement is policy-governed execution with audit-grade proof, start with a focused Decionis workflow instead of a broader platform rollout.
Scope an on-premise rollout