guardrail-sim

E-commerce discount logic is deceptively complex. Every rule has exceptions, every exception has edge cases.

In my work on Shopify Plus stores, I've seen teams accidentally stack discounts that cost thousands, or block legitimate promotions because the rules conflicted. The business rules are scattered across code, configs, and tribal knowledge.

Testing these scenarios manually is tedious and error-prone. You can't possibly check every combination of discounts, customer types, and product categories.

guardrail-sim lets you define policies declaratively and simulate outcomes before they hit production. Think of it as unit tests for your business rules.

Instead of encoding rules in imperative code, you define constraints as data: "max discount 40%", "clearance items excluded", "employee discount doesn't stack". Then you simulate scenarios against these policies.

The simulation engine evaluates constraints in milliseconds, telling you exactly what's allowed, what's blocked, and why.

• Discount stacking.Can a 20% coupon combine with a site-wide sale? guardrail-sim evaluates the scenario and returns whether it's allowed, clamped, or rejected.
• Category exclusions. Are clearance items eligible for this promo? Define the constraint once, test any scenario.
• Time windows. Is this Black Friday deal still active? Simulate against the current timestamp.

• Declarative > Imperative for rules. When business rules change frequently, having them as data makes updates safer.
• Simulation builds confidence.Being able to test "what if" scenarios without touching production is invaluable.
• Domain expertise has value. Understanding e-commerce discount patterns from real experience shaped the constraint types.