Rules engine
Rules let you steer the AI agent without editing prompts by hand. They are the merchant-safe layer between your catalog and Zubby — boost a collection, bury a weak product, pin specific SKUs to the top of a carousel, pre-write answers to common objections, cap discounts, or nudge the AI's tone. Everything here lives under Dashboard → Rules and is scoped to a single store.
The Rules area has four tabs. The main Rules & Voice tab handles broad merchandising and voice controls; the Merchandising and Objections tabs are focused authoring surfaces for two high-value rule kinds; and Effectiveness reports how each rule is actually performing.
Rules are not the knowledge base
Rules & Voice (the main tab)
This is the general-purpose rule list at Rules. Each rule has a type, a target scope, an optional target value, a description, and a weight modifier. Click Add Rule to open the editor; first-time authors can start from a preset (push a collection, never recommend a product, favor a category, approved offer) which pre-fills sensible defaults.
Rule types
| Type | What it does |
|---|---|
| Boost | Promotes the target earlier in search and recommendation results. Weight above 1 lifts it (a preset uses 1.5–1.8x). |
| Exclude | Hides the target from search and recommendations entirely. Use for discontinued or out-of-strategy items. Weight 0. |
| Block Claim | Stops the AI from confirming a specific claim (for example a health or performance claim). When a shopper raises it, the AI refuses to confirm and defers to policy. |
| Tone | Applies a voice override the AI follows on every message — for example “always warm and concise, never pushy.” |
| Discount Limit | Controls what the AI may offer when a shopper asks for a deal. You attach an approved offer (type, label, code, minimum subtotal, notes); the weight becomes the maximum percent-off allowed. Without an approved offer, the AI refuses ad-hoc discounts. |
Target scope and value
Scope decides what the rule applies to: Global (all products, no target value needed), Collection, Product, or Category. For non-global scopes the editor suggests real entities from your synced catalog — click a suggestion chip so the target matches an actual product, collection, or category rather than a guessed ID. Product targets use the platform product ID; collections use the internal collection ID.
Weight modifier
For boost and exclude rules the weight is a multiplier: 1.0 is normal, above 1 promotes, below 1 de-prioritizes, and 0 excludes. For a discount-limit rule the same field means something different — it is the maximum percent-off the AI may ever quote for that offer.
Managing rules
Each rule card shows an inline IF/AI preview — what the shopper does and what the AI does in response — so you can sanity-check behavior before saving. You can toggle a rule on or off without deleting it (disabled rules dim and stop firing), edit it, or delete it. Select multiple rules with the checkboxes to enable, disable, or delete in bulk.
Merchandising — pre-merchandised carousels
The Rules → Merchandisingtab pins specific SKUs that should lead the AI's product carousel whenever a shopper's message contains your trigger phrase. Think “wedding gift” → your three hero registry products always show first. Fields:
- Trigger phrase— matched as a case-insensitive substring against the shopper's message (e.g.
wedding gift). - Internal label — a name for your own reference (optional).
- Pinned internal product IDs — the internal product UUIDs from the catalog page, comma- or whitespace-separated. Up to 6 pinned products show per turn.
- Priority — 0–100; the highest-priority matching rule wins when several match.
- Active and Notes — toggle off to disable without deleting; notes are internal only.
Pinned items still respect filters
Objections — pre-authored answers
The Rules → Objectionstab lets you write the exact answer the AI should reach for when a shopper raises a known objection like “too expensive” or “what if it doesn't fit?” Each objection response has:
- Category — one of price, fit, shipping, returns, quality, stock, comparison, trust, or general.
- Trigger phrase— case-insensitive substring match against the shopper's message (e.g.
too expensive). - Response template — the guidance the AI follows when it fires (e.g. lead with the value prop, mention the 30-day return policy, offer free shipping over $75).
- Priority — 0–100; higher wins. Up to 2 objection rules can fire per turn.
- Active and Notes — same toggle and internal-note behavior as merchandising rules.
Write templates as guidance, not a rigid script. The AI weaves the template into a natural reply for that specific shopper rather than pasting it verbatim, so it still reads as a conversation.
Effectiveness — is it working?
The Rules → Effectiveness tab estimates how each merchant-authored rule performed over the last 30 days. For every objection and merchandising rule it measures two things:
- Hits— conversations in the window whose shopper messages contained the rule's trigger phrase.
- Carts — how many of those conversations went on to produce an add-to-cart event, shown as a conversion rate.
Three summary cards at the top show active triggers, total trigger hits, and the portfolio cart rate. Rows are sorted by hits, then by conversion rate, so the rules pulling the most weight surface first. Rates above 5% are highlighted; a rule with many hits and a 0% rate is flagged as dead weight worth rewriting or retiring.
Effectiveness is a heuristic
How rules reach the agent
Matching rules are resolved per turn and injected into the conversation as constraints the agent must respect — boosts and excludes reweight retrieval, block-claim and tone rules shape wording, and discount limits cap what the AI may offer. This is the same mechanism the AI agent uses for knowledge and playbooks: merchant intent in, grounded behavior out.
Gotchas
- Trigger matching is plain case-insensitive substring matching, not fuzzy or semantic.
too expensivewill not catch “pricey” — add multiple rules or phrasings for full coverage. - Priority breaks ties; among rules that match, the highest priority wins (and a fixed number fire per turn — 6 pinned products, 2 objection responses).
- Disabling beats deleting when you are experimenting — a toggled-off rule keeps its history and shows up in Effectiveness so you can compare before/after.
- Discount rules are the only safe way to let the AI mention a code. Without an approved offer the agent refuses discounts, which is the intended default.
Related
- How the AI agent works
- Playbooks & agent-assist — for step-by-step in-chat procedures
- Knowledge base — for store facts and policies
- Conversations — where rule effects show up in real transcripts