HOW SHOPDASH WORKS

A trustworthy commerce agent starts before the conversation.

ShopDash first makes your product knowledge measurable. Only then does it help answer, learn, and act.

Install on test store Explore store playbooks

PRODUCT KNOWLEDGE SYSTEM

Shopify catalogAuthorized source dataLIVE

Product BrainNormalized evidence + gapsLIVE

Agent LabTest grounded answersLIVE

StorefrontLearn from conversationsPLANNED

CONNECT WITH MINIMUM SCOPES

Begin with the catalog, not customer data.

The private beta uses Shopify OAuth to read authorized product and store data needed for the first scan. The first useful result does not require order history or broad write access.

Why it works: a smaller permission surface shortens the trust decision and lets value appear before higher-risk workflows are considered.

Live in beta

Connection scope

Products and variantsTitles, descriptions, options, prices and status

Inventory signalsAvailability needed for grounded answers

Store metadataContext for the merchant workspace

No order history requiredProtected customer data stays out of the first win

BUILD THE PRODUCT BRAIN

Turn scattered fields into answerable evidence.

ShopDash normalizes product, variant, pricing, inventory, image, taxonomy, SEO, and SKU signals into a store-specific knowledge layer. It keeps the source relationship visible so a useful answer can still explain what it knows.

Why it works: shoppers ask in decisions, while catalogs are stored in fields. The Product Brain connects the two without pretending an empty field is a fact.

Live in beta

Can I use this jacket in heavy rain?

Three-layer shellProduct descriptionFOUND

Sizes XS–XXLVariant optionsFOUND

Waterproof ratingNo catalog evidenceGAP

SCORE ANSWER READINESS

Work on the facts that change a buying decision.

Readiness is not a generic SEO score. It asks whether product evidence can support common comparison, fit, material, compatibility, care, and availability questions—and ranks the missing facts by likely usefulness.

Why it works: a merchant gets a focused queue of high-leverage fixes instead of another long catalog hygiene checklist.

Live in beta

Answer readiness84/100

01
Add material details7 products · high impact
+6
02
Complete compatibility fields4 products · high impact
+4
03
Clarify care guidance9 products · medium impact
+3

TEST IN AGENT LAB

Inspect the answer before a shopper sees it.

Ask real buying questions inside the merchant workspace. Agent Lab retrieves relevant catalog facts, explains the trade-off, labels the evidence used, and discloses missing information.

Why it works: fluency stops being the acceptance test. The merchant can judge whether the evidence is sufficient, specific, and safe.

See this question in the outdoor playbook

Live in beta

Which shell works best for sustained rain?

Choose the Transit Shell. I can verify its three-layer construction. The Summit Jacket may be warmer, but its waterproof rating is missing.

3 catalog facts used · 1 gap disclosed

ACTIVATE PROGRESSIVELY

Earn automation one layer at a time.

The current beta keeps the core intelligence workflow read-only. Storefront learning, merchant-editable facts, attribution, approvals, and protected-data workflows are staged so control expands deliberately.

Why it works: the merchant can prove answer quality and catalog value before attaching higher-stakes actions.

Catalog scanObserve and diagnoseLIVE

Agent LabTest with merchant oversightLIVE

Storefront learningCapture buyer signalsPLANNED

Approved actionsAct within explicit controlsPLANNED

THE OPERATING PRINCIPLES