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Product

A Shopify AI agent that plans and executes store tasks

Describe the outcome you want, and Spark uses connected Shopify data and tools to plan and execute supported tasks. Use this AI assistant for ecommerce to turn store questions into scoped actions and reviewable results.

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Goal-driven AI agent · Autonomous planning & execution · Connected store data & tools

Today overview
Proof

Start with the strongest outcome signals

Context

Connected store signals

Bring Shopify, analytics, tracking, and channel data together so Spark can work with current store context.

Flow

Goal -> plan -> action

Spark starts from the outcome you want, scopes a supported path, and helps move the task forward.

Workspace

One task center

Keep issues, AI work, content jobs, and follow-up tasks closer together instead of bouncing across tools.

Workflow

How it works

Understand the product in the same order merchants actually use it.

  1. Connect your store signals and tools

    Bring Shopify, marketing, and tracking sources together so Spark has the current store context needed for review and execution.

    • Connect store, ad, analytics, and tracking systems
    • Pull performance, health, and issue signals into one workspace
    • Reduce the scattered context that slows daily decisions
    Connected context

  2. Describe a goal and let Spark plan

    Tell Spark what outcome you want and let it scope a supported path using the connected tools, permissions, and current store state.

    • Turn a question or goal into a clearer plan
    • Use AI guidance to understand what matters and why
    • Keep planning tied to the tools that can actually do the work
    Goal driven

  3. Execute supported tasks and review the result

    Move from insight to supported action without leaving the workspace, then inspect the result and decide the next step.

    • Run supported tasks inside the connected environment
    • Keep task records, AI work, and operations in one task center
    • Shorten the gap between seeing an issue and acting on it
    Action loop
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Use cases

Typical scenarios

Open product playbook
ScalingSpark: AI Store Assistant
Spark: AI Store Assistant
Use CaseScaling

Reallocate Paid Budget During Scaling Weeks

When traffic is moving and spend is rising, use Spark to spot low-efficiency ads faster, pause the obvious losers, and move budget toward the campaigns that are still producing acceptable ROAS.

Open use case
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Features

Core capabilities

AI store assistant

AI store assistant

Ask questions about store signals, business performance, and operations tasks.

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Control

Automation still works better when you keep the key decisions

01

Set the goal, scope, and permission boundary

Spark works inside the context and tool access you have already connected, so supported actions stay tied to real permissions.

02

Review what Spark is doing and why

Use AI guidance and task history to understand how Spark interpreted a store issue or goal before deciding the next move.

03

Keep human approval where it matters

Spark can help plan and execute supported work, but the workflow still lets operators check outcomes and continue with clear oversight.

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Deeper look

The value is not another dashboard. It is a shorter path from signal to action.

01

See the issue in operating context

Start with the current revenue, conversion, traffic, tracking, or content signal instead of opening five tools to piece the situation together.

02

Let Spark turn the goal into a path

Describe the outcome you want, and Spark can scope a supported route using connected tools, current data, and task context.

03

Keep execution and follow-up in one workspace

Move into supported tasks, inspect the result, and decide the next step without rebuilding the context from scratch.

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Compare

Why this product may fit better

Moves from signals to supported actions

Spark is not limited to showing store data. It can turn a goal into a plan, execute supported tasks, and return a reviewable result.

  • Bridges the gap between store insight and follow-through
  • Supports goal-driven workflows instead of dashboard-only review
  • Keeps task planning and execution closer to the original signal

Keeps daily store work in one workspace

Bring performance, issues, AI guidance, tasks, content, and tracking work into one place so teams switch tools less often.

  • Fewer context switches across analytics, tracking, and operations
  • Makes daily review easier for growing Shopify teams
  • Helps marketing, content, and operations stay closer together

Designed around merchant goals, not just reports

Start with the outcome you want and let Spark scope the work around connected tools, permissions, and the current store context.

  • Closer to how operators actually work day to day
  • More useful when the next step matters more than the raw metric
  • Turns a store question into a more actionable path forward
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Related resources

Related resources

Spark: AI Store Assistant frequently asked questions

What is Spark?

Spark is an AI agent for Shopify merchants. It uses connected store data and tools to plan and execute supported tasks from a goal you describe.

Is Spark just another analytics dashboard?

No. Spark can move from understanding store signals to planning and executing supported tasks. What it can complete depends on the connected tools and permissions for the task.

What data sources can Spark connect?

Spark supports Meta, Google, TikTok, GA4, Search Console, PageSpeed, and related data sources, plus Web Pixel and storefront tracking setup and validation.

Next step

Explore Spark: AI Store Assistant

A Shopify AI agent and ecommerce assistant that plans and executes supported store tasks using connected data and tools.

Install on ShopifyBrowse resources