Run everything you never had time to implement. Define your process once, and keep it

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PLATFORM

Q: Your eCommerce-Aware LLM

Q is the assistant you ask inside Qore. It arrives knowing eCommerce, reads your live account data, and remembers your business between sessions. There is no onboarding ritual and nothing to re-explain.

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Open beta. Built on Claude via MCP.
What’s different
Built for eCommerce.
Wired into your business.
Time comes back
The Monday reporting, the routine checks, the digging: Q handles the pulling and assembling, so your hours go to decisions instead of preparation.
Granularity that pays
Answers land at the level performance lives: the keyword, the ASIN, the placement. Not a summary you still have to investigate.
Builds on your work
Q remembers your targets, your terms, and what changed last week, so every answer builds on the last one instead of starting over.
No stack to build
The connections, the data foundation, and the guardrails come included. You bring the accounts and the questions.
The distinction that matters
Ask a general assistant to raise a
bid and it raises the bid.
Ask a general assistant to raise a bid and it raises the bid.
It has no idea what the bid was, whether you touched it on Monday, or whether
four clicks is enough to know anything. Fluency is not the same as knowing the account.
It has no idea what the bid was, whether you touched it on Monday, or whether four clicks is enough to know anything. Fluency is not the same as knowing the account.
A general assistant
Reads the sentence
  • Does exactly what you said, at the size you said it
  • No memory of what it changed last week
  • Treats 4 clicks and 4,000 clicks as equally conclusive
  • Averages the ACoS column
  • Agrees with your premise, because agreeing reads as helpful
Q
Reads the account
  • Sizes the change against where the number sits now
  • Knows what it changed, and when, and waits out the window
  • Has a data floor before it will call anything a loser
  • Recomputes ratios from totals, every time
  • Tells you when your premise is wrong, or when there is nothing to do

How it Works

Integrate

Plug in the accounts and tools you already run: Amazon, Walmart, Shopify, Google, TikTok, and more. Everything lands in one place for Q to work from.

Reason

Q weighs your live data against what it already knows: your targets, your terms, and what changed last week. It sizes the answer to the account, not to the question.

Act

Q returns the number and the why, and stages any change for your approval. Nothing reaches the account until you say so.

Integrate

Plug in the accounts and tools you already run: Amazon, Walmart, Shopify, Google, TikTok, and more. Everything lands in one place for Q to work from.

Reason

Q weighs your live data against what it already knows: your targets, your terms, and what changed last week. It sizes the answer to the account, not to the question.

Act

Q returns the number and the why, and stages any change for your approval. Nothing reaches the account until you say so.

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Worked example
“Raise the bid on organic protein powder to $12.”
Here is what happens between asking and anything reaching your account.
You
Raise the bid on “organic protein powder” to $12.
1
Current bid is $1.40. That is a 757% increase.
Above the sanity limit for a single change, so it will not stage silently. You get a warning first, and the full $12 needs a deliberate confirmation.
2
You raised this bid three days ago, $1.10 to $1.40.
Inside the learning window. Change it again now and neither move can be attributed. You will have spent money to learn nothing.
3
Headroom does exist: 30-day ACoS is 18% against a 25% target.
So the instinct is right, the size is not.
4
Top-of-search estimate for this term sits at $2.10.
Beyond that you are bidding past the placement you were trying to win.
5
Inventory covers 34 days at current velocity.
Safe to push. If it were nine days, Q would have said so before anything else.
Standard equipment
Built in, not bolted on.
Q's advantages ship with it. Nothing to configure, prompt, or maintain.
Memory
Q keeps a working memory of your business: targets, brand terms, naming conventions, how you like answers formatted. Correct it once and it stays corrected.
Integrations
Amazon, Walmart, Shopify, Google, and TikTok, plus research tools like Keepa and Jungle Scout. Bring your own MCP connections and Q reads those too.
Delivery
Answers land where you already work: Slack, email, spreadsheets, or a client-ready deck. Ask once, put it on a schedule, and it keeps arriving.
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What it will not do
The refusals are the feature.
Every operator who has been burned by a general model was burned by confident
output, not by a refusal. So Q is built to decline, and to say why.
Every operator who has been burned by a general model was burned by confident output, not by a refusal. So Q is built to decline, and to say why.
NO
Act on its own.
Q reads and proposes. Only an approved action writes to your account, and there is no setting that removes the gate.
NO
Agree with a wrong premise.
Ask why sales dropped in a week sales rose and it will say so, rather than producing five reasons for a thing that did not happen.
NO
Answer past its data.
If the window is partially attributed or the connector is stale, it tells you that before it tells you a number.
NO
Manufacture work.
“Nothing to do this week” is a valid answer and it will give it.
NO
Pretend the marketplaces are the same.
It applies the rules of the channel in front of it, and says when a capability is Amazon-only.
Use Cases
The reviews, audits, and reports operators run with Q every week.
Find growth
Find new sales streams
Your next stream of sales is already in your data. This shows where market share and repeat are climbing, so you double down before it is obvious.
Ads
3 skills
Amazon
Walmart
Find growth
Stop leaving margin on the table
Some products would sell just as well at a higher price. This finds them, and how far you can raise.
Pricing
3 skills
Amazon
Walmart
Cut waste
Find wasted spend
Some of your spend goes to targets that never convert. You find it when you have an afternoon, which is not most afternoons.
Ads
3 skills
Amazon
Walmart
Related Surfaces
Skills
Builder
Skills Builder
Turn a Q conversation into a workflow that runs on a schedule.
See Surface>
Dashboard 
& Reporting
Dashboard 
& Reporting
Dashboards and client-ready decks, built by asking for them.
See Surface>
AMC
Clean Room
AMC Clean Room
Query AMC in plain English. No SQL required.
See Surface>
Meetings
& Tasks
Meetings & Tasks
Meeting context and follow-ups, tied to the accounts they concern.
See Surface>

500+

brands in
open beta

brands in open beta

$900M

in GMV
under execution

in GMV under execution

100B+

tokens

processed

tokens processed

80%

of the weekly workload
Qore takes on

of the weekly workload Qore takes on

Frequently Asked Questions

You can watch a skill run before you sign up at all, and Starter is month to month — which is the answer for most people who want to tinker first.

No. Month to month.

No. You describe the work once, Q encodes it, and it runs on its schedule. The four-hundredth run is identical to the first.

Not necessarily. One, the other, or both.

Not on their own. A general model starts every session from zero on your catalog, your targets, and your history. Q starts from your connected accounts and a memory of your business, so nothing needs re-explaining.

It can carry the repetitive layer: monitoring, search-term reviews, staged bid and budget changes. In Q, that work runs as skills, and every action waits for your approval. Strategy stays yours.

Connect your accounts, describe the report once, and put it on a schedule. Q builds the dashboard or deck and delivers it to Slack, email, or a spreadsheet on cadence.

Amazon (Ads, Seller Central, AMC, Marketing Stream), Walmart, Shopify, Google, and TikTok, plus research tools like Keepa and Jungle Scout. You can also bring your own MCP connections.

Start on Starter.
Move when the schedule fills up.

Most teams outgrow a tier because they scheduled more, which is the honest reason to pay more.
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