What Is Qore? How Trellis' Codified-Workflow Layer Works

If you already keep Claude or ChatGPT open for account work, you have felt the ceiling. Ask the same question on Tuesday and Thursday and you get two slightly different answers. The good workflow lives in your head or a folder of prompts, so it only runs when you sit down and run it, one account at a time. And when it finally lands on a recommendation, you are still the one who has to go make the change. Your best judgment does not scale, and it does not stay put.

Qore is built for that gap. It is where you take a review you already do by hand and turn it into a workflow that runs itself: you describe the check once, the way you would brief a new analyst; Qore builds it into steps you can read and lock so it stops drifting; then it runs on your schedule, across every account you manage, and pauses for your approval before it changes anything. Its assistant, Q, helps you draw the workflow out and sharpen it as you go.

The shift is simple. Instead of remaking the same decision over and over, one account at a time, you make it once, write it down as something you can inspect, and let it travel to every place it applies.

Quick Answer

Qore is a workflow tool for ecommerce operators. You describe a review you do by hand, Qore turns it into a locked, inspectable workflow, and it runs on a schedule across all your accounts, proposing changes and, by default, waiting for your approval before it acts.

The difference from the chat window you use now: Qore is not advice you then go execute somewhere else. It holds your standard, does the repetitive reading for you, and can carry out the change itself once you approve it.

Who Qore Is For

Qore is built for the operator who has a standard in their head and no good way to make it run in more than one place at a time.

You know the pattern if you live it:

• You run more than one storefront. Amazon, Walmart, Google, Shopify, TikTok. Right now each is its own operation, with its own tabs, its own weekly ritual, and the same judgment applied by hand in each.
• You have a review you do every week that you could describe to a new hire in five minutes, but training that new hire and trusting them takes months.
• You have started pasting account data into a general-purpose AI tool, and it gives a reasonable answer, then a slightly different reasonable answer to the same question tomorrow.
• You are the bottleneck. The standard lives with you, so the work waits on you.

If none of that is true, if you run one account and enjoy the weekly review, Qore is solving a problem you do not have yet. That is a fine place to be. Qore is for the point where the same good decision needs to happen in twenty places and you are the only one who knows what it is.

How Qore Works

The mechanism is the point, so here it is in order.

1. Describe it once. You state the workflow in plain terms. What to look at, what counts as a problem, what to do about it. This is the same briefing you would give a capable analyst on their first day.

2. Assemble into visible logic. Qore turns that description into a sequence of steps you can read. Not a black box that returns an answer, a chain of logic you can open and check. If a step reads a metric, applies a threshold, or makes a semantic call about listing quality, you can see that step and what it decided.

3. Lock it. Once the logic matches your standard, you lock the workflow. Locking is what separates Qore from a prompt. A locked workflow does not drift, does not reinterpret itself, and returns the same output from the same inputs. It is a standard, not a suggestion.

4. Schedule it or point it at a roster. A locked workflow can run on a cadence you set, every morning, every Monday, or it can run across an account roster, applying the one standard to every account in the list at once. This is where build-time pays off. You encoded the decision once; it executes in as many places as you have.

5. Gate the actions on approval. When a workflow reaches a step that would change something in an account, it stops and asks. Actions default to manual approval. You review what Qore proposes, why it proposes it, and you approve or you do not. The reading and reasoning are automated. The commitment is yours.

That is the whole loop. Describe, assemble, lock, schedule, run across a roster, approve. Each stage is inspectable, which is the property that makes the rest of it trustworthy.

What Makes It Different From a General-Purpose AI Tool

A general-purpose AI assistant is genuinely useful. It can hold a conversation, recall a thread, and reason through a one-off question well. What it does not do reliably is hold your operating standard. Ask it the same question on Tuesday and Thursday and you can get two defensible answers that disagree at the edges. That variance is fine for brainstorming, but expensive for a standard you apply to real accounts every week.

So you build the box around it. Qore is that box. The encoded standard carries contextual and semantic judgment, not only numeric rules, and because the logic is locked and inspectable, the same inputs return the same output.

Here is the comparison in one view.

The row that matters most is the last one. A numeric rules engine is consistent but blind to nuance. A general-purpose LLM reads nuance but cannot repeat itself. Qore is the combination, a standard that reads context and returns the same answer twice. And it still has a limit, printed in the same row: it codifies a standard, it does not invent one. If you do not have a standard, Qore has nothing to encode.

What Qore Does Not Do

An honest definition includes the edges.

• Qore reads any account, but acts through Trellis. It can analyze and make recommendations no matter what runs your bids. To carry the change out itself, though, Trellis has to be the tool managing those bids. If another platform runs them, you still get the full analysis and the recommendation; Qore just hands it to you to apply rather than pushing it live.
• You bring the judgment; Q helps you shape it. Qore runs on your operating standard, and Q, its assistant, helps you draw that standard out, interviewing you about the review you do, turning it into a workflow, and suggesting checks you would want. What it will not do is decide your strategy for you: what "good" looks like for your catalog is still your call.
• You decide how much runs on its own. By default, Qore proposes a change and waits for your approval before anything moves in an account. Once you trust a skill, you can let it auto-execute. How autonomous each workflow runs is your choice, skill by skill; manual approval is the safe starting point, not a ceiling.

A Concrete Example

Abstractions get slippery, so here is one workflow, start to finish.

• Input: A roster of twelve Amazon accounts. For each, the last 30 days of search-term data, listing content, and campaign structure.
• Workflow (described once, locked): For every account, find search terms spending above a set threshold with no attributed sales in the window. Before flagging them, read the listing the term points to. If the listing is missing the attribute the shopper is searching for, mark it as a content gap rather than a bid problem. If the listing does cover the attribute, propose adding the term as a negative.
• Expected output: A per-account list split into two buckets. Bucket one, wasted-spend terms with a proposed negative keyword action, held for approval. Bucket two, terms that reveal a content gap, flagged for the listing team with the specific missing attribute named.
• Business decision: You open the roster once. You approve the negatives that look right, skip the ones you know are seasonal, and hand the content-gap list to whoever owns listings. Twelve accounts reviewed to your standard in the time it used to take to do one, and the semantic call, bid problem versus content gap, was made the same way in all twelve.

The semantic step is the part a numeric rule cannot do and a drifting assistant cannot repeat. That is the space Qore is built for.

How Qore Relates to Qinetix and AMC

Qore is one layer of a stack, and it helps to see where it sits.

• Trellis is the platform.
• Qore is the codified-workflow layer. It holds the judgment and decides what should happen.
• Qinetix is the execution layer beneath it. Ads automation, dynamic pricing, and DSP. It is what carries out the actions.
• AMC is an optional advanced capability for deeper Amazon Marketing Cloud analysis.

The clean way to hold it: Qore decides, Qinetix executes, AMC widens the evidence for deeper Amazon Marketing Cloud analysis. Qore proposes an action; when you approve it, Qinetix is the hands that do the work.

Conclusion

Qore is not a new kind of intelligence. It is a place to put the intelligence you already have. You make a decision once, in logic you can read, lock it so it stops drifting, and let it run on a schedule or across every account you manage, with the actual changes held for your approval.

The shift is build-time versus run-time. Stop re-making the same call once per account per week. Make it once, encode it, and let it travel. The judgment stays yours. The repetition stops being your job. Qore moved to a public waitlist in July 2026, out of private beta, and actions still default to manual approval, so you can encode a standard without handing over the final say.