What happened?
Sales, ad spend, stock, margin.
The signals that trigger work.Own Your Data · Closed-loop systems · ClearAds seller event
A session for ClearAds sellers · Real Team A + Team B examples
Founder · IdealSync
Dorian Gorski has spent thirteen years inside Amazon conversion work on a data-science foundation: three thousand optimizations across two thousand-plus brands. Founder of Keplo, the CRO platform for 8-figure Amazon brands.
At IdealSync he writes the architecture, runs the build, and signs the handover. The systems on this page are the ones he works in every day, not a deck.
ClearAds · Features in action
Team A supplies the learning and data examples. Team B supplies the task execution examples.
Ask business questions against the company database. Record failures and missing data. Improve the shared guidance.
13,692 recorded tool calls in four complete weeks. A real feedback signal for what to improve next.
Raise a task, apply a reusable flow, resolve owners and due dates, create the work, and track its lifecycle.
Completion returns to the ledger. The next process review can use evidence.
ClearAds · Features in action
10 August–6 September 2026 · four complete Europe/Berlin weeks · production only, smoke checks excluded.
Calls, not conversations
Plus one shared Slack identity
13,239 calls · not answer accuracy
12,186 database query calls
11,021 calls through the shared Slack door
73 calls to log a data request
146 no-data responses + 307 other problem responses expose gaps to investigate.
Tool categories and Slack usage overlap. A request call is not a unique feature request.
ClearAds · Features in action
The same pattern appears in the two systems you have just seen.
Business questions reach the company database. Tool receipts reveal which requests succeed, which fail, and what data is missing.
A person raises and reviews a request. A reusable flow creates the tasks, owners and dates. Changes feed back into the schedule.
Team A links outcomes to decisions and improves shared guidance. Team B records task lifecycle events for the next process review.
These are distinct existing systems demonstrating one philosophy. They are not presented as one combined implementation.
ClearAds · Features in action
A practical lens for any seller’s business, illustrated by mechanisms already built.
Can the team trace an answer to its data? When an answer fails, does the failure improve shared guidance?
Does a request become steps with owners and due dates? Does a changed deadline reach the next person?
Does completion return to tracking? Can the next colleague find the earlier decision and its recorded outcome?
ClearAds is the seller-event audience. Team A and Team B are the implementation examples; demo brands and people are fictional.
What this session is about
Automations your team actually runs — fed by both, sharpened every cycle.
Before we start · one promise
Everything you're about to see, you can build. At the end I'll share a free resource that helps you do exactly that — step by step, inside your Claude.
THE MCP · REVEALED ON THE LAST SLIDE
An assistant trained on this exact method — it guides, designs, and builds with you.
FREE · NO CATCH · SLIDE 38
Section 1 · The foundation
An automation is only as smart as the data underneath it. Crap in, crap out.
The pain · what almost everyone does first
The solution · the medallion model
Bronze is raw and untouched. Silver is cleaned and checked by code. Gold is verified numbers the AI is finally allowed to read.
Raw, exactly as it arrived. Untouched, kept forever.
Cleaned, deduped, checked. Every row passes code.
Verified numbers. The only shelf the AI can see.
AI reads. Python counts.
Accuracy
Each key number is computed from two independent sources. If they disagree, we flag it. If data is stale, we say so. Never a dressed-up number.
when they disagree
A mismatch becomes a finding you can act on, not a guess baked into a dashboard.
Security
I have watched shared Amazon credentials leak and cost real money. The fix: your team never touches the sources. A guarded database sits in between — they read it, and nothing else.
The pain · keys passed around
Everyone holds the key. One copy always slips.
The fix · one guarded door
No keys to expose. One truth — fresh, maintained, verified — for everyone.
What goes in
Business data tells you WHAT happened: sales, ads, ranks, keywords. Operational data tells you WHY: SOPs, decisions, tasks, people, results.
Business data · what happened
MOST BRANDS STOP HERESales, ads, ranks. Every brand tracks this.
Operational data · why it happened
Who decided what, and how it turned out. Almost nobody logs this.
Choose your sources
rented · many just re-read Amazon pages
owned · read into a database you keep
Plug in only the sources that drive real decisions. AI with eyes can read the rest into a database you own.
Where this is heading
Business data spots the problem. Operational data knows who fixes it, and how. Next time, the system already knows what worked.
Section 1 · done
Database structure is the most important decision in the whole journey. Get it right and everything after it gets easy.
Section 2 · SOPs
The hardest part of automation is not the tech. It is writing down how your business actually thinks.
Without a playbook · knowing
Dashboards full. Nothing moves.
With a playbook · doing
Same data, plus a playbook. A path to a finished task.
The pain
Written once, outdated in a month, opened by no one. So the real manual lives in people's heads, and every question still lands on you.
The documents · asleep
Written once. Read never.
The reality · one head
Every question still routes to the owner.
The living library
Best practices. Procedures plus edge cases and alerts. Who owns what. The KPIs that define success. And the unwritten rules your best people use.
Stage 1 · mine what exists
We plug into the tools you already use and read everything — automatically. Out come SOP drafts, the real org map, and the habits that actually make money.
Stage 2 · own the AI doorway
Your team gets company AI agents inside Slack and ClickUp. Every question is logged: who asked, what they needed, what was missing. The library learns where it is thin.
The pain · personal chatbots
Answers vanish. Data drifts into accounts you cannot see.
The fix · one company agent
One doorway. Every gap becomes the next SOP.
Stage 3 · interview the experts
We interview the people who carry the business and log what they know. It comes back out as tasks, recommendations, and guidance for everyone. No more "quick question, how do I…"
The payoff · priority math
When everything is measured, every task gets a price: time it takes vs money it moves. The system does not just create tasks. It ranks them.
Main image suppressed · Product X
Sessions dipping · Product Z
Section 2 · done
Business data and operational knowledge live in one database and point at each other: product, person, SOP, decision, result. That connection is what makes automation possible.
Section 3 · Automations
An automation is just data plus playbook, acting. You built both. This is where they start moving.
The map
In your chat. Inside your team tools. In the background. And on your screens. Each one has a different job.
Sporadic questions, answered from one brain.
They follow the work and step in with help.
Scheduled jobs that end in conclusions.
Screens built for your exact workflows.
Home 1 · company chat
A company chat assistant with read access to your verified data. Sales questions and process questions, one brain, sourced answers.
Home 2 · agents in your team tools
Agents live inside Slack and ClickUp, follow the conversation, and step in when they have something useful.
Sales on the garlic press dipped 30% this week. Anyone know why?
Home 3 · background workers
Scheduled jobs gather data, run the math with plain code, and wake you up with conclusions, not raw numbers.
Home 4 · your own screens
Dashboards and small apps built for your exact workflows. Not what a software vendor thinks you need.
Under the hood
n8n for the plumbing: built once, runs at scale, easy to fix. Paid AI for the heavy thinking. Your own GPUs for the 24/7 work.
Alerts, and beyond
An alert is the floor. The ceiling: the system investigates, plans the fix, drafts the email, and hands you a finished package to approve.
Section 4 · Feedback
Everything the system does leaves a trace. Now the traces go to work.
What gets measured
Each decision, each KPI, each SOP used, each task done, each alert fired. All logged, all connected by IDs.
The loop closes
The loop answers the questions that grow a business: what works, what to fix, what is missing, where the next opportunity hides.
The payoff · from part 1
First lap, humans decided and the system logged. Second lap, it already knows what worked, who did it best, and how long it took.
Second lap · recommendation, pre-filled
Same loop as an hour ago. Now you can see the learning.
Fair by design
The order is fixed: check the data, then the SOP, then coach the person — with facts. Reviews run on logged reality, not memory.
SOP-14 · two versions, measured
Why this compounds
Each cycle: richer data, sharper playbook, smarter automations. A rival can copy your photos tomorrow. Not your years of logged decisions.
Own Your Data · the whole journey
Free · for Claude
Your free Systems Blueprint: map one workflow, find its weak handoffs, and choose a practical next move.
idealsync.com/blueprintDorian’s method and a free MCP helper for Claude.
See roles, required inputs and the next improvement.
Invite one teammate to verify a selected handoff.
Discuss how to turn the plan into a working system.