Seller platform · Decision tree · Continuous learning
A decision center for sellers: not “what happened” but “why, and what to do”.
A full platform for marketplace sellers of any size: inventory, daily sales tracking, finance, new product discovery and a fact-backed decision tree. The system spots a deviation, walks it through a decision tree into causes with their impact on margin, suggests an action, creates a task and checks the effect after 7 days. Every verified decision feeds a knowledge base, so the system learns what works for this particular seller. All in one window instead of a dozen spreadsheets and tools.
Analytics services show numbers without causes, data sits in a dozen spreadsheets, and diagnosing a sales drop takes days
A platform for marketplace sellers breaks a deviation down into causes in money terms, suggests an action and sets a task
Causes of a sales drop in money terms by morning instead of days of exports; every decision is checked after 7 days and the system learns from it
By the numbers
Problem
Why sellers spend days on what should take minutes.
- Tools show numbers, not causes. Sales dropped — but was it a competitor’s price cut, a best-selling size out of stock or pricier ads? Someone has to dig it out by hand.
- Data is scattered across a dozen places. Daily sales tracker, restocking, seasonality and the financial model live in spreadsheets, market analytics in a separate tool, tasks in a CRM.
- Investigating a drop takes days. While exports are pulled and spreadsheets reconciled, money keeps leaking.
- Nobody checks the decisions. Raised a bid, cut a price — did it work? A month later nobody remembers.
- AI advice without evidence. A model confidently explains the drop, but the numbers in its answer don’t match reality.
How it works
Five steps from a deviation to a verified decision.
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Collects data overnight
Sales, stock, ads, competitor prices and finance — from marketplace and analytics APIs using the seller’s own keys.
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Spots the deviation
A forecast for every product is trained on this seller’s own data. Anything that breaks from the forecast goes into an investigation.
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Walks the decision tree
Margin is broken down into price, marketplace discount, commission, logistics, ads, returns and cost; sales into the funnel from impressions to purchase. Code checks the causal patterns, AI picks the ones that fired and explains them in plain language. Every conclusion is backed by facts: numbers from the data and a link to the right rows of the sales tracker.
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Suggests an action and creates a task
Change a bid or a price, restock or reorder. Once a person approves, the task goes to the owner in Telegram or the CRM.
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Checks the effect after 7 days and learns
The result is compared with the forecast. A person marks the analysis “right / partly right / wrong” and the decision goes into the knowledge base. Wrong analyses trigger a review of the patterns; verified ones become training data for fine-tuning the model.
Features
What the decision center includes.
A decision tree backed by facts
From a margin loss to the exact cause in three clicks. Every node is backed by facts: a margin waterfall, numbers from the data and a link to the tracker rows. The library of causal patterns grows with every verified analysis.
A system that always learns
Every decision is checked against the forecast after 7 days. The knowledge base builds up what works for this seller, and the forecast trains on their data from day one.
Code does the math, not the model
AI runs the investigation and writes the explanation, but every number in the answer is checked against a calculation. A made-up figure won’t get through.
Every function in one window
Inventory and stock, daily sales tracker, finance, new product discovery, market and competitors, seasonality, the decision tree, tasks and stand-ups. One dataset, no exports between spreadsheets.
Inventory and sales tracker
Products, stock and a daily sales tracker. From any link in the cause chain, jump to the right row and days.
Stock, restocking and purchasing
A traffic light by size and warehouse, seasonal purchasing and a funnel plan on the same data.
Finance in five tabs
P&L for 12 months, a financial plan with scenarios, working capital, cash flow with loans, unit economics per product.
New product discovery
Niche and product research on the same market data: what is growing, where competition is thin and how it fits your unit economics. The launch decision stays with a person.
What changes
The usual way vs the decision center.
| Task | The usual way | In the decision center |
|---|---|---|
| Investigating a sales drop | days of manual work with exports | a quantified cause chain by morning |
| Where to look at data | a dozen spreadsheets and tools | 12 sections in one window |
| AI explanations | numbers unchecked | every figure verified by code |
| Decisions made | forgotten | effect checked after 7 days |
| Team experience | in people’s heads | a knowledge base that learns |
The platform runs on live seller data: inventory, daily sales tracking, finance, new product discovery and the decision tree — in one window. The finance block reconciles exactly with a real seller’s financial model.
Who it’s for
Who the decision center is for.
For sellers of any size — from a single store to a brand with several teams. For anyone who wants inventory, finance and decisions in one place and to understand why the numbers changed.
What makes it different
Why this isn’t just another analytics tool.
A full cycle in one window
Inventory, analytics, cause, decision, task and effect check usually take several tools and spreadsheets. Here it is all together.
Learns from your decisions
Not generic advice from the internet, but a record of what worked in your own account, with a human verdict.
One window, one dataset
Analysis, sales tracker, stock, finance and tasks are linked. No exports between spreadsheets.
Want a decision center for your store?
Free review: we’ll show where your data already points to lost margin and which decisions are worth testing first.
Quiz · 6 questions · 1 minute
Or right away: Telegram · @one_lab_support → · [email protected]