Data scattered across channels
POS, online store, marketplaces, warehouse and spreadsheets never quite line up, so every report starts with a manual stitch.
Industries / Retail
Agentic BI for RetailYour sales data has a goal.It finds why margin slips.
The agentic BI platform for retail teams. Your AI analyst investigates margin, stock-outs and returns across your POS, e-commerce and inventory data. Every number cited, never guessed.
No credit card · 14-day team trial
Retail analysisstore_sales.csv
LiveQuestion
Why is sell-through down on outerwear this month?
Sell-through by category%
Root cause
Outerwear stock landed three weeks late in two regions, and 64% of its unsold units are sitting there.
60s
to connect your database and start asking
100%
of numbers cited to a query that ran
24/7
monitoring on the stores and products you put on watch
The retail data problem
Every sale is recorded. Almost nothing is explained.
Your POS, online store, inventory and loyalty systems each hold part of the story. Seeing that sales or margin moved is easy. Knowing why takes an analyst a week, by which time the season has moved on.
Stock-outs and overstock
Best sellers run out while slow lines pile up, and nobody sees it until the weekly report.
Margin eaten by discounts
Markdowns and promotions quietly erode margin, and it’s hard to tell which ones actually paid off.
Returns nobody can explain
Return rates climb on certain products, sizes or channels, and the cause stays buried in the data.
Customers you can’t see clearly
Repeat rate, churn and lifetime value are scattered across loyalty, email and checkout data.
Dashboards that go stale
Static exports are out of date the moment they’re emailed, and two teams rarely agree on the same KPI.
How it works
From connected to monitoring in two days
No SQL, no ticket to the data team. SchemaChat does the heavy lifting.
See it in action
Ask the questions merchandising and e-commerce teams actually ask
Four real-world questions, answered the way SchemaChat answers them: the number, the why, and a chart of what it found.
Ask
Why is gross margin down 1.8 points this quarter?
- Compared margin by category, channel and week
- Isolated seasonal lines and promotions
- Split the change into discount, cost and mix effects
SchemaChat
Gross margin fell from 41.2% to 39.4%. Markdowns on seasonal lines account for most of the drop.
Root cause
Winter lines were discounted 30% three weeks early, and sales didn’t rise enough to cover the lost margin.
Ask
Which best sellers keep running out of stock?
- Ranked products by sales velocity
- Counted days at zero stock by store and warehouse
- Estimated lost sales from each product’s normal daily demand
SchemaChat
38 of your top 200 products hit zero stock last month, costing an estimated $214k in lost sales.
Root cause
Reorder points were set on last year’s demand, so fast sellers run dry before the next delivery lands.
Ask
Why is the return rate up on online orders?
- Calculated return rate by product, size, channel and reason
- Compared each against its own 12-month baseline
- Read the return reasons and the sizes affected
SchemaChat
Online returns are 19.4%, up from 15.8%. Two product lines account for 58% of the increase.
Root cause
Sizes M and L in the new jacket line are returned three times as often, with “runs small” as the stated reason.
Ask
Which customers are least likely to come back?
- Grouped customers by first purchase, channel and discount used
- Tracked each group’s second purchase within 60 days
- Checked which follow-up messages each group received
SchemaChat
The 60-day repeat rate fell from 38% to 33%. Customers won with deep discount codes are the biggest drop.
Root cause
Customers acquired with deep-discount codes rarely return at full price, and they now make up 35% of new buyers.
What teams use it for
One analyst for every retail question
From the shop floor to the boardroom, the same governed numbers answer every team.
Gross margin · Sell-through · AOV
Sales & margin analytics
See what drives revenue and margin by category, store and channel, and where the margin goes.
Stock cover · Stock-outs · Turnover
Inventory & stock-outs
Balance stock across stores and warehouses so best sellers stay on the shelf and slow lines stop piling up.
Promo lift · Discount depth · Margin
Pricing & promotions
Learn which discounts lift sales enough to justify the margin they cost, before the next campaign.
Return rate · Reasons · Net sales
Returns & product quality
Trace returns to the product, size or supplier behind them, and fix the cause instead of the symptom.
Repeat rate · Lifetime value · Churn
Customer retention & loyalty
See who comes back, who doesn’t, and what makes the difference, from the very first order.
Demand · Forecast gap · Seasonality
Demand & seasonality
Understand seasonality and trends by product and store so you buy and stock ahead of demand.
Automated dashboards
Retail dashboards that build themselves
- Ask “build me a retail performance dashboard”. It picks the KPIs and charts.
- Live, not snapshots. Every widget re-queries on each view.
- Click any store on the map to cross-filter the whole board.
Try it: click a store on the map.
Build me a retail performance dashboard
Avg order value$58.20+1.4%
Gross margin39.4%−1.8pt
Sell-through71.2%−3.0pt
Return rate9.6%+0.7pt
Stores & channelsColour = sell-through
Sell-through by store
Gross margin, last 12 weeksNow 39.4% · was 41.4%
Integrations
Works with the data you already collect
SchemaChat reads the database your retail systems already feed. There’s no new tracking to install and no data to re-enter.
- POS & store sales
- E-commerce orders
- Inventory & warehouse
- Returns & refunds
- Customers & loyalty
- Promotions & pricing
- Suppliers & purchasing
- Spreadsheets & CSV
Your database read-only
SchemaChat reads from
- PostgreSQL
- MySQL
- BigQuery
- Snowflake
- MongoDB
- CSV files
schemachatAI data analyst
- Root-cause analysisInvestigating margin erosion
- Live dashboardsRetail performance · re-querying
- Alerts & monitoringSell-through < 60% · watching
- Cited answersEvery number traced to its source
Read-only, credentials encrypted.See all integrations →
FAQ
Questions retail teams ask
Which retail systems does SchemaChat work with?
SchemaChat connects to the database your systems write into: PostgreSQL, MySQL, BigQuery, Snowflake or MongoDB. If your POS, e-commerce or inventory data lands in one of those, or in a CSV export, you can start asking questions straight away.
Do merchandisers or analysts need SQL knowledge?
No. You ask in plain English. SchemaChat writes and runs the queries, and shows the exact SQL behind every answer, so your data team can check it any time.
Can it join store and online data?
Yes, if the tables are in the same connected database, SchemaChat can join them. For example it can link online orders to returns and to inventory by product, and define the join once as a governed metric.
How do I know the numbers are right?
Every figure comes from a query that actually ran, with the step behind it cited. If the data isn’t there to answer a question, SchemaChat says so rather than guessing.
Is customer data safe?
All AI queries are SELECT-only, enforced at the SQL parser layer, so SchemaChat cannot modify or delete your data. Database credentials are encrypted at rest. You also choose which tables and views it can see, so personal customer fields can be left out.
Can it help with forecasting?
It analyses your history, including seasonality, trends and what drove past peaks, so you can plan buying and stock with the patterns in front of you. It isn’t a replacement for a dedicated forecasting model.
Can it alert us when stock or margin goes off track?
Yes. Put any finding on watch, for example a store’s sell-through or a product’s stock cover, and you’ll be alerted when it crosses a threshold, along with a digest of what changed.
Can we pull answers into our own tools?
Yes. The REST API lets you ask questions, run read-only queries and pull charts from your own backends, Slack bots and automations using a Bearer token.
How much does it cost to try?
The 14-day team trial lets you connect your own database (read-only) or upload CSVs, run deep analyses and build dashboards, with no credit card and up to 3 seats. After the trial, Premium and custom plans for larger teams are on the pricing page.
Put an AI analyst on your retail data today
Connect a database or upload a CSV of recent sales and ask your first question in about a minute.
- 14-day team trial, no credit card
- No credit card
- Cancel anytime