Hire a retail analyst who works every morning.
Before you've had your coffee, your AI analyst has checked every store, SKU and category — and tells you what happened, why, what needs attention and what to do next.
Live demo: a 30-store chain with a year of data — no signup needed.
Your data knows what's wrong. Nobody has time to ask it.
Multi-store retailers sit on POS, inventory and marketing data that could explain every bad week. But dashboards only show what happened — someone still has to notice the problem, work out why, and decide what to do.
A store's slump shows up in the month-end review, three weeks after it started.
Revenue is down 8%. Is it one store, one category, fewer shoppers, smaller baskets or heavier discounting? The dashboard doesn't say.
A retail analyst who checks every store every morning costs more than most 20-store chains can justify.
Five questions, answered every morning
Not another wall of charts. A short, prioritised brief that reads like it came from your best analyst.
Revenue, margin, traffic and basket size for yesterday, the week and the month — each against the right comparison period.
Changes decomposed into stores, categories, transactions vs basket, discounting and stock availability.
The specific stores, SKUs and categories driving the change, ranked by rupee impact.
A concrete next step for every finding — replenish, transfer, review a discount rule, check staffing.
The metrics to watch so you know whether the action worked.
The issues that cost retailers money — caught automatically
Eleven statistical detectors run over your data after every sync. Each one compares like with like, ignores normal noise and ranks what it finds by rupee impact.
Revenue below its own weekday-adjusted baseline, after allowing for the chain's trend — so seasonality doesn't cry wolf.
Days of supply per SKU per store, revenue at risk, and which store has surplus to transfer.
Margin changes split into discount, category mix and rate effects, down to the store and category.
Stores growing revenue while margin falls — the promotions that buy sales at the expense of profit.
Sales per employee compared with peer stores, to flag rotas that don't match demand.
Categories accelerating or declining against the chain, and inventory that isn't moving.
Every number computed. Every finding explained.
Every insight has a “Why am I seeing this?” panel: the metric, the comparison, the period, the stores and products affected, and every step of the calculation. Your team can check the work before acting on it.
Revenue, margin, AOV and days of supply are calculated one way, everywhere in the product.
Weekday patterns, the chain trend and peer stores are accounted for before anything is flagged.
The insight engine is statistical and runs inside the platform; no outside AI service is needed.
Observed facts, likely explanations and recommendations are always shown separately.
When you want to dig deeper
Every finding links into full analysis pages built around the decisions you actually make.
Ranked drivers behind every store's performance, compared with the chain, its region and similar stores.
- 1.Watches−29.7%−₹4.11L
- 2.Footwear−21.7%−₹1.94L
- 3.Transactions−4.4%945 → 903
- 4.Inventory availability−4.0 pts96% of top 50 in stock
- 5.Discount rate+3.1 pts6.5% → 9.6%
Days of supply, stockout risk and revenue at risk for every SKU in every store — with transfer suggestions.
| SKU | Store | On hand | Days of supply | Risk | Revenue at risk |
|---|---|---|---|---|---|
| SKU-0182 | Store 22 | 4 | 1.6 days | Critical | ₹1.1L |
| SKU-0182 | Store 3 | 6 | 1.7 days | Critical | ₹1.6L |
| SKU-0264 | Store 22 | 0 | 0 days | Out of stock | ₹1.9L |
| SKU-0182 | Store 26 | 8 | 2.6 days | Critical | ₹1.3L |
| SKU-0042 | Store 17 | 0 | 0 days | Out of stock | ₹0.9L |
KPIs for today, yesterday, 7/30 days, MTD, QTD and YTD with trend lines.
Categories, regions, day-of-week patterns, fastest-growing and declining SKUs.
Daily brief, weekly management review and monthly business review — as PDF.
Owners see everything; regional and store managers see only their stores.
From POS export to morning brief
Load your stores, products, sales and inventory — by CSV today. The analyst builds summary tables, runs its detectors, and has your first brief ready as soon as sales data arrives.
- 1Connect your data
Upload your POS exports. Column names are mapped automatically; bad rows are reported with the reason.
- 2The analyst gets to work
Metrics are computed, detectors run and findings are ranked by financial impact.
- 3Read your brief and act
Open the brief each morning, check the evidence behind any finding, and share the PDF with your team.
Built for the people who run the stores
The three things that need your attention, every morning, across the whole chain.
Which of your stores are slipping, why, and how they compare with peers.
Your store's numbers, drivers and stock risks — nothing you can't act on.
Evidence, calculations and exportable reports instead of spreadsheet archaeology.
Questions retailers ask us
Is this another BI dashboard?+
No. Dashboards wait for you to find the problem. The analyst runs every morning, decides what matters, explains it with evidence and tells you what to do. Charts are there when you want to dig in, not as the starting point.
Where do the numbers come from?+
From one analytics engine with fixed metric definitions, calculated directly from your sales and inventory data. Nothing is estimated or generated, and every insight shows its calculation step by step.
What data do I need?+
Stores, products, line-level sales and inventory snapshots. Most POS systems can export these as CSV, and the import wizard maps your column names automatically. Marketing spend and staff lists are optional extras.
Is my data sent to an AI service?+
No. The insight engine is statistical and runs inside the platform, so your sales and inventory data doesn't need to go to any outside AI service.
Can a store manager see other stores?+
No. Regional managers see their region and store managers see their store — dashboards, insights and reports are scoped the same way.
How is my data kept separate from other retailers?+
Each organization's data is isolated inside the database with row-level security, so even a faulty query can't read another company's rows. See the Security page for details.
See what your analyst finds in your data.
Book a 30-minute walkthrough, or explore the live demo with a full year of sample data from a 30-store chain.