A GM for every store that never sleeps.
askotter watches every store for you. It spots stockouts before shelves go empty, catches waste before it adds up, and shows every district manager how their locations are doing right now. It learns each store's patterns and gets smarter over time. AI gives the suggestions. Your managers make the calls.
anomaly detection time
health scoring
per-store monitoring
The loop, running on your site.
Same machine in every vertical: the site goes up, real behavior gets read, the weak spot gets fixed with a human sign-off, and the numbers follow.
What askotter catches at store level.
Thursday, 3:47 PM: holiday weekend incoming
Store #247 is about to run out of milk. Here is what happens:
What changes with askotter.
Built for every role in the chain.
Every role has different data needs. askotter shows the right information to the right person. No per-seat fees. No role-gating.
Store Manager Store Manager
Runs one location every day. Handles staffing, inventory, local promos, and daily sales targets. First person to feel the pain when items run out or shrinkage goes up.
Checks POS in one system, inventory in another, staffing in a third. By the time they spot a problem, the damage is already done. There is no single screen that answers "how is my store doing right now?" Just tabs and spreadsheets.
District / Regional Manager District / Regional Manager
Watches over 10-30 stores. Needs to compare locations, find the struggling ones early, and spread what the best stores do to the rest.
Gets weekly Excel files from store managers. By the time the data shows up, it is old. Cannot answer "which of my 20 stores needs help today?" without calling each one. Best practices live in someone's head, not in a system.
VP of Operations / COO VP of Operations / COO
Owns the P&L across all locations. Responsible for supply chain, shrinkage, and promo ROI at scale. Reports to the CEO on how the whole chain is doing.
The quarterly P&L shows that 15% of locations lose money, but the data is 90 days old. Promo success is measured by foot traffic, not actual margin. Supply chain choices are based on averages that hide what is really happening at each store.
Merchandising / Category Manager Merchandising / Category Manager
Decides what goes on shelves, works with vendors, and plans promotions. Needs to know which products move where and which promos actually help margins.
Promo data is spread across POS, inventory, and marketing. A BOGO deal might bring people in but kill margins on items that would have sold at full price. Nobody connects those numbers until the review meeting.
What retail operators use today and where it falls apart.
Most teams are not missing data. They are missing connections between their data. Here is why the tools you already have cannot close the gap on their own.
✕ NOT ENOUGH POS system reporting (Square / Toast / Lightspeed)
✕ NOT ENOUGH Inventory management tools (TradeGlobal / NetSuite / Fishbowl)
✕ NOT ENOUGH Spreadsheet rollups (Excel / Google Sheets)
✕ NOT ENOUGH Enterprise BI (Tableau / Power BI / Looker)
askotter does not replace your tools. It connects them. Every data source feeds one lake. Agents watch 24/7, spot changes, find root causes, and give you suggestions. Your team reviews every suggestion before it becomes an action. The tools you already have become much more useful when they can talk to each other.
What store operators deal with every day.
Stockouts cost more than you think
Waste adds up fast
You only see store profit once a quarter
Promotions eat into your margins
Per-store metrics that move margin.
From 10 stores to 1,000. Train once, scale everywhere.
Connect every store
POS, inventory, supply chain, staffing, and local marketing all flow into one place. Each store gets its own data lake.
Deploy per-store agents
Every location gets its own AI agent. It watches sales speed, inventory, waste, staffing, and promo results on its own. It learns what normal looks like for that store, that region, and that season. When things change, agents adjust automatically.
Catch problems early
Store #247: milk drops below a 2-day supply on Thursday before a holiday weekend. Reorder triggered. Store #412: produce waste jumped 40% after a cooler temp drifted. Maintenance sent before $8K in spoilage.
Show the right data to the right person
Store managers see their location. District managers see their 15-20 stores ranked by health. Regional VPs see the big picture. Team members save insights and share them across districts.
Ask across every store at once. Save what you learn.
Ready for a GM that watches every store 24/7?
We will connect your POS, inventory, and supply chain data. Your team stays in control. AI shows what matters. Your managers decide what to do.
Talk to Our Team →