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/// retail & multi-location

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.

< 4 min
anomaly detection time
per-store
health scoring
24/7
per-store monitoring
PS POS Systems SA SAP Retail OR Oracle Retail IM Inventory Mgmt SC Supply Chain AD Google Ads +1 more DATA LAKE 7 sources Change Detection Root Cause Predictions Recommendations NLQ Chat
/// live monitoring

What askotter catches at store level.

store health · live 6 STORES
87
Store #247 Austin
HEALTHY
62
Store #412 Denver
ATTENTION
91
Store #89 Charlotte
HEALTHY
78
Store #331 Nashville
HEALTHY
44
Store #518 Tucson
AT RISK
83
Store #205 Portland
HEALTHY
/// AGENT FEEDretail & multi-location
CRITStore #247 Austin: Milk inventory below 2-day supply. Holiday weekend ahead. Auto-reorder recommendation generated.3m
WARNStore #412 Denver: Produce wastage +40% vs 7-day avg. Cooler temp drifted to 48°F (threshold: 40°F). Maintenance routed.18m
INFODistrict 7 (Portland): Basket size up 23% since endcap layout change on Monday. Rolling to District 3 recommended.1h
OKBOGO promo on SKU #1142: Margin impact: -$4.2K but foot traffic +18%. Net positive. Extending through weekend.2h
CRITStore #89 Charlotte: POS terminal offline for 22 min during peak hours. Estimated lost revenue: $1,400. IT ticket auto-created.4h
/// real-time response

Thursday, 3:47 PM: holiday weekend incoming

Store #247 is about to run out of milk. Here is what happens:

3:47 PM
Milk inventory drops below 2-day supply at Store #247
CHANGE DETECTION
3:48 PM
Holiday weekend demand spike model triggered. Predicted shortfall: 340 units
FORECASTING
3:49 PM
Auto-reorder recommendation generated. Nearest DC has 2,400 units available
STRATEGY
3:50 PM
Store manager and district lead notified via dashboard and Slack
ROUTING
/// before & after

What changes with askotter.

METRIC
BEFORE
WITH ASKOTTER
Stockout detection
3-5 days
< 4 minutes
Wastage visibility
Monthly aggregate
Per-store, daily
Store health scoring
Quarterly reviews
Real-time, per-location
Promotional ROI
Gut feel
Margin-adjusted, per-SKU
/// who uses it

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.

THE PAIN POINT

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.

THE PAIN POINT

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 PAIN POINT

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.

THE PAIN POINT

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.

/// why current tools fall short

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)
POS knows what sold and when. But it has no idea about inventory levels, supply chain status, or promo margins. It says SKU #1142 sold out Tuesday. It cannot tell you the reorder shipped late, or that the BOGO deal on that item cost more than it earned.
✕ NOT ENOUGH Inventory management tools (TradeGlobal / NetSuite / Fishbowl)
Tracks stock levels and reorder points. But without knowing how fast items sell, thresholds stay the same all year. A 2-week supply is fine in January but risky before a holiday weekend. These tools do not know your demand patterns. They just count units.
✕ NOT ENOUGH Spreadsheet rollups (Excel / Google Sheets)
The backup for everything else. Store managers email weekly reports. District managers paste them into a master sheet. By the time the regional VP sees it, the data is a week old. No alerts, no way to spot problems, no way to compare 200 stores at once.
✕ NOT ENOUGH Enterprise BI (Tableau / Power BI / Looker)
Nice dashboards, once someone builds them. You need a data team to set up pipelines, clean data, and build reports. It does not monitor or alert. It shows what happened last quarter but cannot tell you what is happening right now at Store #247.
✓ WHAT'S DIFFERENT WITH ASKOTTER

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.

/// store-level pain

What store operators deal with every day.

Stockouts cost more than you think
One item out of stock at one store is easy to miss. Now picture that happening across hundreds of stores every day. POS says it sold out. Inventory says it shipped. No one checks the gap until a customer complains or walks to a competitor.
Waste adds up fast
Food expires on shelves while the same items get reordered somewhere else. Too much in one region, not enough in another. Without store-level demand forecasting, waste is built into how you operate. Even a 1% cut across a big chain saves millions.
You only see store profit once a quarter
Your best store and your worst store look the same in big-picture reports. By the time managers see per-store numbers, the quarter is already over. You need daily store health, not quarterly surprises.
Promotions eat into your margins
A BOGO deal brings people in the door but kills your margin on items that would have sold at full price anyway. Which promos actually grow revenue? Which ones just move the same sales forward? The data is there across POS, inventory, and marketing. No one connects it.
/// store metrics

Per-store metrics that move margin.

Stockout Rate
2.1% ↓ 0.3% this month
tracked per-store, daily
Wastage (% Revenue)
1.4% ↓ 0.1% this week
per-location visibility
Store Health Score
87/100 current avg
scored per-location
Promo ROI
2.3x current avg
margin-adjusted, per-SKU
connected tools
POS Systems SAP Retail Oracle Retail Inventory Mgmt Supply Chain Google Ads Meta Ads +50 more
/// the playbook

From 10 stores to 1,000. Train once, scale everywhere.

1

Connect every store

POS, inventory, supply chain, staffing, and local marketing all flow into one place. Each store gets its own data lake.

2

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.

3

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.

4

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 the chain

Ask across every store at once. Save what you learn.

/// CHATaskotter
Which stores have stockout risk this week?
14 stores flagged. Top 3 by revenue risk: Store #247 Austin (milk, yogurt, $3.2K), Store #89 Charlotte (eggs, butter, $2.8K), Store #331 Nashville (hygiene products, $1.9K). All from pre-holiday demand spikes. Reorder suggestions ready for each.
SOURCES: POS + Inventory + Supply Chain
What is causing the waste spike in District 4?
Produce waste is up 34% in District 4 over 2 weeks. The cause: 3 stores got a bulk berry shipment timed for July 4th, but demand peaked 4 days earlier than last year. Suggestion: switch to just-in-time ordering for perishables during seasonal periods.
SOURCES: Inventory + POS + Historical Sales
Show me the bottom 10 stores by margin.
Here are the bottom 10 stores ranked by gross margin. Store #518 is worst at 12.4% (chain avg: 28.1%). Common pattern: too many staff during slow hours and high shrinkage on premium items. 7 of 10 are in smaller metro areas.
SOURCES: POS + Staffing + Inventory
Ask anything about your retail & multi-location data. Save responses as notes to share. Ask

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 →
/// other industries

Explore more verticals. Same human-in-the-loop approach.