We scaled 400+ retail locations.
We know what goes unnoticed.
Ward came out of running LeanBox. It came out of every problem we found too late.
We lived the problem before we built the solution.
LeanBox was a micromarket retailer — unmanned locations inside offices, hospitals, and universities. We scaled it past 400 locations and 8 figures in annual revenue.
A location slips. Spoilage creeps up. Nobody catches it for weeks.
We had the data. By the time anyone spotted it, the damage was done.
Dashboards, alerts, spreadsheets wired to APIs. It worked, barely.
Real-time visibility for operators, without building a data team from scratch.
Ward came out of every problem we found too late.
Chat
Ask anything. Ward routes to the right agent and returns cited answers.
I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.
| Signal | Finding |
|---|---|
labor_efficiency | Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak |
inventory.fresh | Fresh fill 83%, backroom replenishment lag at 2–4p |
promo.lift | BOGO crackers cannibalized Brand Y by 28%, net category +6% |
Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.
labor_scheduling…
Dashboards
Pinned views built from saved data-lake queries.
Models
Browse, search, and manage data–lake model definitions for your tenant.
| Name | Namespace | Version |
|---|---|---|
retail_pos_transactions | retail | 1.0 |
retail_inventory_snapshot | retail | 1.2 |
retail_labor_scheduling | retail | 1.0 |
retail_promo_calendar | retail | 1.1 |
retail_supplier_performance | retail | 1.0 |
sap_inventory_shrinkage | sap | 1.0 |
ga4_daily_events | marketing | 1.0 |
meta_ads_ad_level | marketing | 1.0 |
Sources
Connect external systems to the data lake.
| Name | Type | Last sync |
|---|---|---|
sap_pos_transactions | import | 2m ago |
sap_inventory_shrinkage | import | 2m ago |
sap_labor_scheduling | import | 14m ago |
retail_inventory_weekly | import | 1h ago |
retail_google_ads_daily | import | 1h ago |
retail_meta_ads_daily | import | 1h ago |
retail_ga4_website_daily | import | 1h ago |
Architecture
Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.
sap.possnow.inventoryPipelines
Move data from sources into models on a schedule.
| Name | Source | Model | Status | Schedule |
|---|---|---|---|---|
sync_sap_pos_transactions | sap_pos_transactions | pos_transactions | enabled | hourly |
sync_sap_labor_scheduling | sap_labor_scheduling | labor_scheduling | enabled | daily |
sync_sap_inventory_shrinkage | sap_inventory_shrinkage | inventory_shrinkage | enabled | daily |
sync_retail_inventory_weekly | retail_inventory_weekly | inventory_weekly | enabled | weekly |
sync_retail_google_ads_daily | retail_google_ads_daily | google_ads_daily | enabled | daily |
sync_retail_ga4_website_daily | retail_ga4_website_daily | ga4_website_daily | enabled | daily |
Streams
Real-time ingestion pipelines.
pos.txnstore_037, basket $42.18inv.movedc_west → store_104labor.clockstore_022 shift_startpos.txnstore_211, basket $19.04
Policies
Browse and manage Cedar access policies for your tenant.
| Policy ID | Effect | Resources |
|---|---|---|
merch-read-default | permit | Model::* |
finance-read-shrinkage | permit | Model::"shrinkage" |
vendor-blocked | forbid | Model::"labor_*" |
region-west-only | permit | Tenant::"acme" |
Entities
Principals and resources referenced by Cedar policies.
| Entity UID | Type | Tenant |
|---|---|---|
Tenant::"acme" | Tenant | acme |
Model::"sap.pos_transactions" | Model | acme |
Model::"sap.inventory_shrinkage" | Model | acme |
Model::"sap.labor_scheduling" | Model | acme |
Model::"retail.toast_pos_daily" | Model | acme |
Model::"retail.ga4_website_daily" | Model | acme |
Providers
Manage LLM API keys and the model profiles that use them.
| Name | Provider | Used by | Created |
|---|---|---|---|
anthropic-default | Anthropic | 3 profiles | Apr 22 |
openai-default | OpenAI | 2 profiles | Apr 22 |
gemini-default | Gemini | 1 profile | Apr 22 |
ollama-onprem | Ollama | 2 profiles | Apr 22 |
LLM-agnostic. Bring your own key, route per task. No lock-in.
Settings
Manage your dashboard preferences and account.
Light and dark themes are available. Your choice is remembered per browser.
We custom-built the entire stack. Every piece of it.
No off-the-shelf tool fit unmanned micromarket retail at our scale. So we built it all.
The system of record that ran inventory and replenishment.
Kiosk checkout capturing every transaction at unmanned locations.
Inventory and ordering unified on the same data.
Five systems wired together into one source of truth.
Catching drift and alerting before it hit the shelf.
It took an engineering team most operators cannot afford. Ward packages all of it into a platform that works out of the box.
Enterprise-grade systems, shipped by the team that ran the stack.
The AI breakthroughs already happened in the labs. The hard part is making them run inside a 200-store operation where the ERP dates to 2014 and the POS still exports CSV. That part we own, end to end.
Managed routes. Renegotiated suppliers. Found why location #247 dropped 30% in two weeks.
We built the ERP, POS, and data systems that ran 400+ locations through 8 figures of revenue.
We have already shipped systems that survived multi-hundred-location retail. Ward is us doing it a second time.
AI recommends. You decide.
Ward surfaces what matters. Humans make the call. Lane assist, not autopilot.
The same team behind LeanBox and askotter.ai.
Retail operators who became data engineers who became platform builders. Ward is the latest product from a team that has shipped together for years.
Scaled LeanBox from concept to 400+ locations and 8-figure revenue. Custom-built the ERP, POS, and data stack that became the blueprint for Ward. 15+ years in growth marketing and software engineering.
Ex-YC and Meta. Helped build and scale LeanBox's product suite. Product strategy, frontend engineering, and platform design. Turns operator pain points into interfaces that actually get used.
Ex-YC and Amazon. Backend systems, data infrastructure, and cloud architecture. Designed the pipelines that process millions of retail transactions at scale.
Investor and operator with multiple exits across consumer and technology. Advises on business development and market strategy.
Retail is the hardest observability problem nobody is solving.
Engineers have had Datadog, PagerDuty, and Grafana for years. We bring that engineering-grade monitoring to retail.
Stockout prediction, shrinkage detection, demand forecasting, price optimization — running continuously across every location and every SKU, every day.
POS, Transactions & Inventory
Every sale, refund, and basket across every location, unified and watched in real time. Stock levels, spoilage, fill rates, and reorder signals run on the same view. Anomalies get caught before they hit the shelf.
Finance, Margin & Operations
Per-location P&L, COGS drift, and margin erosion get flagged automatically, not found in month-end close. Scheduling, route efficiency, and service levels surface alongside them. These are the operational signals that hit the bottom line.
We built Ward because we kept finding problems too late.
The shrinkage we couldn’t attribute. The promos we couldn’t score. See what Ward finds in your data.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.