1. Ward
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  5. Live Pilot
Product · Live Pilot

850+ stores. $300M+ revenue.
Real problems found daily.

Ward is live with a multi-location retail operator. Real POS, labor, finance, inventory, ERP data. Margin leaks, shrinkage patterns, promo conflicts, staffing mismatches — found and explained, across every store, every day.

See what Ward finds at scale →
0+
Stores under management

Multi-location operator across grocery and specialty verticals. Full data stack connected. POS, labor, finance, inventory, and ERP flowing into Ward.

$0M+
Revenue under intelligence

Over $300 million in annual revenue flowing through Ward’s agents, surfacing insights across revenue, margin, labor, and supply chain.

0%
Target EBITDA improvement

Conservative target based on insight-to-action conversion across revenue, labor optimization, and shrinkage reduction.

What the pilot proves

Ward isn’t a concept. It’s running against live retail data at scale. Real POS transactions, real labor schedules, real finance feeds, real inventory movements. The pilot validates that connecting insight to action to outcome — and closing the loop — drives measurable operational improvement.

01
Full data stack connected
POS, labor, finance, inventory, ERP — all flowing into Ward’s unified data layer. Every data source connected, every signal correlated.
02
Agents running continuously
Five AI agents monitoring revenue, stores, finance, inventory, and supply chain. Insight cards surfacing daily to the right people.
03
Outcomes measured
Every action is tracked. Every outcome feeds back. The loop is closing — and each cycle makes the agents sharper.
“The $1.8T AI market is here. The question is whether your tools connect insight to action, or just show you charts.”
Ward thesis, backed by McKinsey & Gartner research

The data case

$40B+
Revenue at stake from stockouts
Each year in the U.S. alone. AI-powered demand forecasting reduces stockout rates by 20–50%.
IHL Group, 2023
2–4%
Margin improvement from AI pricing
Dynamic pricing and markdown optimization translating to tens of millions in incremental profit.
McKinsey, 2024
$112B
Lost to retail shrinkage in 2022
AI-based anomaly detection identifies loss patterns weeks earlier, cutting shrinkage by up to 20%.
NRF, 2023

See Ward across verticals

30,000+
Grocery & Supermarket
Fresh availability, shrinkage, and promo effectiveness across hundreds of stores. Ward monitors perishable turn rates and flags waste before it happens.
15,000+
Fashion & Apparel
Seasonal sell-through, size curve optimization, and markdown timing. Ward monitors style velocity and flags slow movers before the window closes.
3,000+
Convenience & C-Store
High-frequency, low-SKU environments where every facing counts. Ward monitors impulse categories and daypart demand patterns around the clock.
20,000+
Pharmacy & Health
Regulated inventory, seasonal demand spikes, and front-of-store optimization. Ward handles the complexity so your pharmacists focus on patients.
50,000+
Home Improvement
Project-based purchasing, long-tail SKUs, and seasonal volatility. Ward manages the complexity of 50,000+ SKU environments with ease.
10,000+
Furniture Manufacturing & Retail
ERP-locked production data, long lead times, and margin erosion you don't see until quarter-end. Ward connects your internal systems and surfaces what matters.
5,000+
Specialty Retail
High-consideration purchases, curated assortments, and customer lifetime value. Ward tracks the metrics that matter for margin-rich retail.
Ward
Insight
Dispatch
Feedback
Evaluate
Learn
The Closed Loop
01

Insights surface

Ward’s agents detect what changed, why it matters, and what to do about it. Every insight includes a recommended action—not just a chart to interpret.

Real-time detection Root cause + recommendation
02

Insights become actions

Any insight card can be turned into a tracked ticket or task. Dispatched to the right person, on the right channel—mobile push, text, or email. Not every insight needs a ticket. But when one does, it has an owner.

Tickets created automatically Dispatched to the right person
03

Your team responds

Insights get voted up or down with reasoning. Tickets get completed or rejected. Every response is a signal—Ward learns what worked, what missed, and why.

Vote up / down Ticket completed Reasoning attached
04

Outcomes measured

Ward evaluates real results: revenue, margin, fill rate, labor cost. Did the action actually improve the number it targeted? Measured outcomes, not assumptions.

KPI impact tracked Results vs. prediction scored
05

Agents get sharper

Every vote, every completed ticket, every measured outcome feeds back in. Ward learns from your team’s judgment and real-world results. Each cycle sharpens the next. Then it starts again.

Cycle repeats, sharper each time
$1.8T
Projected global AI market by 2030
0
×
Customer acquisition lift for data‑driven orgs
0
+
Foundation models shipped since 2022
0
Guarantees any single model stays on top

850+ stores. Real data. Real problems found.

See what Ward catches at scale — on your data.

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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.

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