If the Evidence Is in Your Data,
We Can Point an Agent at It

Find it. Prove it. Fix it. At Scale.

We are not limited to a list of industries, or a list of use cases. What our customers have in common is that they run their own warehouses, fleets or stores, and the failures that cost them customers start upstream, where nobody is looking.

Purpose-Built AI Agents for Loyalty Operations

Multi-channel Retail

Owns SLA credits, chargebacks, cancellations.

The Challenge

Stock that is not where the system says it is, delivery promises that slip, and a store network where the same failure repeats every week without anyone proving why.

Missions

At-risk order recovery

Steadier Trustpilot, App Store ratings, higher NPS.

Delivery-promise & OTIF

Millions saved in avoided split-shipment costs.

Split-shipment prevention

Steadier OTIF. Fewer chargebacks and SLA credits.

Shrink and inventory accuracy

Seconds vs. hours of resolution time. No added headcount.

35% higher on-shelf availability on the SKUs customers come back for. 20% reduction in shrink and concessions.

D2C & Marketplaces

Owns shrink, theft, fraud, damage.

The Challenge

A repeat order goes wrong upstream, in a stock record that drifted or a hand-off that got dropped, and you find out as a cancellation rather than a complaint.

Missions

At-risk order recovery

20% reduction in shrink and concessions.

Split-shipment prevention

Less margin lost to theft and false claims.

Seller and vendor fraud

Fewer false claims. Less margin lost to concessions.

Fulfillment exception resolution

Fewer false claims. Less margin lost to concessions.

Fewer broken delivery promises. Steadier ratings and NPS. Resolution in minutes rather than days.

Manufacturing and Auto Parts

Owns labor and throughput cost per unit.

The Challenge

You are measured on someone else’s SLA, across sites and clients that each run differently, and proving what caused a miss takes longer than the miss itself.

Missions

Excess-inventory correction

20% less overstock, steadier loyalty signal.

Line-stopping stockout prevention

7% more units per hour. No added headcount.

Inbound supplier performance

70% faster resolution. No added headcount.

In-transit loss and damage

70% faster resolution. No added headcount.

22% less excess inventory. 35% fewer line-stopping stockouts. 7% more units per hour.

3PL and Fulfillment Providers

Detects broken delivery promise patterns and automatically triggers corrective action.

The Challenge

In-transit failures (delays, damage, loss) drive NPS detractors, Trustpilot complaints, and silent repeat-customer churn.

Missions

Directed putaway and receiving breakdown

20% less overstock, steadier loyalty signal.

Throughput bottlenecks

20% less overstock, steadier loyalty signal.

Labor reallocation

20% less overstock, steadier loyalty signal.

Claims and damage attribution

20% less overstock, steadier loyalty signal.

70% faster root-cause resolution. 7% more units per hour without added headcount.

Inventory Shrink Agent

Identifies theft, shrink, and execution failures across facilities before they drain the loyalty budget.

The Challenge

Silent margin erosion drains the loyalty budget. Every dollar lost to shrink is a dollar that cannot fund the customer experience.

Use Cases

Theft (internal or external)

Process failures in stores or warehouses

Inventory discrepancies and execution gaps

Impact: Protected revenue, lower losses, loyalty budget intact

Fulfillment Execution Agent

Detects fulfillment execution gaps across every node and automatically drives corrective actions end-to-end.

The Challenge

Manual exception triage and process gaps that delay every loyalty fix. Throughput collapses in peak.

Use Cases

Manual exception triage and ticket handling

Training and process gaps

Throughput bottlenecks during peak periods

Impact: Seconds vs. hours of resolution time. No added headcount

For any industry

If the evidence is in your data, we can point an agent at it, prove what the failure costs, and fix the cause so it stops repeating. We work on what already went wrong in your supply chain and operations, which is how you stop it going wrong again.

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