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Industries · Logistics & Supply Chain

Autonomous AI discovery built for logistics & supply chain.

Run an autonomous AI analyst on your shipments, inventory, and exception data — find the drift before customers escalate, and ship the recommendation the same week.

Built for 3PLs, freight & last-mile carriers, fulfillment networks, and shippers & manufacturers.

CRITICALCarrier drift
One carrier-lane combo missed on-time SLA 7 days running.
Carrier B on the NW-MW lane dropped from 96%…
From a real DecisionBox run
DecisionBox for logistics

Autonomous discovery on the data your network already runs on.

Your shipments, lanes, exceptions, dock events, and inventory positions already live in your data. The questions worth asking — which lane is drifting, where exceptions concentrate, why one DC is bottlenecked — sit there too, unasked. DecisionBox runs them, with every number re-checked against your data before it ships.

See how every number gets checked
The cost of missed insights

The signals already in your data are what every missed promise looks like.

Three benchmarks every ops, supply-chain, and inventory leader is quietly paying right now. Every one points to the same gap: the signal is in the data; nobody has the bandwidth to read it. By the time the customer call lands, the SLA has slipped, the SKU has stocked out, or the lane has drifted past recovery.

Failed delivery cost
$17.78
Average cost of a single failed delivery attempt — re-delivery, support time, lost lifetime value. With 8–20% of parcels failing first-attempt, the math compounds fast.
Industry compilations, 2024–2025
Disruption cost
$301B
Annual losses from supply-chain disruption alone. Most disruptions show up as lane drift, exception spikes, or capacity pressure days before they become customer escalations.
IHL Group, 2024
AI adoption gap
<1 in 4
Retailers and operators with AI deployed in the areas most impacted by inventory distortion. The signal is in the data; reading it in time is the bottleneck.
IHL Group, 2024
From real runs

From a pattern to a playbook.

Every finding the agent surfaces comes paired with a recommendation — a target segment, an expected impact, a concrete set of actions. Three pairs below, pinned as the agent surfaced them.

HIGHCarrier drift
Carrier B on the NW–MW lane fell from 96% to 78% on-time in 7 days.
18pp drop · 7 days · 1,240 shipments late
Mid-stage 3PL
ship this one first
P1Recommendation
Recommendation
Shift Carrier B overflow on the NW–MW lane to the backup carrier for 14 days;
Expected impact
+10–12pp on-time · 14-day window
Target: 1,240 shipments5 concrete actions
HIGHHidden concentration
DC-4's exception rate jumped 4.1× in 14 days.
4.1× exceptions · 71% from one SKU class
Fulfillment network
cross-system pattern
P2Recommendation
Recommendation
Pull SKU class HZM-09 out of cross-dock into staged putaway until the receiving-side anomaly resolves;
Expected impact
DC exception rate to baseline · 3–5 days
Target: one SKU class, one…4 concrete actions
HIGHRoute variance
Three last-mile routes look like 91% on-time on the scorecard.
91% average hides 71% & 76% routes
Last-mile carrier
zero added capacity
P2Recommendation
Recommendation
Rebalance Routes 14 and 22 across overlapping high-density zones;
Expected impact
+15–20pp on-time on affected routes
Target: 340 weekly stops4 concrete actions

See the lane, SKU, or DC drifting on your data — before the next customer call.

Use cases

For 3PLs, freight, fulfillment, and every flow in between.

Same engine, same data, different analysis areas. Pick the surface that matters this ops cycle.

Reorder timing, stock-out risk, and demand drift the planner never had time to ask.

Inventory distortion costs the global retail industry $1.73T a year. Most of that is unread cohort, demand-curve, and replenishment signal sitting in the data. The agent runs the questions the planner doesn't have time to ask — and surfaces the SKUs and locations where the next stock-out or overstock is forming.

Sample analysis areas
  • Reorder-point drift by SKU and location
  • Stock-out risk scored against demand signal and lead time
  • Replenishment cadence decay across cohorts
  • Production-planning misalignment with inbound demand
  • List-price and quote-pricing drift against margin and demand response
Why DecisionBox

Built for the people who own the customer call.

Three things every ops review needs and never quite has time to do. Every DecisionBox run ships all three.

Sees what nobody had time to look for

The lane, SKU class, or DC drifting now — surfaced before the carrier scorecard catches it and before the customer call lands. You review the shortlist; you don't write the queries.

Every number you'd defend in a QBR

Every figure is independently re-checked against your data before it ships. When the customer or the CFO pushes back, you have the trail to back it up — no guesswork, no "let me get back to you."

Your data stays with you

Operations data and credentials never leave your environment. You stay in control of where the data sits, who sees it, and what leaves the building.

The next ops review question, already answered.