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Consolidation Intelligence for Complex Networks

Consolidation Intelligence
for Complex Networks

The Load Planning Engine That Thinks For Itself.

The Load Planning Engine That
Thinks For Itself.

The Load Planning Engine
That Thinks For Itself.

Pack AI evaluates millions of consolidation scenarios in seconds — maximising vehicle fill, respecting every constraint,
and continuously optimising for the lowest cost and highest service levels. No manual planning required.

Pack AI evaluates millions of consolidation scenarios in seconds — maximising vehicle fill, respecting every constraint, and continuously optimising for the lowest cost and highest service levels. No manual planning required.

Replace manual planning with autonomous
decision-making routing shipments, and managing
transport execution in real time.

See Pack AI in Action

The Problem

Your planners spend hours building loads. The plan is obsolete before dispatch.

Your planners spend hours building loads.
The plan is obsolete before dispatch.

Manual load planning doesn't scale. Planners juggle delivery windows, vehicle capacity, multi-drop sequencing, customer priorities, and cost constraints — all while racing against cut-off times. By the time a plan is finalised, orders may have changed. The result: part-loads, missed consolidation windows, higher costs, and lower service levels.

Large manufacturers and distributors running hundreds of daily shipments can't afford to plan this way anymore.

How Pack AI Works

`GPU-accelerated optimisation. Constraint-aware. Self-learning.`

Pack AI combines GPU-accelerated vehicle routing with enterprise-grade constraint logic and continuous learning loops:

Automated load building

Ingests orders from your ERP/TMS, applies your business rules (FTL-first, multi-drop thresholds, delivery windows, product segregation), and solves the vehicle routing problem in seconds — not hours.

A Digital Twin of Your Logistics Network

Create and compare alternative realities side-by-side. Test different priorities (cost vs service level, vehicle mix, delivery windows) before committing to a plan. Choose the scenario that matches your operational goals.

Self-learning feedback loops

Every planner override, rejection, or modification feeds back into the system. A set of AI agents collect operator decisions, analyse performance variance, and deploy parameter tuning automatically — refining the rules set with you at implementation.

Real-time feasibility checks

Pack AI validates every recommendation against dock capacity, delivery slot availability, carrier constraints, and route feasibility before surfacing it to planners. No phantom recommendations.

How Pack AI Works

`GPU-accelerated optimisation. Constraint-aware. Self-learning.`

Unlike traditional frameworks, we believe that randomness can lead to breakthroughs, unconventional ideas, and novel solutions.

Automated load building

A Digital Twin of Your Logistics Network

Self-learning feedback loops

Real-time feasibility checks

Pack AI validates every recommendation against dock capacity, delivery slot availability, carrier constraints, and route feasibility before surfacing it to planners. No phantom recommendations.

Outcomes

Measurable impact.
Every planning cycle.

Load planning compressed from
45 minutes to under 3 minutes

freeing planners for exception management, not routine consolidation.

Our customer has achieved
a 20% increase in customer service levels

Up to 20% reduction in logistics costs

through better vehicle utilisation and fewer part-loads.

Our customer has achieved
a 20% increase in customer service levels

Higher service levels

via slot-aware planning that respects delivery windows and warehouse operating hours.

Our customer has achieved
a 20% increase in customer service levels

Lower CO₂ emissions

through fewer vehicle movements and optimised routing.

Our customer has achieved
a 20% increase in customer service levels

`What Makes Pack AI Different`

`GPU-accelerated optimisation. Constraint-aware. Self-learning.`

Multi-drop intelligence

Pack AI doesn't just fill trucks — it sequences multi-drop routes with real-world constraints. GPU-accelerated solving means you can model complex multi-drop scenarios without compromising speed.

Constraint-native, not bolt-on

Customer priorities, minimum order quantities, trailer type restrictions, and delivery time windows aren't handled as post-processing filters — they're built into the core optimisation model.

Transparent trade-offs

Every recommendation comes with KPI deltas: cost impact, utilisation change, service level risk. Planners see exactly what they're trading off when approving or modifying a plan.

Integration

Built to Fit In, Not Replace

Your planners spend hours building loads.
The plan is obsolete before dispatch.

Pack AI integrates with SAP (ECC, S/4HANA, TM), Oracle ERP/TM, and any other enterprise systems via API. Orders flow in, optimised load plans flow back. Your ERP remains the system of record — Pack AI supercharges execution.

Combinatorial AI Optimisation

Stop planning loads manually.
Let Pack AI solve it.

European manufacturers are already running Pack AI in production. See what automated load planning looks like for your operation.

Replace manual planning with autonomous
decision-making routing shipments, and managing
transport execution in real time.

Request a Demo

The Load Planning Engine That Thinks For Itself.

The Load Planning Engine That
Thinks For Itself.

The Load Planning Engine
That Thinks For Itself.

Your planners spend hours building loads. The plan is obsolete before dispatch.

Your planners spend hours building loads. The plan is obsolete before dispatch.

Your planners spend hours building loads.
The plan is obsolete before dispatch.

`GPU-accelerated optimisation. Constraint-aware. Self-learning.`

`GPU-accelerated optimisation. Constraint-aware. Self-learning.`

Automated load building

A Digital Twin of Your Logistics Network

Self-learning feedback loops

Real-time feasibility checks

`GPU-accelerated optimisation. Constraint-aware. Self-learning.`

Automated load building

A Digital Twin of Your Logistics Network

Self-learning feedback loops

Real-time feasibility checks