Logistics
Logistics needs operational intelligence. Not just tracking dashboards.
Logistics companies operate at the intersection of physical constraints and data complexity. Routes change, loads shift, demand spikes, and equipment fails. The businesses that stay competitive are not those with the most trucks or the largest warehouses. They are those that make the best decisions the fastest, and act on them automatically.
$12.7B
Global AI in logistics market by 2027, growing at 42% annually
Statista, 2024
23%
Average reduction in transportation costs achieved through AI optimised routing
McKinsey, 2023
40%
Of supply chain disruptions are predictable with AI monitoring systems built on existing data
Gartner, 2024
AI maturity curve
Where most operators stall.
Five stages define the logistics AI maturity curve. Most operators only operate in the first two. And wonder why their costs stay high while margins keep shrinking.
Data capture
GPS, warehouse scanners, and order management generate large data volumes. Almost never connected into a unified operational intelligence layer
Visibility dashboards
Tracking and reporting exist but require manual interpretation. Data informs retrospective reviews, not real time decisions
Route optimisation
Dynamic routing used selectively. Most fleets still plan with static rules and dispatcher judgment, not live conditions
Predictive operations
Demand forecasting and predictive maintenance at operational scale. Significant ROI, limited deployment
Autonomous decisioning
Self adjusting systems for capacity, routing, and pricing. Almost no operator runs this in production
Failure patterns
Recognise any of these?
Route planning depends on dispatcher experience and static rules. Every delivery exception requires a phone call
When conditions change on a live route including traffic, access restrictions, or load adjustments, the response is a call to the driver. There is no system that recalculates and communicates automatically. The dispatcher becomes the bottleneck and the driver makes decisions without the full picture.
Warehouse picking throughput is capped by how fast people can walk, not by how intelligently the system directs them
Pick paths are assigned by zone or product type, not by order profile, travel distance, or real time floor state. Replenishment signals are visual checks or end of day counts. Throughput is limited by process design, not physical capacity. The fix is not more staff. It is smarter sequencing.
Demand forecasting is based on historical averages. Capacity and staffing decisions consistently lag actual conditions
Seasonal patterns are understood broadly, but the granular signal by route, depot, and product category is not available early enough to act on. The result is overcommitment on some lanes and under resourcing on others. The gap between planned and actual is filled by overtime and reactive spot buying.
Customer delivery communication is reactive. They call to find out where their shipment is because you have not told them
Tracking data exists in the transport management system. But no layer converts it into proactive outbound communication. The customer does not know what you know. The result is inbound support volume that consumes capacity without adding any commercial value.
Fleet maintenance is scheduled by calendar intervals. Vehicles break down regardless of when they were last serviced
Service intervals are standardised across vehicles regardless of actual usage patterns, route conditions, or component wear signals. Predictive maintenance is feasible with existing telemetry data. But no system converts those signals into condition based service triggers before a breakdown occurs.
Carrier and lane performance data is tracked in spreadsheets. Procurement decisions are made on relationship and habit
On time delivery rates, damage rates, and cost per lane are available at month end from finance reports. They are not available at the moment a carrier is being selected. Procurement decisions happen without the context needed to make the best choice for that specific shipment.
The gap
Where you are vs where you could be.
Static route plans with dispatcher overrides for exceptions. Changes require phone calls and manual recalculation with no live conditions input
AI optimised routing that updates in real time based on traffic, load changes, access restrictions, and delivery window commitments without dispatcher intervention
Zone based picking with manual replenishment triggers. Throughput limited by staff travel time and process design rather than physical capacity
Intelligent pick path optimisation and automated replenishment signals based on real time inventory levels and order flow, increasing throughput without additional headcount
Monthly forecasting from historical averages with no lane or depot level granularity. Capacity and staffing decisions lag actual demand by weeks
AI demand signals by route, depot, and category that drive proactive capacity, staffing, and procurement decisions before the pressure is felt operationally
Reactive status updates when customers call to ask. Support team spends significant capacity answering questions the tracking system could answer automatically
Automated proactive communication at every shipment stage from collection to proof of delivery, triggered by real events without any manual action
What we build
The operational intelligence your logistics business needs. Built to run.
We build route optimisation engines, warehouse intelligence layers, demand forecasting platforms, and customer visibility infrastructure. Each system connected to your existing fleet management and warehouse tools from day one.
Route optimisation system
Dynamic routing engine that recalculates in real time based on traffic, load changes, access conditions, and delivery window commitments. No dispatcher required for base layer decisions.
Warehouse intelligence layer
Pick path optimisation, real time replenishment signals, and exception detection for mispicks and inventory discrepancies. Throughput improvement without headcount increase.
Demand forecasting platform
Lane, depot, and category level predictions from live order signals, weather, and seasonality data. Capacity and staffing decisions made before the demand arrives.
Predictive fleet maintenance
Telemetry driven condition assessment and service scheduling that replaces calendar based intervals. Fewer breakdowns, lower emergency repair costs, and better vehicle availability.
Customer visibility layer
Automated status communication workflows triggered by real shipment events. Proactive outbound updates that reduce inbound support volume and improve delivery experience.
Carrier and lane analytics
On time delivery rates, damage rates, and cost per lane available at decision time, not month end. Carrier selection and procurement decisions grounded in current performance data.
Start a discovery
Your operation runs on thousands of decisions made every day. We make those decisions faster and more accurate.
A 30 minute diagnostic conversation is enough to identify where the highest leverage intervention is in your logistics operation. No proposal before we understand the problem.
For operations and fleet managers
Routing, maintenance, and capacity decisions that update automatically. Your team handles the exceptions that genuinely need human judgment, not the base layer of every delivery.
For commercial and finance leadership
Cost per delivery, lane profitability, and carrier performance data available in real time. Business decisions grounded in current operational data, not last month's reports.
Relevant services
Capability areas we most often combine for this context.