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IndustriesLogistics
AI for Logistics & Supply Chain
Supply Chains That Move Smarter, Not Just Faster
From predictive demand forecasting and dynamic route optimisation to warehouse automation and last-mile intelligence — we build AI systems that reduce cost, eliminate waste, and make your supply chain self-correcting.
Industry Challenges
Why Traditional Logistics Models Are Breaking
Rising fuel costs, demand volatility, last-mile complexity, and carrier capacity shortages are exposing the limits of rules-based planning tools built for a simpler era.
4 key challenges
01
Route & Network Inefficiency
Static routing algorithms can't adapt to real-time traffic, weather, or capacity changes — leaving fleets running 20–30% above optimal fuel and time costs.
02
Demand Forecasting Failures
ARIMA and Excel-based forecasting models struggle with seasonality, external shocks, and new product launches — resulting in costly overstocks and stockouts.
03
Warehouse Throughput Bottlenecks
Manual picking processes, poor slotting logic, and unoptimised dock scheduling combine to leave 30–40% of warehouse capacity underutilised.
04
Last-Mile Delivery Costs
Last-mile represents 53% of total shipping cost and rising customer expectations for same-day delivery are making legacy dispatch models economically unsustainable.
AI Solutions
AI That Makes Your Supply ChainSelf-Correcting
Purpose-built AI models that turn logistics data into dynamic, self-optimising decisions — from supplier to doorstep.
Dynamic Route Optimisation
Real-time ML routing that adapts to live traffic, weather, vehicle capacity, and delivery windows — reducing fuel costs by 15–25% versus static models.
AI Demand Forecasting
Gradient boosting and transformer models that incorporate 200+ external signals — achieving 92%+ accuracy across SKUs with minimal historical data.
Warehouse Intelligence
Slotting optimisation, pick-path routing, and dock appointment scheduling powered by reinforcement learning — increasing throughput by 30–40%.
Fleet & Asset Management AI
Predictive maintenance models that schedule servicing before breakdown, reducing unplanned downtime by 65% and extending vehicle lifecycle.
Supply Chain Risk Intelligence
NLP-powered monitoring of supplier news, port disruptions, and geopolitical signals — giving 72-hour early warning on supply chain disruptions.
Last-Mile Delivery AI
Dynamic dispatch, crowd-sourced driver allocation, and customer preference learning that reduce last-mile cost per delivery by up to 28%.
Use Cases
AI Delivering Real Logistics Savings
How logistics operators, 3PLs, and e-commerce players used our AI to cut costs, hit SLAs, and build resilient supply chains.
01Case Study
Route OptimisationFleet AICost Reduction
Multi-Stop Route Optimisation
The Challenge
A regional courier network operating 1,400 vehicles with a legacy routing system producing 18% more kilometres than necessary — costing £3.8M in excess fuel annually.
The Outcome
Dynamic ML routing reduced kilometres driven by 18% and fuel costs by £3.1M in year one. On-time delivery rate improved from 87% to 96.4%.
02Case Study
Demand ForecastingFMCGInventory AI
Demand Forecasting at Scale
The Challenge
A global FMCG distributor with 85,000 SKUs experiencing 34% forecast error on seasonal products, resulting in £12M annual write-offs from overstock.
The Outcome
ML demand forecasting model achieved 92.3% accuracy across all SKUs. Overstock write-offs reduced to £2.8M. Working capital freed: £9M.
03Case Study
Predictive MaintenanceIoT AIFleet
Predictive Fleet Maintenance
The Challenge
A logistics operator with 640 heavy goods vehicles suffering 11% unplanned breakdown rate — each incident averaging 14 hours of downtime and £4,200 in costs.
The Outcome
IoT sensor AI predicts failures 5 days in advance. Unplanned breakdowns reduced 71%. Annual maintenance cost savings: £2.6M. Fleet utilisation up 12%.
04Case Study
Warehouse AIPick Optimisation3PL
Warehouse Slotting & Pick Optimisation
The Challenge
A 3PL operating a 480,000 sq ft fulfilment centre with 22-minute average pick cycle times and 15% pick error rate on 40,000 daily orders.
The Outcome
AI slotting and pick-path optimisation reduced pick cycle to 11 minutes. Error rate fell to 1.2%. Throughput capacity increased 38% without additional headcount.
Proven Impact
The Logistics AI Advantage
Real performance gains from AI deployments across logistics, 3PL, and supply chain operations.
Top Result
18%
Fuel Cost ReductionDynamic route optimisation
92%
Demand Forecast AccuracyAcross 85,000+ SKUs
71%
Unplanned Downtime CutPredictive fleet maintenance
38%
Warehouse Throughput GainPick path & slotting AI
Ready to Transform Logistics with AI?
Book a free 45-minute AI discovery session with one of our logistics AI specialists.
Client Stories
Built With Trust. Proven in Production.
Hear directly from the leaders who partnered with us to ship AI-powered products, modernize platforms, and move faster than they thought possible.
"Great company with great management quality developers were really dedicated to get the job done in a timely cost-effective manner."
- Alexandar Salahsour
"They consistently delivers reliable, high-quality development solutions with exceptional communication, value, and trusted partnership."
- Joe Pellegrino, Jordan Pellegrino
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