Logistics & Supply Chain
AI that optimises routes, automates warehouses and predicts demand before your customers do
You get route optimisation agents, warehouse automation workflows, demand planning models, fleet management intelligence, and customs compliance automation. All running on your cloud, connected to your TMS and WMS, with the audit trails your operations teams require.
Built on your AWS, Azure or GCP tenant. Integrated with your TMS, WMS, and ERP.
Proof of concept
To measurable ROI
AWS, Azure or GCP
Your fuel costs keep rising. Your delivery windows keep tightening. AI fixes both.
Logistics companies spend 30 to 40 percent of operating costs on transportation. Warehouses operate at 65 percent average efficiency. Demand forecasts miss by 20 to 50 percent, creating either stockouts or excess inventory. These are not supply chain problems. They are decision-speed problems.
We build AI agents that sit between your data and your operations — optimising routes in real time, automating warehouse workflows, forecasting demand with precision, managing fleet utilisation, and automating customs compliance.
reduction in transportation costs with AI route optimisation (McKinsey, 2024)
improvement in warehouse throughput with AI-driven automation (BCG, 2024)
fuel savings through predictive fleet management and route planning (Deloitte, 2024)
annual value at stake from AI in supply chain and logistics (McKinsey Global Institute, 2024)
From sensor data to optimised delivery in one governed workflow
We connect your TMS, WMS, ERP systems, IoT sensors, and GPS feeds to AI agents that optimise routes, forecast demand, automate warehouse operations, and generate the compliance documentation your customs and trade teams require
TMS, WMS, ERP, IoT sensors, GPS feeds
- Approval gates
- Human-in-loop
- Audit logging
AWS, Azure or GCP
- Route plans
- Demand forecasts
- Compliance filings
What we deliver for logistics and supply chain
From route planning to customs filing. Governed, auditable, operations-ready.
Route optimisation
Calculate optimal routes in real time to cut cost per delivery.
ROI
Process
Ingest route variables
Live traffic, weather forecasts, delivery time windows, vehicle payload, and driver availability streamed into the optimisation engine
Compute optimal routes
ML models evaluate millions of route permutations, balancing fuel cost, delivery time, and compliance with driving regulations
Dispatch and adapt
Optimised routes pushed to drivers with real-time re-routing as conditions change throughout the day
Sources
McKinsey, 'AI-driven logistics optimisation,' 2024; BCG, 'The AI-powered supply chain,' 2024 — reproduced for illustrative purposes
Warehouse automation AI
Optimise pick paths and coordinate robotic systems.
ROI
Process
Map warehouse operations
Order patterns, pick frequencies, storage layouts, and robotic system capabilities analysed to build operational models
Optimise and schedule
AI calculates optimal pick sequences, replenishment triggers, and staff-to-robot coordination schedules
Execute and learn
Workflows deployed to WMS with continuous performance monitoring and model retraining on actual throughput data
Sources
BCG, 'AI in warehouse operations,' 2024; Deloitte, 'Smart warehousing: the AI advantage,' 2024 — reproduced for illustrative purposes
Demand planning and forecasting
Predict SKU-level demand and reduce forecast error by 30-50%.
ROI
Process
Aggregate demand signals
Historical sales, promotional calendars, economic indicators, weather data, and market intelligence combined into feature sets
Generate forecasts
Ensemble models produce SKU-level demand forecasts with confidence intervals across planning horizons
Feed planning systems
Forecasts integrated into ERP and inventory management with automated safety stock and reorder point calculations
Sources
McKinsey, 'AI-powered demand sensing,' 2024; Deloitte, 'Predictive analytics in supply chain planning,' 2024 — reproduced for illustrative purposes
Fleet management intelligence
Predict vehicle maintenance and optimise fleet utilisation.
ROI
Process
Collect telematics data
OBD sensors, GPS trackers, and maintenance records streamed from every vehicle in the fleet
Predict and prioritise
ML models predict component failures, calculate optimal maintenance windows, and score driver efficiency
Schedule and report
Maintenance schedules optimised to minimise downtime. Fleet utilisation dashboards with actionable recommendations
Sources
McKinsey, 'The future of fleet management,' 2024; BCG, 'AI-enabled fleet operations,' 2024 — reproduced for illustrative purposes
Last-mile delivery optimisation
Optimise last-mile sequences and cut failed deliveries.
ROI
Process
Profile delivery zones
Address accessibility, customer availability patterns, and historical delivery success rates mapped for every delivery point
Optimise sequences
AI calculates delivery order, time windows, and driver assignments that minimise cost per successful delivery
Execute with real-time updates
Drivers receive optimised sequences with dynamic re-routing. Customers get precise delivery windows
Sources
Deloitte, 'Solving the last-mile challenge with AI,' 2024; McKinsey, 'The future of last-mile delivery,' 2024 — reproduced for illustrative purposes
Customs and trade compliance
Automate tariff classification and customs declarations.
ROI
Process
Classify and validate
AI reads product descriptions, assigns HS codes, validates country-of-origin declarations, and checks against denied party lists
Generate filings
Customs declarations, certificates of origin, and trade compliance documents auto-generated with full audit trails
Monitor and alert
Continuous monitoring of regulatory changes across trade corridors with automated impact assessments on existing shipments
Sources
Deloitte, 'AI in trade compliance,' 2024; McKinsey, 'Digital customs and trade facilitation,' 2024 — reproduced for illustrative purposes
CEO, Maersk
Vincent Clerc
"We have invested in AI-driven logistics optimisation across our fleet and terminal operations. The result is measurable: fewer empty container movements, better vessel utilisation, and faster turnaround times at port. This is not a pilot. It is running at scale across our network."
Originally published: Maersk — Digital Transformation Update — reproduced for illustrative purposes
How we work in logistics and supply chain environments
Supply chain AI must integrate with your existing TMS, WMS, and ERP systems — not replace them. We start by identifying the one bottleneck that costs you the most, then build the AI workflow that eliminates it with measurable throughput and cost improvements.
Everything runs on your cloud tenant. Your shipment data, your route plans, your demand forecasts. Nothing leaves your perimeter. Your operations and compliance teams can inspect every decision the system makes.
01
Operations and data mapping
We assess your logistics data landscape, system integrations, and operational bottlenecks. Then identify the highest-value use case that delivers measurable cost or throughput improvement.
02
30-day proof of concept
A working AI agent on your cloud, connected to your TMS or WMS, producing optimised outputs in 30 days.
03
Integration with existing systems
Your TMS, WMS, ERP, and IoT platforms connected through governed APIs with real-time data flows and human-in-the-loop controls.
04
Scale in 12-18 weeks
From one validated workflow to measurable operational ROI across route planning, warehousing, demand forecasting, or fleet management.
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Frequently Asked Questions
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