AI for Logistics Operations
Document intelligence, agents and forecasting connected to your TMS, WMS and ERP.
AI Across the Supply Chain
Document intelligence, agents and forecasting connected to your TMS, WMS and ERP.
Document Extraction
Bills of lading, invoices, customs forms and proof of delivery read into your TMS and ERP.
- Bills of lading
- Invoices
- Customs forms
Exception Handling Agents
Agents that spot late shipments, update customers and propose a fix for a coordinator to approve.
- Late shipments
- Customer updates
- Approvals
Route and Load Planning
Routes and loads planned around time windows, capacity and driver hours.
- Time windows
- Capacity
- Driver hours
Volume Forecasting
Volume forecasts for staffing, capacity and inventory planning.
- Staffing
- Capacity
- Inventory
Warehouse Operations
Slotting, picking and replenishment suggestions from order and stock data.
- Slotting
- Picking
- Replenishment
Customer Service Copilot
Drafted replies, shipment summaries and search across emails and tickets inside the tools your team uses.
- Drafted replies
- Shipment summaries
- Search
From Use Case to Production
Three phases. Each one ends with something your team can evaluate before the next begins.
Phase 1
Assess
We pick use cases with a clear business case, check data quality and access, and agree on how success is measured.
- Use cases ranked by value and risk
- Data and access review
- Success criteria and evaluation set
Phase 2
Build and Evaluate
We choose a model for each task, connect it to your data and systems, and test it against real cases before go-live.
- Model choice by task, cost and data residency
- Retrieval and system integrations
- Evaluations and guardrails
Phase 3
Deploy and Monitor
We release with human approval where it matters, monitor quality and cost in production, and extend to more sites and workflows.
- Approvals and audit trails
- Quality, cost and drift monitoring
- Rollout across sites
Across Logistics Operations
From the dock to the customer's door.
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Transportation
Fleet planning, routing and driver workflows.
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Warehousing
Inventory accuracy, picking and slotting.
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Last Mile
Delivery windows, arrival estimates and customer updates.
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Supply Chain
Visibility, customs documents and disruption alerts across partners.
AI for Logistics: Common Questions
What logistics teams ask before starting an AI project.
Contact our teamDocument extraction for bills of lading and invoices, exception handling for late shipments, and volume forecasting are common first projects.
Yes, through their APIs, EDI and carrier integrations. Results land in the systems your coordinators already use.
They can draft updates and send routine ones within rules you set. Anything outside those rules goes to a coordinator first, and every message is logged.
Frontier APIs or open-weight models run privately, chosen per task by quality, cost and where the data may go.
We agree on a baseline and a target measure in the assessment phase, then test the pilot against it before scaling.
Discuss Your Logistics AI Use Case with Our Team
Tell us which routes, sites or workflows you want to improve. We will review your data and propose a first phase.
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01Discovery
We review your processes, systems and objectives.
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02Proposal
Defined scope, timeline and investment for phase one.
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03Delivery
A production release, with a roadmap to scale.