01
Carrier and capacity matching
An agent checks live carrier capacity and cost against shipment requirements and books the match, instead of a dispatcher working a spreadsheet by hand.
Agentic AI in logistics
Agents that work across your logistics network .
We build agents that match loads to carriers, triage shipment exceptions, and route orders across the systems you already run — escalating to a dispatcher when the call is genuinely theirs to make.
Trusted by operators
-
+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
Read case study -
Supply chain intelligence agents reducing manual coordination overhead.
-
37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
-
AI agent implementation for a global automotive enterprise.
-
2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
Read case study -
0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
Read case study
Why agentic AI
Most logistics AI automates the clean path and stalls on the exceptions
Freight, capacity, and order data live in separate systems — TMS, WMS, carrier EDI feeds, spreadsheets a dispatcher still keeps on the side. Rules engines handle the clean path fine. The moment a carrier cancels, an ETA slips, or a document is missing, the workflow needs a system that can read across all of it, weigh constraints, and decide — or know it should not decide and hand it to a person instead.
every routing decision logged and reviewable
Use cases
Where agents earn trust in logistics operations
Workflows with clear rules most of the time, and a defined escalation path for the rest.
02
Shipment exception triage
When an ETA slips or a delivery fails, the agent pulls context from the TMS and carrier feed, proposes a resolution, and escalates only the cases outside its confidence threshold.
03
Multi-system order routing
Cross-checks inventory, warehouse capacity, and delivery windows across systems before committing an order to a fulfillment path — the coordination pattern behind Mixam's order-completion agent.
04
Dock and appointment scheduling
Reads inbound and outbound volume against dock availability and books appointments directly, flagging conflicts a scheduler needs to resolve manually.
The cost of manual coordination
Where multi-system operations lose hours today
These numbers come from our shipped multi-system orchestration engagements — not industry averages. Every figure below links to the case study behind it.
We have not yet shipped inside a logistics-specific company. The mechanics behind these results (real-time constraint checking, multi-step orchestration, human escalation) transfer directly to load matching and shipment routing.
Manual multi-step workflow setup
2 hrs
Engineers assembled each complex workflow by hand on Synera's platform — two hours of tedious configuration per workflow.
95.4%
Routing success rate in production
Mixam's multi-agent system evaluates capacity and dispatches jobs in real time — the same coordination pattern behind load matching.
1% → 100%
Knowledge-inquiry response rate, before vs after
ARIJ Network's bilingual retrieval agent answers reader and trainee inquiries from its own knowledge base across 22 countries — the same grounded, real-time coordination pattern load matching and shipment routing need.
Client results
Proof from production multi-system agent deployments
We have not yet shipped a production agent inside a logistics-specific company — here is what the same multi-system orchestration pattern delivers in production elsewhere.
Delivery path
From workflow audit to a production routing agent
TriStorm keeps the exception-handling logic and the escalation rules aligned before full build commitment.
Scope the workflow and systems
We audit the target workflow, the systems it touches (TMS, WMS, carrier APIs) and where a human dispatcher currently makes the call. Use cases get ranked by volume and manual-effort saved.
- Workflow & systems audit
- Data access map
- Prioritised use case
Build, test and validate
We implement against real shipment and carrier data, with an evaluation suite scored against historical dispatcher decisions before the agent touches a live shipment.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and support
Production rollout with monitoring, audit logging on every routing decision, and a structured handoff so your operations team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own routing and exception-handling workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in logistics, answered
Where does agentic AI actually fit into logistics operations?
In workflows that span multiple systems and require a decision, not just a lookup — load-to-carrier matching, exception handling on delayed shipments, dock scheduling, order-to-fulfillment routing. These are multi-step, multi-source tasks. A chatbot or a rules engine handles one step at a time; an agent reasons across the shipment, the carrier network, and the exception, then acts or escalates.
How is this different from the TMS or WMS automation we already have?
Your TMS and WMS execute fixed logic against clean data. Agents sit above that layer and handle the cases the fixed logic cannot: an ETA slips, a carrier cancels, a document is missing. The agent reads the exception, checks constraints across systems, and either resolves it or routes it to a dispatcher with the reasoning attached.
What logistics workflows have the best return for a first deployment?
High-frequency, well-bounded decisions with a clear escalation path: carrier/capacity matching, shipment exception triage, and multi-system order routing. We start with the workflow that has the most volume and the most manual firefighting attached to it — that is usually where an agent pays for itself fastest.
Can an agent actually replace a dispatcher's judgment calls?
Not entirely, and we do not build it to. The agent handles the routine matching and triage (the high-volume decisions that follow a pattern) and escalates the judgment calls a dispatcher should still own. The line between the two is something we define with your operations team before we build anything.
Can this integrate with our existing TMS, WMS, and carrier APIs?
Yes, through your existing system APIs and EDI feeds — we do not ask you to replace core infrastructure. The agent orchestrates across systems you already run: reading shipment status, checking carrier capacity, writing back the decision.
What is the typical timeline to a working system?
A scoped Proof of Value on one workflow, against real shipment and carrier data, typically lands in around three weeks. Production rollout with monitoring and operator handoff follows the same TriStorm phases as any other engagement.
What happens when the agent gets it wrong, or hits a case it has not seen?
It escalates rather than guesses. Confidence thresholds and coverage gaps route the shipment or exception to a dispatcher with the full reasoning trail attached, so nothing silently fails and every decision is reviewable after the fact.
Do you have logistics-specific proof, or is this adapted from other industries?
We have not yet shipped a production agent inside a logistics-specific company. What we can point to is the same coordination pattern applied elsewhere: a multi-agent system that evaluates capacity and dispatches jobs in real time for Mixam at a 95.4% routing success rate, and Synera's production agent platform, where generating one validated multi-step workflow went from about two hours to about three minutes. The mechanics (real-time constraint checking, multi-step orchestration, human escalation) transfer directly to load matching and shipment routing.
Start with one workflow
Map one routing or exception workflow worth automating
A 30-minute call identifies your system boundaries, escalation rules, and a realistic path to a working agent your dispatch team will trust.