AI Intelligence
AI Intelligence: Increase the value of your real-time data with AI
A continuous stream of temperature and location, turned into decisions and action.
The core idea
Your real-time devices already capture a continuous stream of temperature and location
The Controlant AI journey turns that stream into questions answered, decisions made, and actions taken, and over time into a single command center.
Passive loggers
One reading, at the end of the trip
With Controlant
A continuous, enriched stream, the whole way
AI use cases
Your shipment data, put to work at every stage
The same real-time data answers different questions before a shipment leaves, while it moves, and once it has arrived.
Before the shipment
Plan it
During the shipment
Act on it
After the shipment
Learn from it
01 · Before the shipment
Mode and route risk
Rank routes from completed shipment history.
“Which of these two routes is actually riskier, and by how much?”
Ranks the routes to a site from completed shipment history: transit spread, excursion rate and where the failures sit.
01 · Before the shipment
Thermal packaging protection
The specification follows the season.
“Are we paying for more protection than this lane actually needs?”
Scores every qualified packaging type against each lane's own history, month by month, so the specification follows the season instead of the worst case.
Case story: the lane deep dive, PDF
02 · During the shipment
The morning risk briefing
Every shipment on the move, sorted by risk.
“This morning's risk across the active fleet, most urgent first, one action per line.”
Cold chain, disruption, delay and data-quality risk in one read, triaged, with an action against each.
01 · During the shipment
Customer service automation
Only the exceptions reach your desk.
“Where is my shipment, and did it arrive in spec?”
Answers the routine questions shippers and customers ask, straight from the live shipment record, and sends only the exceptions to your team.
03 · After the shipment
Root cause on lanes
Dwell times, variance and where excursions start.
“Where on this lane do we lose time and temperature?”
Breaks each lane down leg by leg from completed shipments: how long shipments dwell at every stop, how much transit times vary, and the points where excursions most often begin.
01 · After the shipment
Lane qualification
PQ evidence ready for QA to sign.
“Is this lane still qualified, and can we prove it?”
Builds the performance qualification from every completed shipment on the lane, with the limits read off the record, so QA signs off on evidence instead of a handful of test runs.
With product release switched on, each one sharpens. Risk stops being a percentage and becomes the hours of stability budget a route is likely to consume, a recovery plan counts units rather than shipments, and the briefing ranks by which product is closest to losing its allowance.
You do not need a data project first
Every AI answer on this page stands on four layers. At the base is the validated shipment record you already have. Above it, AI-ready data models and a semantic layer that knows what an excursion or your temperature limits mean to you. At the top, the AI tool where you ask, in plain language: your own or ours.
No new devices. No data project. No instrumentation.
GenAI tool
Layer 4
- Route and packaging simulation runs(controlant)
- Risk, cost and carbon scores(controlant)
- Morning risk briefing(controlant)
- Lane qualification report(controlant)
Semantic context layer
Layer 3
- MKT and transit p95 definitions(controlant)
- Alarm vs. excursion state(controlant)
- Product stability limits(internal)
AI-ready data models
Layer 2
- Lane model(controlant)
- Shipper and packaging model(controlant)
- Product class model(controlant)
Your validated shipment record
Layer 1
- Temperature profile(controlant)
- Position and route detail(controlant)
- Packaging and freight cost(internal)
- Airport weather history(external)
Two ways to use AI on your shipment data: your own tool or ours
Bring your own AI tool
MCP access to your AI-ready data models, queried straight from the assistant your team already uses.
Your own sources join the same conversation: ERP, quality, logistics.
Best if you already hold an enterprise AI agreement.
Access via AI Intelligence
We connect you to reasoning AI agents through the Controlant Control Tower. Nothing to procure or configure.
Same data models, same semantic layer, same answers.
Best if you want value in weeks rather than after an IT review.
Use case · lane deep dive
Have the biggest assumptions in your cold chain ever been tested?
One lane, worked through with the four layers above: where to look first, three ways to run the same lane inside your temperature limits, and what it saves. Based on a customer proof of concept.
Eight pages, PDF. We email it to your work address.
Your data stays governed the way it is today
Where does our data go?
Queries run through your own Controlant account and return only what that account may already see.
Who processes it?
With your own tool, your existing enterprise AI agreement governs it and we introduce no new processor. With ours, the agent runs under enterprise terms named in our DPA. Business data is not used for model training.
Is an AI answer a validated record?
No. Controlant remains your validated system of record. AI output is a draft prepared for human review.
Wondering what this would look like on your lanes?
We'll walk through your routes, your products, and where deviations are costing you most.