Moving people and freight with forecasting that holds up operationally.
Transport runs on thin margins and tight schedules. We turn vehicle, network, and ridership data into plans dispatchers and planners can use, and secure the connected systems they depend on.
Problems we solve in transportation
The questions transportation teams bring us, what is usually behind them, and what we do about it.
“Why are our vehicles in the shop instead of on the road?”
The problemBreakdowns drive downtime, and maintenance is scheduled by mileage rather than condition.
How we solve itPredictive maintenance, fuel analytics, and utilization models built on vehicle telemetry.
“How many riders will we have, and where?”
The problemService plans lag behind changing ridership, so some routes run empty while others overflow.
How we solve itRidership forecasting and real-time tracking analytics that feed directly into service planning.
“Where are we losing time and money in the supply chain?”
The problemInventory, warehouse, and last-mile decisions are made in separate systems with separate numbers.
How we solve itDemand forecasting, warehouse and last-mile planning, and supplier performance analytics on one data foundation.
“Are our connected vehicles and networks secure?”
The problemEvery sensor and telematics unit added to the fleet is another way in.
How we solve itVehicle and IoT network security, data privacy in transit, and incident response planning.
Each of those answers depends on data someone can trust. Here is how we make it trustworthy in transportation.
Governed from the first pipeline
Data engineering, analytics, and AI built in order, on governance designed for this sector’s rules rather than retrofitted to them.
Data governance in transportation
Vehicle and rider data can locate a person. It is handled that way.
- Rider and driver location data de-identified wherever identity is not needed.
- Telematics retention set deliberately, not left at a vendor default.
- Safety analytics documented well enough to stand up in an incident review.
- NIST Cybersecurity Framework
- Title VI equity analysis
- State privacy law
The method is the same everywhere. The gates are what keep it honest.
Phase by phase, starting small
Each gate is agreed before its phase begins, so nothing scales on optimism.
Assess
Inventory the data and systems, and pick the one problem worth solving first.
Gate: First problem and success measure agreedPilot
Solve it end to end at limited scope, measured against the threshold set in Assess.
Gate: Threshold met, or pilot stoppedDeploy
Roll out with training and handover, inside your security perimeter.
Gate: Your team running itExtend
Add the next problem on the same data foundation and governance.
Gate: Ongoing
A good first pilot
Predictive maintenance for one depot’s fleet, or a ridership forecast for one corridor.
Go deeper
Everything we do in transportation
Fleet & logistics
- Route planning, predictive maintenance, and fuel analytics
- Driver behavior and fleet utilization
Supply chain
- Inventory and demand forecasting
- Warehouse automation and last-mile planning
- Supplier performance analytics
Autonomy & public transit
- Vehicle data collection, sensor fusion, and safety analytics
- Regulatory compliance tracking
- Ridership forecasting, real-time tracking, and rider experience analytics
Security & sustainability
- Vehicle and network cybersecurity, IoT protection
- Data privacy in transit and incident response
- EV fleet planning, carbon tracking, and green logistics
Tell us which route, depot, or lane costs you the most.
We will look at the data you already collect and show you where to start.
Other industries
- EnergyGrid-scale analytics and security for utilities under load and under scrutiny.
- Public SectorEvidence for policy decisions, and services residents can actually reach.
- HealthcareClinical and operational analytics that respect the compliance perimeter.
- Financial ServicesRisk, reporting, and controls that survive an audit.
- RetailDemand, inventory, and customer signals joined into one view.