Trusted AI for Transportation
Transportation and logistics companies run AI in fleet maintenance, route optimization, and safety compliance, where DOT inspectors and insurance carriers can investigate every decision. Seekr® gives safety, operations, and compliance teams continuous observability, plain-language explainability, and a defensible audit trail for every decision.
How Seekr works for transportation
From fleet maintenance through ongoing model monitoring, Seekr gives safety, operations, and compliance teams the evidence they need at every stage of the AI lifecycle.
Prepare and govern your data
Turn telematics streams, ELD records, maintenance logs, and cargo management data into training-ready datasets with full lineage. Your data stays in your environment, and every preparation step is logged, so you can show investigators exactly what the model learned from.
Select and certify models
Compare candidate models side by side against your own fleet data, safety thresholds, and operational scenarios. Score for accuracy, safety margins, and failure modes before any model touches a production decision, so the model you approve is the one you can defend.
Deploy with full provenance
Every output carries a confidence score and a source trail that traces the answer back to the data, the reasoning, and the model version that produced it. When a DOT inspector or accident investigator asks how you reached a maintenance or routing decision, the evidence is already assembled.
Monitor and re-evaluate
Detect model drift as routes change, fleet composition evolves, weather patterns shift, or regulations tighten. Retest against the same criteria on a continuous basis, and maintain a documented record of performance, risk, and change over time.
Core capabilities
Know why maintenance calls were made and routes chosen, catch failures before production, and track safety-margin drift as routes and fleets change, with right-sized models.
Model-agnostic explainability
Trace any output to its data and reasoning. When inspectors ask why a maintenance call was made or a route chosen, show what drove it.
Quantified model risk
Define risk profiles for your fleet types, routes, and regulations. Score models against them and catch failures before production.
Flexible deployment
Run in Seekr’s cloud, yours, on premises, or air gapped. Each is designed to keep telematics, driver records, and operations data in your infrastructure.
Continuous evaluation and drift detection
Routes shift, fleets evolve, and regulations tighten. Detect drift in accuracy, safety margins, and risk posture, with audit-ready evidence over time.
Contestability
When your team sees why a decision was made, they can challenge it, correct it, and confirm the fix, with every intervention documented.
Predictable cost structure
Match model and compute to each workload, with evidence to defend every choice. The leanest model that meets requirements keeps costs predictable.
Seekrflow is the AI operating system for transportation
Launch prebuilt transportation solutions instantly or create your own on a robust, extensible architecture built for scale. SeekrFlow enables full visibility and control over how AI learns, reasons, and delivers results in modern transportation environments.
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Deploy Seekr anywhere
Seekr cloud
Fully managed in the Seekr cloud. Fastest path to production for carriers and logistics operators piloting AI on routing or maintenance workflows
Fully managed in the Seekr cloud. Fastest path to production for carriers and logistics operators piloting AI on routing or maintenance workflows
Your cloud
Deploy in your AWS, Azure, or GCP environment. Telematics and fleet data stay in your tenancy under your controls.
Deploy in your AWS, Azure, or GCP environment. Telematics and fleet data stay in your tenancy under your controls.
On-premises
Deployment for operators that require physical control over fleet management systems, ELD data, or air-gapped environments.
Deployment for operators that require physical control over fleet management systems, ELD data, or air-gapped environments.
At the edge
Deploy in the fleet cab, at the distribution center, or in the maintenance facility where connectivity constraints require processing closer to the asset.
Deploy in the fleet cab, at the distribution center, or in the maintenance facility where connectivity constraints require processing closer to the asset.
FAQ
How does Seekr help transportation companies defend AI-powered decisions?
Seekr traces every AI output to the data, reasoning, and model version that produced it, creating an evidence trail that maps to what inspectors and investigators ask for. Instead of reconstructing why a maintenance decision was made after an incident, your team produces the provenance at the time of the decision. This covers fleet maintenance, route selection, driver risk scoring, and any workflow where an inspector, an auditor, or an investigator needs to see how the answer was reached.
Can Seekr run AI on telematics and fleet data without that data leaving the corporate boundary?
Yes. Seekr deploys in your cloud, your data center on premises, or air gapped. Telematics streams, ELD records, driver behavior data, and maintenance histories stay in your infrastructure, and your data is never used to train another organization’s models. You choose the deployment model, and Seekr runs where your data already lives.
How do we evaluate whether an AI model is safe to use for critical decisions?
Seekr lets you compare candidate models side by side against your own data, scenarios, and safety thresholds rather than relying on generic benchmarks or vendor claims. You define what matters (maintenance prediction accuracy, false positive rates on safety alerts, route compliance under adverse conditions) and score models against those criteria before any model reaches a production decision.
What happens when operating conditions change and a model has to change with it?
Seekr monitors for drift in accuracy, safety margins, and risk posture on an ongoing basis and flags when a model’s behavior deviates from the criteria it was approved against. You retest against the same framework you used during initial evaluation, which creates a documented record of performance over time. When conditions change, you re-evaluate, and the evidence record stays continuous.
How is Seekr different from observability tools we already use?
Observability tools record what happened: which sensors were read, which algorithms ran, how long each step took. That’s useful for debugging. Seekr goes further by answering what influenced the outcome, tracing which inputs materially drove the output rather than logging system activity. In an FMCSA audit, an accident investigation, or an insurance claim, the difference between showing your workflow and showing your evidence is the difference between describing a process and defending a decision.
Make every fleet and safety decision one you can defend
See how Seekr works for transportation safety, operations, and compliance in a 30-minute technical briefing.