# Build the intelligence behind every journey.

> Evaluate, monitor, and govern AI across automotive and transportation operations, with performance, safety, and compliance continuously measured.

## By the numbers

| Stat | Meaning |
| --- | --- |
| 70% | of transportation and logistics companies have adopted AI (Source: Penske Transportation Leaders Survey, 2025) |
| 95% | of automakers are making significant investments in AI (Source: Gartner research reported by Reuters, 2025) |
| 23% | of automotive software professionals report repeated exposure to code vulnerabilities (Source: Perforce State of Automotive Software Development, 2026) |

## AI is making operational decisions faster than teams can verify them.

AI now influences routing, maintenance, production quality, inventory, warranty decisions, and customer operations.

But performance can change as data, models, suppliers, and operating conditions evolve. Most companies lack one consistent way to test these systems, monitor them in production, and trace every outcome to the model and version behind it.

### Operational performance changes over time.

Forecasting, routing, maintenance, and quality models can lose accuracy as demand, assets, suppliers, and production conditions change.

### AI is fragmented across teams and suppliers.

Internal models, embedded vendor AI, and third-party tools operate without one inventory, evaluation standard, or approval process.

### Failures are difficult to trace and correct.

When AI contributes to a delay, defect, unnecessary repair, or customer issue, teams struggle to identify the model, version, data, and decision behind it.

## Transportation companies are putting AI at the center of operations.

AI is routing fleets, forecasting demand, scheduling maintenance, and managing critical workflows. When it fails, errors can spread across an entire operation.

ISO 26262, UNECE and NHTSA expectations, NIST AI RMF, and the EU AI Act are raising the standard for documented, ongoing evidence of AI behavior. EU AI Act penalties can reach 7% of global revenue.

- EU AI Act
- ISO/IEC 42001
- NIST AI RMF
- ISO 26262

## Continuous assurance for the AI running your operations

Openlayer connects evaluation, production monitoring, governance, and evidence across every model and version. Catch regressions before release, detect changing performance in production, and prove each system remains accurate, reliable, and within policy.

## What you get

### Fleet and route optimization

Evaluate routing, dispatch, load planning, and fuel optimization across fleets, regions, and changing demand.

### Predictive maintenance

Test failure predictions and maintenance recommendations against real asset performance and service outcomes.

### Manufacturing and quality

Monitor computer vision, robotics, and production AI for detection accuracy, defects, failures, and performance drift.

### Supply chain forecasting

Track demand, inventory, sourcing, and planning models as suppliers, markets, and production requirements change.

### Customer operations

Test diagnostics, service assistants, warranty tools, and customer communications for accuracy, privacy, and successful resolution.

### Supplier and third-party AI

Track every internal and vendor model with its owner, approved use, risk, evaluations, version, and production evidence.

## Keep every operation moving forward.

Keep performance, reliability, and compliance measurable across every fleet, facility, workflow, and AI system.