Join our free early access and secure yourself 1000€ in free compute credits.
From Raw Time-series Data to High-Quality Datasets
Transform messy driving logs into analysis-ready datasets through automated data preprocessing and annotation.
Smart Data Quality & Auto-Fix
Raw Signal. Issues Detected. Perfectly Cleaned.
Event Detection & Labeling
Every Event & Maneuver. Detected Automatically. Labeled Instantly.
Simplicity and flexibility win
01
Upload &
Map Signals
Load your dataset and match your signal names to a standardized schema. DataPilot suggests mappings automatically – you review and confirm.
02
Check Data
Quality
Automated checks run across every signal and flag issues with full detail – so you know exactly what failed and why.
03
Inspect & Fix
Drill into any signal, select a fix method, and preview the corrected result against the raw data before applying anything. Fix them in one guided session.
04
Label Events
Driving events and maneuvers are detected automatically. Just review the results.
05
Export
Get a validated, labeled dataset ready for your calibration or AI workflow.
Watch On-Demand Webinar: DataPilot Demo
Designed for teams working with vehicle time series data:
Vehicle Dynamics Engineers
Validate every measurement campaign instantly – no more discovering sensor errors days later.
Calibration & Validation Teams
Trust your reference measurements. Automated checks ensure signal reliability before you invest time in calibration.
Data Scientists & ML Engineers in automotive R&D
Train better models faster. Clean, event-labeled datasets with balanced splits across road surfaces and maneuvers.
Join Early Access
Help shape the next generation of automotive data tools.
Join our free early access and secure yourself 1000€ in free compute credits.
FAQ
Data preparation is unavoidable – but not all of it needs to be manual. The most time-consuming parts, like hunting for quality issues across hundreds of signals or building driving context from raw logs, can be handled significantly faster. DataPilot takes the tedious, repetitive work off your plate so your engineers can focus on the analysis itself.
DataPilot includes an automated matching step that maps your own signal names to a standardised schema. You can override suggestions manually.
You don’t have to. Every flagged issue shows what was detected and why it was flagged. Before any fix is applied, you see a preview of the corrected signal alongside the original. Nothing changes in your data without your explicit confirmation, and your raw files are never touched – cleaned output is always a separate version.
DataPilot detects driving events – braking, acceleration, cornering, and driving context such as road type and conditions – automatically from the signal data. Results are reviewable per trip before you export anything. The detection logic is transparent: thresholds are visible, not hidden inside a model.
On-premise and data-lake deployment is available for enterprise customers. Reach out directly to discuss deployment options for your organization.
DataPilot is built for the engineers who work directly with vehicle data – data scientists, test engineers, calibration engineers, and ML engineers who currently spend time writing scripts to clean and validate datasets before analysis.
We’re currently running a structured beta program with a limited number of organizations. Beta access includes full platform functionality, direct access to our team for feedback sessions, and no commitment. If you’re interested, apply through the beta program and we’ll schedule an onboarding session.
Pricing is not yet public. DataPilot is in active beta, and we’re working with early partners to define pricing that reflects actual time saved and data volume processed. If you need a commercial proposal for internal approval, contact us, and we’ll work through it together.
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