In-cabin AI data built for
how drivers actually speak.
Studio recordings do not survive a moving vehicle. We collect speech in real acoustic environments, annotate intent across 155+ locales, and benchmark voice models against the conditions they will actually face.
Where automotive AI programs fail before launch.
In-cabin voice models and AV perception systems share the same data failure mode: training data that does not reflect deployment conditions. For voice, that means studio audio that collapses under road noise. For AV, that means sensor datasets collected in controlled environments that do not generalize to real roads.
Speech data collected in studios, not vehicles
Clean-audio voice models fail under real in-cabin conditions: road noise, HVAC interference, multiple speakers, varying speeds, and window state. Without acoustic environment diversity in training data, speech recognition failures are baked in before the model ships.
Dialect and accent coverage gaps
A global vehicle platform deployed across 50+ markets requires speech recognition that works for every driver. Accent and dialect gaps produce recognition failures in specific markets that erode driver trust and, for safety-critical voice commands, create real risk.
Intent taxonomies built for consumer devices, not vehicles
Climate control, navigation, infotainment, and vehicle diagnostics require automotive-specific NLU intent structures. Consumer virtual assistant datasets do not contain the command patterns, multi-turn sequences, and error recovery flows that in-cabin AI requires.
Use cases for automotive AI teams.
In-Cabin Speech Data Collection
Speech collected in actual vehicles and controlled acoustic environments with configurable conditions: engine state, HVAC level, radio interference, occupancy, road speed, and speaker distance. Includes multi-speaker interaction scenarios capturing driver-passenger interactions, cross-language conversations, and real-world usage patterns. Covers 155+ locales with systematic age, accent, and dialect stratification.
Automotive NLU Intent Annotation
Natural language command annotation across the full vehicle control domain: climate, navigation, infotainment, diagnostics, and communication. Covers multi-turn dialogue sequences, error recovery paths, and ambiguous command resolution.
In-Cabin Dialogue and Personalization Data
Training data for context-aware multi-turn in-cabin conversations, driver-persona adaptation, and proactive AI responses based on route, time, and occupancy context.
RAG Validation Against OEM Documentation
Retrieval-augmented generation evaluation verifying that AI responses to vehicle queries are grounded in owner manuals, system reports, and OEM technical specifications across multi-language documentation.
In-Cabin Voice Model Benchmarking
Accuracy testing under real-world automotive acoustic conditions, multilingual benchmarking across accent and dialect cohorts, edge case evaluation for safety-critical command misinterpretation scenarios, and model optimization benchmarking for efficient performance within automotive edge compute constraints.
Adversarial Testing and Voice Safety Compliance
Identification of voice spoofing vulnerabilities, safety-critical command misinterpretation risks, and adversarial prompt scenarios. Structured to support automotive-grade functional safety requirements.
Automotive data types we handle.
In-Cabin Speech and Audio
Vehicle recordings across 155+ locales with systematic acoustic environment variation: engine states, HVAC interference, radio noise, multi-occupancy, varying road speeds, and speaker distances. Multi-speaker scenarios include driver-passenger interactions and cross-language conversations. Age, accent, and dialect stratification by design.
Dialogue and Conversational Data
Multi-turn driver interaction transcripts, intent-labeled command sequences, and error recovery annotations for in-cabin conversational AI training across global vehicle platforms.
OEM Text and Documentation
Owner manuals, technical service documentation, and system reports annotated and structured for RAG validation, model grounding, and diagnostic AI training across multilingual vehicle variants.
AV Sensor and Perception Data
LiDAR point clouds, camera feeds, radar data, and sensor fusion datasets annotated with bounding boxes, segmentation masks, and 3D object labels across diverse driving environments.
Four reasons automotive AI teams choose Welo Data.
Speech collected in vehicles, not booths.
Our data collection protocols are built around automotive acoustic environments, not adapted from general speech collection. We deploy in actual vehicles with configurable acoustic conditions, and apply systematic stratification across age, accent, dialect, and occupancy variables to every program.
ISO 26262-aligned programs with full data governance.
Every automotive program operates under ISO/IEC 27001 data security certification and ISO 26262 functional safety alignment. GDPR-compliant data handling governs all recordings and associated metadata from collection through delivery.
NIMO identity assurance for high-scale speech collection.
Speech collection at scale is a high-identity-risk operation. Our NIMO platform applies continuous identity verification, behavioral monitoring, and output quality management to every contributor across every collection session.
In-cabin NLU at 155+ locales with automotive-domain linguists.
We staff in-country automotive linguists who understand regional command preferences, automotive terminology, and driving context in their native language. We do not translate English NLU guidelines across markets.
What automotive AI buyers ask us.
Yes. We collect in actual vehicles and controlled acoustic environments with configurable conditions: engine state, HVAC level, radio presence, occupancy, and road speed. Every program applies systematic variation across these variables to produce deployment-representative data.
Climate control, navigation, infotainment, vehicle diagnostics, communication functions, and emergency command handling. Intent taxonomies are built around OEM-specific command structures and adapted per locale for regional command preference patterns.
155+ locales and 200+ dialects with in-country linguists who have native-language automotive context. We build each locale from ground-level linguistic expertise, not translated English command structures.
Targeted language programs reach first data delivery within 3 to 4 weeks from scoping. Large multilingual programs spanning 20+ languages reach full production capacity within 2 months using our pre-screened in-country contributor pools.
Yes. We annotate LiDAR point clouds, camera feeds, radar data, and sensor fusion datasets with bounding boxes, segmentation masks, and 3D object labels. Programs are designed to cover diverse driving environments and weather conditions.
In-cabin AI that works in the real world.
155+ locales. Real acoustic environments. Automotive-domain NLU built for vehicles, not adapted from consumer devices.