AI data, evaluation and implementation under one accountable delivery model.
Contact us Become a ContributorYPAI scopes in-cabin voice, perception data, and evaluation under one delivery. Native-speaker speech. Camera, LiDAR and sensor-fusion annotation. Acceptance criteria written before production.
Norwegian legal entity · EEA-based processing available · DPA per engagement
Languages available through the YPAI contributor and specialist network
3D point-cloud and sensor-fusion annotation for ADAS and autonomy programmes
Speaker recruitment and recording in the required language markets
Processing and residency options scoped per engagement, with a DPA
Evaluation, collection and annotation work across OEM and Tier-1 programmes. Relationship and scope vary by engagement.
YPAI scopes the people, vehicles, sensors and conditions that decide whether automotive AI works outside the lab.
Start with in-cabin voice, perception annotation, or evaluation. The same delivery can carry supporting modalities when the brief requires it.
Voice and perception pathways
Wake-word, intent, multilingual commands and cabin-noise data. Native speakers in the required markets.
Scope the programme around the actual cabin, sensor suite and acceptance criteria, then attach the evidence trail the buyer needs to review.
Image, video and 3D labeling for driving scenarios, objects and behaviors, against the project taxonomy.
Sensor and delivery pathways
Market, sensor suite, rights and acceptance requirements turn this scope into a programme specification.
Three ways to enter the same controlled delivery.
For a new market, cabin profile, sensor suite or vehicle experience.
Turn scenarios, speaker or sensor requirements, and output specs into a capture and annotation plan, then deliver an accepted corpus with the agreed metadata and labels.
For a system or dataset with known failure conditions.
Use model outputs and failure cases to isolate gaps by market, cabin condition or operating domain, then define the right evaluation, review, re-annotation or targeted collection.
For a defined production brief, ready to execute.
Turn the specification, rights model and acceptance criteria into qualification, calibration, production, QA, review, rework and accepted batches.
The delivery plan takes shape around the actual work.
Align participant or asset profiles, technical setup, review criteria and capture protocol before production begins.
Prove the specification against submitted material, then use the accepted batch to set the working forecast.
Capture, review, rework and replacement coverage move together against the programme brief.
The forecast uses the unit that matters to the programme: accepted speaker-hours, recordings, frames, sessions or assets.
AI DATA & EVALUATION
Managed collection, annotation, validation and evaluation, with the evidence trail attached.
Not a contributor marketplace. A managed data operation with accountable delivery.
Scope an automotive programme
Road, weather, cabin acoustics and sensor conditions change what must be collected and how it is judged.
For relevant YPAI-managed work, YPAI's Data Collection and Assurance Platform connects:
Reviewers and operations teams see the same task status and version history.
Failed material returns for recapture, rework, replacement or rejection according to the project rules.
Accepted material moves into versioned batches, manifests and secure delivery.
Role-based dashboard and client-portal access can be provided for YPAI-managed work.
The operating model can use YPAI-managed infrastructure, customer systems or a hybrid. API access and custom integrations are available on a project-specific basis. Third-party tools can be adapters; YPAI's native human review remains platform core.
End-to-end support for automotive AI development from data collection through deployment, powering the future of intelligent transportation.
Comprehensive testing frameworks ensuring safety and reliability across all driving scenarios
Precision 3D environmental mapping combining multiple sensor inputs for superior perception
High-precision labeling of driving scenarios, objects, and behaviors for ML training
Specialized 3D data labeling for accurate depth perception and spatial understanding
Synchronized multi-sensor data annotation for comprehensive environmental awareness
Frame-by-frame labeling for dashcam analysis, parking assistance, and driver monitoring
Precision labeling for object detection, lane recognition, and traffic sign classification
Real-world driving data capture across diverse conditions, edge cases, and scenarios
AUTOMOTIVE INTAKE
Bring the program type, primary use case, and any regulatory or homologation deadline. A named project lead replies within one EU business day with a feasibility read.
GDPR Article 28 · EU AI Act Article 10 · EEA jurisdiction
