YPAI 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 residency by default · 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 labelling for driving scenarios, objects and behaviours, 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.
A managed data operation, accountable for every accepted batch.
Scope an automotive programme
Road, weather, cabin acoustics and sensor conditions change what must be collected and how it is judged.
For work YPAI manages, our collection 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. You can keep your own tools; native-speaker review stays with YPAI.
Support for automotive AI development, from data collection through deployment.
Validation and testing against the driving scenarios agreed for the programme
3D point-cloud and sensor-fusion annotation
Labelling of driving scenarios, objects and behaviours for ML training
3D data labelling for depth perception and spatial understanding
Synchronised multi-sensor data annotation for environmental perception
Frame-by-frame labelling for dashcam analysis, parking assistance and driver monitoring
Labelling for object detection, lane recognition and traffic-sign classification
Real-world driving data capture across conditions, edge cases and scenarios
QUESTIONS
In-cabin voice, perception annotation and evaluation, in one delivery: wake-word, intent, multilingual command and cabin-noise data; image, video and 3D labelling for driving scenarios; and point-cloud, cuboid and time-synchronised multi-sensor annotation when the brief extends into the perception stack.
At one of three points: build a new voice or perception corpus for a new market, cabin profile or sensor suite; evaluate and remediate an existing system or dataset with known failure conditions; or run controlled production against a defined brief.
Native speakers recruited and recorded in the required language markets, under the drivers, passengers, road surface, weather and sensor suite the brief names.
In the unit that matters to the programme: accepted speaker-hours, recordings, frames, sessions or assets. Qualification and calibration come first, and the accepted batch sets the working forecast.
YPAI is a Norwegian company. Data stays in the EEA by default, a GDPR Article 28 DPA comes with every engagement, and processing and residency options are scoped per engagement.
Yes. The operating model can use YPAI-managed infrastructure, your systems or a hybrid, with API access and custom integrations. Native-speaker review stays with YPAI.
AUTOMOTIVE INTAKE
Bring the programme 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
Contribute
We recruit contributors for this kind of work continuously. Openings are listed per country, with the rate stated in the country's own currency.
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