--- title: "Multimodal Data Systems for Regulated AI | YPAI" url: https://ypai.ai/data-collection/ description: "YPAI builds multimodal data products and evaluation sets for high-stakes AI: real environments, consent verification, governance, domain-shift ready." source: "src/copy/routes (route /data-collection/)" --- # Benchmarks are recorded in quiet rooms. Your users are not in one. > YPAI builds multimodal data products and evaluation sets for high-stakes AI: real environments, consent verification, governance, domain-shift ready. Models that pass the benchmark and fail in production have a data problem Models that pass the benchmark and fail in production have a data problem. YPAI manufactures the multimodal training and evaluation data that exposes domain shift before it ships, with consent and provenance artefacts your conformity review can read. Built in Norway, on EEA infrastructure, under Norwegian jurisdiction. ## Four axes decide whether your data survives deployment You name the range your deployment has to hold. The programme is built to span it, and every sample lands with its coverage recorded. - Capture across heterogeneous hardware, because a model trained on one tier degrades on another. - The room, the vehicle, the street. Acoustics, light and interference are specified, not hoped for. - Stratified participant pools, so the long tail is represented rather than averaged away. - 150+ languages and their regional variants - Targeted capture of the cases a benchmark removes and production does not. Coverage is agreed before capture begins and delivered as part of the record. Where a required range cannot be covered, YPAI says so during scoping rather than after delivery. ## A benchmark covers one corner. A programme covers the grid Every cell is a named condition. Public benchmarks cluster in the quiet corner. Your programme is specified against the cells your deployment will actually meet, and ships the proof in the record. Coverage is recorded per sample and delivered in the record. ## Hear it in your own voice Record three seconds in your quiet room. The same take is transcribed clean, and again with the world mixed in at a chosen level. The words that survive are the difference training data has to cover. Runs in this tab, on your device Example shown until you run it. Illustrative, no customer data. ## Start from the data your model needs Six channels, one operation. Each line names what is captured and where its depth lives, and no modality ships without the same record behind it. ### [Speech and audio](https://ypai.ai/speech-data/) - Read and spontaneous speech at 48 kHz / 24-bit, in-cabin and far-field, code-switching, wake words, professional voice and TTS, with transcripts verified by native reviewers. ### [Video](https://ypai.ai/video-data/) - First-party video for tasks that depend on motion and sequence: driver monitoring, gesture and body pose, multi-camera synchronisation with documented lighting and occlusion. ### [Image](https://ypai.ai/image-data/) - Still-frame capture across lighting, viewpoint, device and demographic variation, collected to your specification with consent recorded per contributor. ### Text and document - Documents, parallel corpora and written interaction data across 150+ languages, produced by native speakers under documented rights. ### [LiDAR, 3D and sensor](https://ypai.ai/image-3d-sensor-data/) - LiDAR, radar, IMU, thermal and IoT streams with calibration-verified alignment and time-synced capture for ADAS, robotics and spatial AI. ### [Physical AI and robotics](https://ypai.ai/physical-ai-data/) - Demonstration and episode data for robots and embodied systems, indoor and outdoor, captured with stereo camera and RTK GPS rigs. - [Automotive in-cabin](https://ypai.ai/solutions/automotive/) - [Clinical and healthcare](https://ypai.ai/solutions/healthcare/custom-data-collection/) - [Geospatial](https://ypai.ai/geospatial-data-solutions/) - [Ready-made datasets](https://ypai.ai/audio/datasets/) Working with a modality not listed here? YPAI designs capture protocols across data types; describe the deployment and the constraint it runs under. ## Every sample arrives with its record One programme, four steps. Each step stamps the record that travels with the data, so every delivery arrives ready to inspect. ### Coverage specification - The four axes are turned into quotas: who, where, on what device, and how much of the edge. - **Quotas**: Per axis, agreed before capture - **Acceptance**: Criteria written before work starts ### Capture - Identity-verified contributors record under the specified conditions, consent per record. - **Consent**: GDPR Article 7, per record - **Provenance**: SHA-256 manifest per sample ### Quality gates - Automated checks plus human review against the agreed sampling and acceptance plan. - **Review**: Native reviewer on sampled work - **Verdict**: Accepted, or reworked until it passes ### Delivery - Raw and processed data with annotation layers, in the formats and location you named. - **Formats**: Raw, processed, annotation layers - **Destination**: S3, GCS, Azure or on-premise The architecture, formats, controls and delivery boundaries are described in full. ## Qualified contributors, not a crowd Each contributor is recruited, identity-verified and screened for the specific programme they record in. Programmes draw on a network of 210,000+ registered contributors; each programme uses only the contributors qualified for it. - **Recruited**: Sourced for the programme's languages, demographics and conditions - **Identity-verified**: Verified identity on file, consent per record - **Qualified**: Passes the programme's own screening tasks before production - **Calibrated**: Agreement tracked against reference work during production - **Reviewed**: Independent native review on sampled work ## The record your legal and security review will ask for Governance artefacts are produced by the operation as it runs. Depth and format follow your risk profile and are agreed during scoping. What your review will ask, and what is on file Where a person is in the record, consent travels with the sample. - **Legal basis**: What is the legal basis for each sample?. Consent, legitimate interest or contractual necessity, documented per sample - **Consent**: Can you show consent, per contributor?. GDPR Article 7, collected on the YPAI platform, withdrawable - **Provenance**: Where did each sample come from?. Per-sample manifest, aligned to EU AI Act Article 10 data governance - **Residency**: Where does the data reside, and under whose jurisdiction?. EEA by default, Norwegian jurisdiction; US or on-premise by arrangement - **Retention**: What happens to the data when the engagement ends?. Retention, withdrawal, erasure and closeout defined in the engagement - **Agreements**: What governs the engagement on paper?. DPA signed, SCCs available for cross-border transfer ## Every industry, four with dedicated depth Every programme inherits the same capture discipline, whatever the industry. Four run deep enough to carry their own collection pages; the rest start at the intake below. ### [Healthcare](https://ypai.ai/solutions/healthcare/custom-data-collection/) - Speech and imaging pipelines for clinical workflows, processed in the EEA by default. Radiologist-vetted annotation, consent-verified participant pools. - EEA processing by default, per-project residency controls ### [Automotive](https://ypai.ai/solutions/automotive/) - In-cabin voice, multilingual driver interaction, sensor-fused datasets for ADAS validation under cross-environment capture. ### [Education](https://ypai.ai/solutions/education/) - Multilingual learning content, accent coverage, accessibility datasets for K-12 and higher-ed applications. ### [Robotics and industrial vision](https://ypai.ai/annotation/robotics-industrial-vision-annotation-services/) - Defect detection, robotic-arm telemetry, manufacturing-floor edge cases. Sensor and vision multi-modal pipelines. ## From scoping to production dataset GDPR Article 7 · EU AI Act Article 10 · DPA included Technical assessment inside one EU business day. Enquiry details treated as confidential. ### Describe your use case - What modalities, environments, and constraints define your deployment? ### Technical assessment - We evaluate feasibility, define QA rubrics, and identify governance requirements. ### Pilot delivery - Small-scale data delivery to validate quality gates, formats, and integration. ### Production scale - Full dataset delivery with ongoing QA, versioning, and support. ## Consent, provenance, and audit readiness ### What governance artefacts can be delivered with a dataset? We can deliver documentation aligned to your risk profile: consent records, provenance logs, demographic breakdowns, QA audit trails, and data processing agreements. Format and depth depend on your compliance requirements. ### Can data be collected under a specific legal basis? Yes. We support consent-based collection, legitimate interest frameworks, and contractual necessity depending on jurisdiction and use case. Legal basis is documented per-sample. ### What data residency options are available? Primary operations are EU-based (Norway). We can arrange US residency or on-premise delivery for restricted deployments. Residency requirements are defined in the project scope. ### How is participant consent managed? Consent is collected through our platform with clear disclosure of data use, retention, and rights. Participants can withdraw, and we support downstream anonymisation or deletion requirements. ### Can YPAI sign a DPA or work under our existing agreements? Yes. We routinely sign DPAs and can operate under client-provided agreements where feasible. Standard Contractual Clauses (SCCs) are available for cross-border transfers. We will define the data product required for your deployment context and constraints. If YPAI is not the right fit, we will say so directly.