Real-world data systems for auditable AI
YPAI manufactures multimodal data products and evaluation sets designed for domain shift, regulated environments, and deployment inside your security perimeter.
This capability layer connects into sovereign model deployment and agent governance when required.
What breaks outside the benchmark
Standard training data fails when deployment conditions diverge from collection conditions. We manufacture data products that anticipate and cover this gap.
Domain shift is the real bottleneck
Models trained on clean inputs degrade when microphones, cameras, lighting, acoustics, and workflows change in production. We build datasets that expose and cover this variance before deployment.
Long-tail human variance
Real users differ from benchmark speakers. We capture demographic, dialectal, and behavioral diversity.
Regulated data constraints
Some deployments cannot use public data. We manufacture first-party datasets with audit trails.
Evaluation before deployment
Standard benchmarks mask production failure modes. We design evaluation sets that reflect your actual operating conditions, not sanitized lab environments.
Data products across input types
Audio, video, documents, and sensor data captured under controlled variability with consent verification and governance artifacts.
Speech data built for real conditions
Capture speech in the environments where systems fail: in-vehicle noise, reverberant rooms, far-field pickup, accented speakers, emotional states. Multi-device recording captures microphone variability.
- In-vehicle and in-cabin audio
- Far-field and close-talk capture
- Multi-accent, multi-dialect pools
- Emotion and speaking style variation
From specification to production delivery
Each engagement produces a defined data product: coverage specification, collection execution, delivery formats, and optional evaluation sets.
Coverage specification
- - Demographic matrix Age, gender, accent, dialect distributions
- - Environment conditions Noise types, lighting, device profiles
- - Edge case allocation Quota for low-resource segments
Collection execution
- - Consent and provenance GDPR-aligned, per-sample audit trail
- - Multi-device capture Synchronized cross-device recording
- - QA pipeline Automated + human review gates
Delivery formats
- - Raw and processed Originals plus ML-ready features
- - Annotation layers Transcripts, labels, bounding boxes
- - Integration support S3, GCS, Azure, on-prem delivery
Evaluation sets
- - Domain-shift benchmarks Expose production failure modes
- - Held-out segments Reserved speakers, environments
- - Regression tracking Versioned sets for iteration
Evaluation sets designed for domain shift reveal failure modes that standard benchmarks hide. We can build held-out test data that reflects your actual deployment conditions.
Evaluation that reflects production
Cross-device evaluation
Device variabilityTest performance across the microphone, camera, and sensor variants your users actually have.
Cross-environment test sets
Environment shiftEvaluate in the acoustic and visual conditions where lab-trained models degrade.
Demographic coverage analysis
Fairness testingVerify performance equity across age, accent, dialect, and behavioral segments.
Adversarial conditions
Robustness testingEdge cases, corrupted inputs, and stress scenarios that break production systems.
Temporal drift detection
Drift monitoringHeld-out data from different time periods to detect model staleness.
Regression test suites
CI/CD integrationVersioned evaluation sets for tracking model performance across iterations.
Consent, provenance, and audit readiness
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.
Yes. We support consent-based collection, legitimate interest frameworks, and contractual necessity depending on jurisdiction and use case. Legal basis is documented per-sample.
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.
Consent is collected through our platform with clear disclosure of data use, retention, and rights. Participants can withdraw, and we support downstream anonymization or deletion requirements.
Yes. We routinely sign DPAs and can operate under client-provided agreements where feasible. Standard Contractual Clauses (SCCs) are available for cross-border transfers.
For governance questions, contact: [email protected]
Multimodal Data Systems
Real environments, real variance, defined QA
Sovereign AI Infrastructure
Deploy models inside your perimeter when required
Agentic Systems & Governance
Audit trails and HITL gates for high-stakes workflows
Need an integrated deployment? We connect data products to sovereign model hosting and governed agent workflows.
From scoping to production dataset
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.
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.
Talk to an engineerFor procurement and engineering
Frequently asked questions
What kinds of data does YPAI collect?
YPAI manufactures multimodal data products: speech and audio corpora (read, scripted, spontaneous, in-cabin, clinical), image and video sets, sensor traces (LiDAR, radar, IMU) for automotive, OCR-grade scanned-document corpora, and dialogue or tool-use traces for agentic-AI fine-tuning. Each engagement starts from the deployment context (devices, environments, domain shift) rather than a fixed catalog. The goal is data that survives covariate shift in production, not pretty internal benchmarks.
Is YPAI data collection GDPR-compliant?
Yes. Every collection runs on an explicit lawful basis (consent, contract, or legitimate-interest assessment) with informed-consent flows, identity-of-controller disclosure, and Article 35 DPIA scaffolding. Recordings ship with per-subject consent receipts and revocation hooks so subject-rights requests (access, erasure, restriction) resolve inside the GDPR 30-day window. See ethical framework for the underlying governance.
How many languages and dialects can YPAI collect?
YPAI has delivered across 150+ languages and dialects, anchored on European and Nordic markets with significant coverage of major Asian and Latin American languages. Each collection is recruited natively (no synthetic dubbing, no MT-back-translated prompts) so phonetic and prosodic distributions reflect production speakers. Coverage maps and locale-specific recruiter pools are part of the scoping document.
Does YPAI collect in noisy or in-cabin environments?
Yes. In-cabin automotive speech (driver, passenger, multi-occupant), industrial floor environments, retail point-of-sale, and clinical examination rooms are recurring formats. Collection rigs include consented device variety (smartphone, far-field array, headset, in-cabin OEM mic) so the resulting corpus reflects the acoustic distribution the deployed model will actually see in production.
How does YPAI document data provenance?
Every record ships with a provenance trail: collection date, locale, recruiter pool, device class, consent identifier, and any post-processing applied. The bundle is structured for EU AI Act Article 10 dataset documentation and slots directly into a Technical File. Provenance is part of the deliverable, not a request-only artefact.
Can YPAI run collections inside our security perimeter?
Yes. Sovereign-collection projects ship into customer-controlled environments (EU regional cloud, on-prem rack, customer VPN) with separation of tooling and data. The collection app, annotation tooling, and storage can run inside the customer perimeter for defence, healthcare, and public-sector engagements where third-party processors are not acceptable. Specific feasibility is confirmed at scoping.
How long does a typical data-collection engagement take?
Pilot collections (single-locale, scripted, a few hundred speakers) typically run inside a quarter from kickoff. Multi-locale, multi-device, or in-cabin engagements run on a 3-6 month cadence because the limiting factor is consented speaker recruitment, not recording capacity. Timelines are committed at scoping after the locale and device matrix is fixed.
Does YPAI sell off-the-shelf datasets?
YPAI manufactures bespoke data on customer demand and does not run an off-the-shelf marketplace. The reason: production AI failure modes (covariate shift, locale drift, device drift) surface from collection design choices that pre-built datasets typically obscure. Engagements start at /contact-us/ with a project brief.