Spec Lock
Languages, quotas, metadata schema, and acceptance criteria.
The Infrastructure
for Sovereign AI.
We engineer consent-verified speech datasets for regulated enterprises. Replacing grey-market scraping with documented, audit-ready provenance aligned to the EU AI Act.
210,000+
Contributors
100%
Verified Consent
150+
Languages
EU/GDPR
Sovereignty
Why General Models
Fail in Production
Off-the-shelf datasets lack the acoustic and linguistic nuance required for real-world deployment. The gap between "sample pack" quality and production reality drives WER spikes.
Impact on WER
+15% to +40%
"When systems move toward production, 'good enough audio' becomes expensive fast."
Demographic Bias
Models trained on standard US/UK distributions fail on Swiss German, regional accents, and non-native speakers.
Acoustic Mismatch
Studio recordings do not generalize to noisy in-cabin, street, or far-field environments.
Invalid Consent
Web-scraped or grey-market data blocks legal clearance for commercial deployment.
Metadata Void
Unlabeled audio cannot be filtered for specific edge cases or bias correction.
Competitive Clarity
Why Teams Replace Generic Audio Vendors
Most vendors optimize for volume. YPAI is built for production reliability and regulatory clearance.
| Capability | Generic Vendors | YPAI Control |
|---|---|---|
| Consent Lineage | Partial or aggregated | Per-record, verifiable consent |
| Dialect Coverage | Standard distributions | Swiss German, UK regional, Code-switching |
| Collection Method | Browser tools / crowds | Proprietary collection app |
| Acoustic Realism | Studio-biased | In-car, street, far-field |
| Metadata Depth | Minimal / optional | Rich JSON sidecars |
| Audit Readiness | Ad-hoc documentation | Included with every delivery |
| Sovereignty | US-exposed | EU-resident delivery available |
Result: Lower WER volatility, faster production approval, and fewer late-stage blockers.
YPAI Control Platform
Controlled Delivery Architecture
This is not generic sourcing. It is a controlled, documented engineering process designed for ML teams.
Proprietary Collection App
Standardized capture workflows, guided prompts, and built-in acoustic validation. We control the recording chain from device to cloud, ensuring uniform quality across thousands of hours.
iOS Native Android Native WebAssembly
ACTIVE
210,000+ Contributors
Verified contributors enable demographic targeting and longitudinal continuity.
ID Verified
Native Speaker Checks
Acoustic Control
Define quotas by language, region, device type, and environment.
Metadata Schema
Rich JSON sidecars with device info, SNR logs, and speaker demographics.
Sovereign Delivery
EU-resident options available. Fully aligned with EU AI Act requirements.
Technical Specifications
Collection Capabilities
We capture the edge cases your model misses. From specific regional dialects to high-noise acoustic environments, every dataset is engineered to your exact SNR and linguistic requirements.
delivery_manifest.json
Standard Delivery Manifest
- Audio (WAV/FLAC 48kHz)
- Rich JSON Metadata
- Speaker Demographics
- Environment/Device Tags
- QA Verification Reports
ASR & Voice Command
Wake words, keywords, command-and-control, and domain vocabulary. Precision recording for trigger phrase optimization with controlled SNR.
Wake Words Cmd & Ctrl Domain Vocab
Conversational Speech
Natural dialogues, turn-taking, and multi-speaker interactions. Simulating real human-to-human or human-to-agent interaction flows.
Multi-Turn Dyadic Overlaps
Multilingual & Accented
European regional accents, dialects, and real code-switching scenarios. Fixing the "standard distribution" bias (e.g., Swiss German, UK Regional).
Code-Switching Regional Dialects Non-Native
Complex Environments
In-car, street, public spaces, office, and home conditions. Capturing the noise floor, reverb, and acoustic reflections of real usage scenarios.
Far-Field In-Cabin Street Noise
Specialized Industry Verticals
Automotive
In-cabin command, road noise profiles.
Healthcare
Clinical dictation, patient flows.
Finance
Biometric auth, fraud detection.
Methodology
How Quality Is Defined
and Verified
Quality is not subjective. It is measured, documented, and enforced. We ensure predictable performance when models move from lab to production.
Collection-time controls
Real-time Signal-to-Noise Ratio (SNR) thresholds, silence detection, clipping prevention, and environment validation per project.
Dataset-level validation
Speaker balance against defined quotas, accent/locale distribution checks, and environment coverage verification.
Acceptance criteria
QA pass/fail thresholds defined before collection. Re-recording triggered automatically when criteria are not met.
ypai_qa_protocol.sh
~/ypai-verification run --validation-suite
[INFO] Checking SNR thresholds...
PASS (>25dB)
[INFO] Verifying speaker distribution...
BALANCED
[INFO] Detecting silence/clipping...
CLEAN
[INFO] Validating consent_id chain...
VERIFIED (35,402)
~/ypai-verification generate --manifest-report
Generating QA_Report_v4.2.json... _
Status: AUDIT_READY
Latency: 12ms
GDPR
Aligned DPAs
EU AI Act
Article 10 Ready
EEA
Data Residency
30-Day
Erasure SLA
Trust & Governance
Security, Compliance,
Security, Compliance,
and Risk Control
Designed for regulated and high-risk deployments. We assume every dataset will be audited by legal teams.
Explicit Consent
Recorded per project requirements with clear scope. No grey-market data.
GDPR-Aligned Workflows
Privacy-by-design, right to be forgotten support, and localized storage.
Audit-Ready Documentation
DPAs available. Dataset versioning and provenance logs included with delivery.
RISK CONTROL: Anonymization protocols applied where required by local jurisdiction.
Scale & Operations
Enterprise Delivery Model
Built for teams deploying across markets. This is a dedicated service engagement, not a self-serve product.
-
Dedicated Account Ownership
Direct access to project managers who understand ML requirements and collection logistics.
-
Predictable Timelines
Project-specific schedules with transparent milestones and weekly reporting.
-
Written Acceptance Criteria
QA thresholds (WER/SNR) and acceptance definitions locked in contract before collection starts.
-
Iterative Delivery & Refresh
Support for model feedback loops, gap re-collection, and locale expansion using the same baseline.
What "Audit-Ready" Actually Means
Every dataset is delivered with a governance package, not just audio files. These artifacts are designed to be reviewed by legal and compliance teams.
Right-to-Withdraw Version Control
Consent Receipts
Scope, timestamp, and user ID mapped.
Protocol Summary
Collection method and validation gates.
QA & Acceptance Report
Pass/Fail metrics against spec.
Exception Log
Re-collection and anomaly notes.
EXECUTION MODEL
Delivery Lifecycle
A predictable, gate-checked process designed for procurement and risk teams.
Protocol Design
Prompts, scripts, and validation gates defined.
Allocation
Recruitment from the contributor network aligned to demographics.
Capture
Recording via app with real-time quality checks.
Validation
Multi-pass QA, structured packaging, and delivery.
Request a Quote
Start Your Audio
Data Project
Tell us what you need. We'll respond with a scoped plan, timeline, and quote in 1 business day.
Enterprise Ready
NDA on request, Article 28 DPA with every data engagement. EEA data residency under European jurisdiction.
Fast Response
Dedicated account manager responds within 24 hours with detailed proposal and timeline.
GDPR Compliant
EU-based operations with full GDPR compliance and EU AI Act readiness.
Stakeholder Guides
How Different Teams Use This Page
TECHNICAL
ML & Data Teams
- • Review dialect coverage & acoustic realism
- • See how failure cases are captured
- • Align dataset specs to model requirements
GOVERNANCE
Legal & Compliance
- • Confirm consent handling & docs
- • Review governance artifacts & DPAs
- • Validate audit readiness
OPERATIONS
Procurement
- • Understand delivery model
- • Confirm timelines & accountability
- • Check contracting readiness
Optional First Step
Edge-Case Audit (Optional First Step)
If you already know where your model fails, start there. We can scope a targeted evaluation dataset for specific dialects, noise environments, or known failure cases.
Share failure logs
Get scoping estimate
No commitment required