HIPAA-Aware Speech Data, Sovereign by Design
Auditable consent, BAA-eligible engagement, and small language models you deploy inside your own enclave. Built for teams shipping clinical AI under HIPAA, GDPR, and the EU AI Act ahead of the August 2, 2026 enforcement deadline.
- BAA-eligible engagement model: we sign the Business Associate Agreement.
- Verified consent pipeline with chain-of-custody from speaker to dataset.
- Small language models deployable inside secure enclaves: no PHI egress.
30-minute session. Norwegian entity. BAA signed in week one. 48-hour response.
PDF, 14 pages. No gating beyond email.
Agent Decision Logging
Small Language Models in Your Enclave
Verified Clinical Speech Data
Built for Teams Operating Under HIPAA, GDPR, and the EU AI Act
We sign Business Associate Agreements as part of clinical engagements.
Norwegian-headquartered. Born under GDPR, not retrofitted.
Training-data summary template, post-market disclosure, data-card provenance.
Small language models deploy inside the client enclave; PHI does not leave the perimeter.
Models Trained on Questionable Data Become a Compliance Liability
Off-the-shelf speech corpora and black-box cloud APIs were not built for the chain-of-custody, BAA-eligibility, or demographic coverage that clinical AI demands. With EU AI Act enforcement starting August 2, 2026 and fines reaching 7% of global revenue, deploying on shaky data converts technical debt into audit findings, retraining cycles, and sovereignty gaps.
PHI exposure risk in any pipeline that touches patient audio
Self-built de-identification is technically complex, legally ambiguous, and strips the clinical context the model needs to perform.
BAA-eligibility gates every vendor decision
Most speech-data marketplaces and cloud APIs will not sign a Business Associate Agreement. Without one, the data cannot enter your clinical AI pipeline at all.
Black-box cloud APIs do not equal an audit trail
No training-data provenance, no enclave deployment, no documented redaction methodology. Auditors and EU AI Act reviewers will ask. The API vendor cannot answer.
Demographic and dialect bias fails on real patient populations
Standard US and UK speech corpora collapse on non-native English speakers, regional accents, and cross-device clinical environments. Domain shift becomes patient harm.
Data, Sovereign Models, and Governance: One Engagement, One BAA
YPAI is AI infrastructure. The product is the integrated pipeline: consent-verified clinical speech data, small language models you deploy inside your enclave (with RLHF and fine-tuning workflows on your domain corpus), and agent decision logging that produces immutable audit records of every agent action. One procurement, one BAA, one auditable stack.
For the cross-vertical view, see the speech data infrastructure overview.
Agent Decision Logging
Auditable by Design
- Immutable, cryptographically chained agent-decision logs.
- Time-travel debugging across LangGraph state machines.
- Tool-usage tracking, HITL gates, and EHR-aware integration patterns for clinical decisions.
Small Language Models in Your Enclave
Deployed Where You Say
- Small language models tuned for clinical speech tasks (RLHF and supervised fine-tuning workflows included).
- Secure-enclave deployment options: cloud, private cloud, on-prem.
- No PHI egress from your perimeter, ever.
Verified Clinical Speech Data
Real-World, Consent-Verified, Multimodal
- Consent captured at source with chain-of-custody documentation.
- Real demographic, dialect, and environmental coverage.
- Multimodal pipelines: audio, video, documents, sensors.
Auditable from Speaker Consent to Agent Decision
Five engineered layers that make the chain-of-custody literal. Compliance buyers walk auditors through this section instead of explaining policy.
- 01
BAA-Eligible Engagement
Business Associate Agreement signed as part of the engagement. Defines covered services, breach-notification timelines, and data-handling obligations explicitly.
Addresses: How do we contract with you under HIPAA? - 02
Verified Consent at Source
Speaker consent captured at the moment of recording with documented identity, scope, and retention terms. Not retroactive de-identification: defensible consent, traceable to the individual speaker.
Addresses: Why not de-identify our own PHI? How do we prove consent in audit? - 03
PHI Redaction Methodology, Documented
Configurable redaction layer covering the 18 HIPAA PHI identifiers. Safe Harbor or Expert Determination paths, with redaction events logged. The methodology is documentation, not opacity.
Addresses: How do you handle the 18 PHI identifiers? Can I see the methodology? - 04
Sovereign Deployment Inside Your Enclave
Small language models run where you say: cloud, private cloud, dedicated tenancy, on-prem secure enclave. No PHI egress. No CLOUD Act exposure for non-US deployments.
Addresses: Is this only for US companies? Can we keep PHI in jurisdiction? - 05
Immutable Agent Decision Logs
Every agent decision, tool call, and HITL gate is cryptographically chained. Time-travel debugging across LangGraph state machines. Audit-pack export per dataset and per agent run.
Addresses: How would my auditor verify what your agents did with our data?
Architecture diagrams + sample audit-pack export. Email gating only.
Cross-jurisdiction context: read the EU AI Act readiness brief for the obligations stacking onto HIPAA from August 2, 2026.
Real Demographics. Real Environments. Real Devices.
Synthetic data alone collapses on real patient populations. YPAI builds on consent-verified anchor data spanning the dimensions that drive domain shift in production, and uses synthetic augmentation to fill edge cases responsibly.
Age bands, gender expression, accent regions, dialect groups, socio-linguistic background. Documented per dataset.
Native and non-native English speakers. Norwegian, Swedish, Danish, Finnish, German, French, Spanish, Polish, and more. Code-switching captured.
Clinic exam room, hospital ward, telehealth video, ER background noise, pharmacy counter. Not booth-recorded benchmarks.
Laptop microphones, mobile phones, headsets, room-array systems, dictation devices. Cross-device acoustic shift captured.
Synthetic data fills targeted edge cases (rare conditions, specific dialect gaps) on top of real anchors. Synthetic does not replace real consent-verified data.
Audio, video, documents, and structured sensor signals where the clinical workflow demands it. One pipeline, one governance model.
The contributors behind this anchor data are recruited and consented through a vetted network. See how we vet clinical-domain contributors for the screening, consent, and quality protocols.
From Compliance Review to Production Deployment
An infrastructure partnership, not a one-off purchase. The path is concrete enough that procurement and clinical operations can plan against it.
- 01
Compliance Strategy Session
1 session, 30 minutesCompliance posture assessment, BAA scope draft, evidence-pack outline.
- 02
Discovery and Pilot
Weeks, not monthsReady-to-use clinical speech data sample for evaluation, signed BAA, pilot architecture diagram.
- 03
Production Build
Engineered to your clinical contextCustom-collected real-world data, small language model deployed in your enclave (RLHF and supervised fine-tuning workflows on your domain corpus), agent decision logging wired to your pipeline.
- 04
Audit-Ready Operations
OngoingAudit-pack exports per dataset and per agent run, EU AI Act post-market disclosures, periodic review cadence.
Stage 1 begins as soon as you book.
What Compliance, ML, and CTO Buyers Ask Before Signing
Pulled from real evaluations with health-system innovation labs, digital-health CTOs, and clinical research operations leads.
Are you HIPAA certified?
There is no such certification under HIPAA. We provide data and infrastructure to help you build HIPAA-compliant clinical AI solutions, including BAA execution as part of the engagement. Anyone marketing 'HIPAA certified' speech infrastructure is using a phrase that does not exist.
Why not just use a managed cloud transcription API?
Managed transcription APIs are products for turning audio into text inside the provider cloud. They are not training-data infrastructure. You cannot fine-tune a sovereign model on them, you cannot deploy them inside your own enclave, and you cannot audit their training-data origins. YPAI gives you the data and the sovereign models you actually own.
Why can't we de-identify our own PHI?
You can. Self-de-identification is technically complex, legally ambiguous, and tends to strip the clinical context the model needs. Verified consent at the moment of collection is more defensible to auditors than retroactive scrubbing, and it preserves the signal.
How do we prove consent to auditors?
The verified consent pipeline produces a chain-of-custody record per speaker. Agent decision logging adds immutable, cryptographically chained records of every agent decision. Audit-pack exports per dataset and per agent run are part of the engagement.
Is your data diverse enough for our patient population?
Per dataset, we document the demographic axes (age, gender expression, accent region, dialect group), the recording environments (clinic, home, telehealth, ER), the device classes (laptop, mobile, headset, room array), and the language coverage. Synthetic augmentation fills targeted gaps without replacing real anchors.
Is this only for US companies?
No. The infrastructure is designed against HIPAA, GDPR, and the EU AI Act in one stack. Norwegian-headquartered operations and EU-resident sovereign deployment options eliminate CLOUD Act exposure for non-US clinical AI products.
How long does this take to implement?
Ready-to-use clinical speech data products are available for fast pilot evaluation. Custom production builds are engineered to your specific clinical context. The first conversation is a Compliance Strategy Session, not a sales demo, and it produces a posture assessment plus a BAA scope draft.
Do you support RLHF and fine-tuning workflows for clinical SLMs?
Yes. Small language models in your enclave ship with supervised fine-tuning and RLHF workflows tuned for clinical speech tasks. They run inside your enclave so your domain corpus and reward signals never egress. We document the training-data provenance, the reward-model training, and the evaluation criteria as part of the EU AI Act-aligned data card and the per-engagement audit pack.
Clinical AI Builds Better When the Data and the Governance Arrive Together
Schedule a Compliance Strategy Session and leave with a posture assessment, a BAA scope draft, and an evidence-pack outline for your auditors. 30 minutes. Norwegian entity. 48-hour response.
- 01 Clinical use case in scope (ambient documentation, clinical research voice capture, patient-reported outcomes, decision support, other).
- 02 Jurisdictions in play (HIPAA, GDPR, EU AI Act, sector-specific regulators).
- 03 Deployment preference (cloud, private cloud, on-prem secure enclave).
What We Do Not Claim
We do not claim certifications we do not hold. We provide data and infrastructure to help you build HIPAA-compliant clinical AI; we are not 'HIPAA certified', because that designation does not exist under HIPAA.
We do not publish customer counts, hours-of-clinical-audio totals, or '99% accuracy' benchmark numbers without verifiable per-asset case studies. When we have a documented case study with the customer's permission, we publish it specifically and link to it. Until then, we describe the architecture and the engagement model, not the volume.
We document our PHI redaction methodology, our consent pipeline, our deployment options, and our agent-governance architecture in the Compliance Documentation. The first Compliance Strategy Session produces a posture assessment specific to your clinical use case.