Healthcare AI that understands how patients actually speak.
De-identified Indic clinical dialogue, medical NLP and speech data — consented and DPDP-aligned for healthcare AI you can deploy safely.
Triage bots, clinical scribes and patient copilots break on real Indian healthcare conversations — code-mixed symptoms, regional terms, accented speech. We build the de-identified, consented, expert-annotated clinical data that healthcare AI needs to be safe, accurate and compliant.
What teams in Healthcare are up against
Healthcare is the highest-stakes, most regulated place to deploy AI — and the data is the hardest part:
- Patients don't speak textbook. Symptoms arrive code-mixed, with regional terms and folk descriptions that English clinical models never saw.
- Errors carry clinical risk. Mis-transcribed dosages or missed negations are patient-safety events, demanding expert annotation and QA.
- PHI is everywhere. Clinical text and speech are dense with personal health information that must be de-identified before training.
- Speech is accented and noisy. OPD and tele-consult audio spans accents, dialects and real-world noise that generic ASR fails on.
Compliance & residency
Healthcare data demands the strictest privacy and consent posture:
- DPDP Act, 2023 — explicit consent and documented processing for personal health data.
- PHI de-identification — names, IDs, contacts and dates removed with audited SOPs before annotation.
- Clinical review — medically-trained annotators and reviewers for safety-critical labels.
- India-residency option — in-country processing and storage for hospitals, payers and health-tech.
How we solve it
Safe, consented clinical data — collected and labeled by experts
De-identified, medically-reviewed data across dialogue, documents and speech — built for patient safety and compliance.
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Clinical speech & dialogue collection
Consented OPD, tele-consult and symptom-description audio across accents and dialects, captured and de-identified in the field.
Multilingual data collection -
Medical NLP annotation
Entity, intent, negation and relation labeling on clinical text by medically-trained annotators with two-pass QA.
Data annotation -
PHI de-identification & safety
DPDP-aligned PII/PHI detection and redaction, plus safety review for harmful or unsafe medical outputs.
Content moderation & safety -
Clinical evaluation sets
Evaluation data that tests whether your model handles negation, dosage, code-mix and regional terms correctly before deployment.
Cultural & cross-lingual evaluation
Proof
Why teams trust us with this vertical
- aligned consent & de-identification SOPs
- DPDP
- IAA on clinical entity & intent labels
- ≥ 0.85
- accent & dialect coverage for clinical speech
- Multi
aligned consent & de-identification SOPs
IAA on clinical entity & intent labels
accent & dialect coverage for clinical speech
Go deeper
Datasets and services for this vertical
Jump straight into the catalog filtered for this domain, or scope a custom program.
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Browse the dataset catalog
See rights-cleared, documented datasets filtered to this vertical — or commission a custom set.
View datasets -
Explore our services
End-to-end collection, annotation, RLHF/DPO, evaluation and safety — applied to your use case.
All services -
Keep it sovereign
India-resident collection, annotation and storage for regulated and government-grade programs.
Sovereign Data
FAQ
Healthcare — common questions
- How do you handle PHI and patient consent?
Explicit consent, audited PHI de-identification SOPs and documented processing records aligned with the DPDP Act, 2023 — with an India-residency option.
- Are clinical labels reviewed by medical experts?
Yes. Safety-critical annotation uses medically-trained annotators and reviewers with two-pass QA. See Data Annotation.
- Can you capture accented, code-mixed clinical speech?
Yes — consented field capture across accents and dialects. Explore healthcare datasets or commission a custom corpus.
Build an AI data program for Healthcare.
Tell us your languages, modalities and use case — we'll scope a rights-cleared, documented data program and a delivery schedule.
Where our data services apply
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Multilingual Data Collection
Native-speaker audio, video, image and text collection across Indic, African and low-resource languages — field-grade, consented, documented.
Explore the service -
Data Annotation
Speech, NLP, CV and multimodal annotation at IAA ≥ 0.85 with two-pass QA — built for foundation-model SLAs.
Explore the service -
Content Moderation & Safety
PII filtering, harmful-content taxonomies and culturally-aware safety pipelines — calibrated for Indian context.
Explore the service -
Cultural & Cross-Lingual Evaluation
Evaluation for honorifics, code-mix, idioms, caste-safety and pragmatic correctness — beyond translated MMLU.
Explore the service