Built in healthcare. Scaled for AI.
Integral started where the stakes were highest: HIPAA-regulated healthcare data. The team spent decades inside those environments and built the platform they wished existed. Today, the same rigor powers privacy programs across the AI data supply chain — from frontier model training to enterprise data products.
{
"dataset": "clinical_notes_q1_2026",
"standard": "hipaa_expert_determination",
"records_processed": 2_847_391,
"phi_entities_found": 14_209_455,
"phi_entities_redacted": 14_209_455,
"data_utility_preserved": "94.2%",
"re_identification_risk": "0.04%",
"status": "determined",
"completed_in": "4 days",
"expert_review": "approved"
}Healthcare roots, AI-scale methodology
Healthcare data privacy is one of the most demanding disciplines in regulated data. HIPAA Expert Determination requires statistically rigorous assessment, signal-preserving remediation, and documentation that survives regulatory scrutiny. That is where Integral cut its teeth.
The methodology we built — assess re-identification risk, calibrate remediation to preserve utility, verify the result, and produce documentation that travels with the dataset — turns out to be exactly what every regulated data domain needs. Today we apply it across healthcare, financial, consumer, and AI training data.
Infrastructure Privacy Is Not Data Privacy
Most organizations have HIPAA-ready infrastructure: encryption in transit, access controls on their databases. That attests to the container. Expert Determination formally assesses the contents.
A secure data center full of improperly de-identified patient records is a secure building with a liability problem inside it. Integral closes that gap, ensuring the data itself is defensible, not just the systems that store it.
The team behind the platform
Our team includes qualified statisticians, privacy engineers, data engineers, and product leaders who have worked across healthcare, financial services, and AI infrastructure. That experience is encoded directly into the platform and every engagement we run.
The real advantage shows up in remediation design. There is no one-size-fits-all formula; the right strategy depends on the data, the use case, and the privacy posture. We optimize to preserve what matters — geographic precision, temporal relationships, clinical detail — and produce documentation calibrated to the domain. You choose the trade-offs, and we find the path that preserves the most value.