Healthcare data de-identification for software testing and AI model training

Accelerate the development of AI-driven healthcare applications with high-fidelity synthetic test data. Ensure data privacy and utility across staging, development, and QA environments while maintaining fidelity to real-world patient and provider datasets.

Retain your data’s clinical richness and preserve its statistical integrity without putting privacy at risk by replacing PHI with realistic synthetic values. Ensure optimal model training for LLM fine-tuning and custom healthcare AI models, such as diagnostic assistants and predictive analytics.

Redact sensitive patient information before entering data into LLM prompts, to ensure that PHI is never exposed by chatbots or AI-driven clinical decision support tools. Maintain compliance with HIPAA and other privacy regulations while enabling safe and effective AI interactions.

Provide LLMs with redacted clinical text while optionally exposing the original data to authorized users. Automate pipelines to extract, structure, and normalize unstructured healthcare data (clinical notes, EHR records, and medical literature) into AI-ready formats for retrieval-augmented generation.

Power digital twins of healthcare systems, patient populations, or medical devices with de-identified or synthetic data. Maintain patient privacy while enabling accurate simulations for clinical research, treatment optimization, and operational efficiency.