Data for AI refers to the preparation of high-quality, privacy-preserved datasets that can be safely used to train and operate AI systems. This includes structured enterprise data, unstructured documents, conversational data, images, audio, and health-related data, with built-in privacy controls.

Maya prepares AI-ready datasets across multimodal data sources, including ERP, HR, sales and operations data, chat logs, scanned PDFs, text files, images, audio recordings, medical notes, and conversational data used in AI and GenAI applications.

Maya protects sensitive data through automated PII detection, de-identification, and controlled re-identification. Privacy guardrails are applied across structured and unstructured data before AI training or inference.

Multimodal AI data handling refers to preparing and managing different data formats such as text, PDFs, images, audio, video, and healthcare data for AI use. Maya ensures consistent privacy-preserved processing across all data types.

Yes. Maya enables responsible use of internal ERP, HR, sales, and operational data for internal AI tools like productivity AI, analytics, and RAG-based AI, while reducing privacy and compliance risks.

Maya maintains visibility into how data is detected, de-identified, and used across AI pipelines, helping organizations build trust in AI outcomes and simplify compliance audits.

Yes. Maya supports controlled re-identification for regulated or audit use cases. Re-identification is applied only when appropriate and is not available for irreversibly anonymized data.

Maya Data for AI is delivered via API-based services that can be used independently or combined. Services support synchronous and asynchronous processing, including PII detection, de-identification, re-identification, and file-based de-identification.

Maya Data for AI addresses privacy risks in AI training, loss of business secrets, lack of traceability and explainability, regulatory barriers, and challenges in scaling AI globally.