Solix Enterprise AI

Establish a unified governance fabric with security, RBAC, comprehensive auditing across structured, semi-structured and unstructured data, backed by lineage, cataloging, and auto-classification. Establish end-to-end observability over data and processes, operationalize compliance (e.g., GDPR, HIPAA, NIST/CCPA) and support AI safety and security and governance over both training and RAG datasets.

Manage documents, PDFs, images, video and IoT signals as first-class data products to support multimodal AI use cases (vision + text + speech). Enable enterprise search, classification, and semantic enrichment over unstructured assets to improve discoverability, reuse, and control.

Integrate generative AI by storing and retrieving vector embeddings to power retrieval-augmented generation (RAG architecture) while fine-tuning or augmenting private LLMs with governed enterprise data. Enable prompt-based analytics/BI and multimodal intelligence (text, image, audio, IoT) by shifting from data preparation to on-the-fly activation leveraging business semantics and natural-language access.

Establish an extensible, open systems approach based on W3C standards and cloud native computing architecture to avoid vendor lock-in and proprietary architectures which cannot be properly explained, interpreted or governed. Open Table Format database tables and Open Metadata sharing.

As the AI regulatory environment evolves, new compliance and governance requirements across all industries are anticipated from state/local, federal and regional regulators and authorities.