Build better GenAI models with rich, multi-modal datasets
Improve LLM performance by seamlessly integrating structured, semi-structured and unstructured data from a wide array of heterogeneous systems and data sources
Unified connectivity: Build and implement GenAI prototypes fast with no-code workflows across cloud services, SaaS applications, databases, and APIs.
Data quality management: Aggregate, reconcile, and standardize diverse data formats to ensure accuracy and consistency while reducing dataset redundancy.
Data transformation: Seamlessly convert heterogeneous data sources into compatible formats for optimized GenAI performance.
Securely generate AI models with auditability and control
Develop and deploy trustworthy AI models that deliver accurate and relevant RAG-generated outputs by securely leveraging highly sensitive, rapidly changing data sources.
Data provenance: Gain granular insights into the origin, processing, and lineage of your data, ensuring transparency and accountability.
Robust security & controls: Control exactly what data is shared, using fine-grained controls and anonymization for robust security and privacy.
Activate your AI strategy with vendor-agnostic architecture
Seamlessly move data across analytical systems and multi-warehouse environments via future-ready data architecture that balances flexibility and security.
Effortless data movement: Easily share data across multiple data warehouses and analytical systems without duplicating data pipelines.
Data standardization: Support and transform a wide range of data formats, including Apache Iceberg and Delta Sharing, for any AI initiative.
Cost efficiency: Avoid vendor lock-in and optimize costs with infrastructure that supports any best-of-breed strategy.
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WHITEPAPER
Overcoming common data hurdles to accelerate GenAI projects