TensorNative
TensorNative provides an AI-first immutable storage platform that ensures data cannot be altered or deleted, guaranteeing integrity for AI training and inference. Its autonomic driver processes dynamic data on-chip, achieving 99% GPU saturation and eliminating RAG bottlenecks. The platform vectorizes 1 PB of data in 15 seconds on GPU, reducing re-indexing needs by 90% and improving query performance by 5X.
- Artificial Intelligence
- Enterprise Software
- Developer Tools
- Software Only
- Data & Analytics
- Regulatory & Compliance Technology
Funding
Founders
Founder details are not available yet.
Product
Problem
Organizations building and deploying AI systems face significant challenges in managing the underlying data, including ensuring data integrity, preventing tampering, and maintaining permanent, reliable records. Traditional storage solutions are not designed for the unique demands of AI workflows, which require immutable, verifiable data to ensure model accuracy, compliance, and auditability.
Solution
TensorNative offers an AI-first immutable storage platform that provides a secure, permanent foundation for data used in artificial intelligence applications. The platform ensures that once data is stored, it cannot be altered or deleted, guaranteeing integrity and trustworthiness for AI training and inference processes. The system's autonomic driver creates a constant stream of data, eliminating RAG bottlenecks and ensuring 99% GPU saturation by keeping GPUs processing instead of waiting. This dramatically reduces re-indexing overhead and query latency while lowering both energy consumption and cloud costs compared to traditional RAG pipelines.
Target Audience
Primary customers are data engineering teams, AI/ML developers, and enterprises that require tamper-proof, permanent data storage to support their artificial intelligence and machine learning initiatives, particularly those dealing with large-scale unstructured data for retrieval-augmented generation workloads.
Features
- Immutable storage architecture that prevents data modification or deletion, ensuring long-term data integrity
- Autonomic driver that processes dynamic data at scale on-chip, handling petabytes of data without CPU bottlenecks
- Eco-optimized compute delivering 99% GPU saturation, eliminating idling that wastes up to 60% of GPU time in traditional pipelines
- 1-click deployment via a single optimized binary packaged for Docker, compatible with spot instances and serverless containers
- Eliminates the need for Spark clusters, Python dependencies, and heavy orchestration frameworks
- Vectorizes 1 PB of data in 15 seconds on GPU, reducing re-indexing needs by 90% and improving query performance by 5X