Enterprises face challenges in leveraging GenAI due to data silos across multiple clouds, on-premise systems, and edge locations. Moving sensitive data to centralized AI platforms raises privacy and security concerns, hindering the adoption of scalable AI solutions.

Kamiwaza.ai offers a GenAI stack featuring an Inference Mesh and a locality-aware Distributed Data Engine, enabling enterprises to process data where it resides without compromising privacy. The platform integrates open-source components like Ray, CockroachDB, Milvus, and vLLM, packaged in containers with connectors to commercial products. Kamiwaza's solution allows businesses to run private GenAI models on private data across diverse environments, processing data locally and only transmitting inference results.

Inference Mesh: Processes data at its source location, minimizing data movement and maximizing security.

Locality-Aware Data Engine: Ensures data processing occurs where the data resides, optimizing efficiency for geographically distributed deployments.

Scalable Inference Architecture: Leverages Ray for distributed inference, scaling across enterprise hardware resources.

Enterprise-Savvy Chunking: Implements tokenizer-aware text segmentation for improved retrieval accuracy and resource efficiency.

Unified Enterprise Interface: Provides a standardized interface for interacting with various vector databases.

Enterprise-Grade Stability: Offers a local model and metadata repository, protecting operations from external model changes.

Flexible Enterprise Integration: Integrates with enterprise identity systems for advanced access control and auditing.

The primary customers are enterprises seeking to deploy scalable and secure GenAI solutions across hybrid and multi-cloud environments, including those in regulated industries with strict data privacy requirements.

Pricing is available through cloud marketplaces (AWS, Azure, GCP) and OEM partners.