The startup develops cloud data warehouses that utilize dynamic algorithms to virtualize database workloads, enabling enterprise-grade service level agreements for SQL queries. This technology empowers telecommunication companies and athletes to leverage AI and machine learning for improved decision-making and operational efficiency.
Funding
$2.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Many enterprises struggle to leverage AI on their private data due to concerns around data sovereignty, compliance, and the risks associated with exposing sensitive information to public cloud environments. Scaling retrieval-augmented generation (RAG) workflows for large, dynamic datasets and managing the compute infrastructure for AI inference at scale also present significant challenges.
Solution
Terizza offers a self-hosted AI platform that enables organizations to securely deploy notebook-based large language models (LLMs) and other AI applications within their private cloud or colocation facilities. The platform provides complete control over proprietary data, ensuring compliance with regulations like HIPAA, GDPR, and SOC 2. Terizza optimizes compute resources, including GPUs and CPUs, to deliver scalable, real-time inference, batch processing, and AI-driven search. Its integrated data management framework combines vector and graph databases, transactional data lakes, and real-time processing capabilities, streamlining AI application development while maintaining security and operational resilience.
Target Audience
The primary target audience includes enterprises in healthcare, finance, and media that require a secure, scalable, and compliant AI platform for processing sensitive data and building AI-powered applications.
Features
- Secure deployment of AI applications in private clouds or colocation facilities, ensuring data sovereignty and compliance.
- Support for notebook-based LLMs and other AI models.
- Optimized compute infrastructure for high-concurrency AI workloads, including real-time inference and batch processing.
- Integrated data management framework with vector databases, graph databases, and transactional data lakes.
- Automated pre-filtering and data transformation for efficient handling of petabytes of tabular and multi-modal data.
- Dynamic querying of data sources and real-time updating of embeddings for low-latency RAG workflows.
- Unified MLOps and LLMOps framework for seamless AI deployment and infrastructure optimization.