H2O.ai

H2O.ai provides a machine learning platform that enables the development of predictive models and smart applications using automated machine learning techniques. This platform enhances data-driven decision-making by simplifying the model training process and improving the accuracy of insights derived from large datasets.

Traction 10/10

Mountain View, United States · HQ

Founded 2012351

Artificial Intelligence
Data & Analytics
Enterprise Software
Software Only

Updated 5 months ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

3 founders

Sri Ambati

CEO

Arno Candel

Co-Founder

Scott Kaufman

Co-Founder

Product

Problem

Many organizations struggle to effectively leverage machine learning due to the complexity of model development, deployment, and the need for specialized expertise. Traditional machine learning workflows often involve manual processes, making it difficult to scale AI initiatives and derive timely insights from large datasets.

Solution

H2O.ai provides a comprehensive AI platform that simplifies the development and deployment of both predictive and generative AI models. The H2O AI Cloud offers automated machine learning (AutoML) capabilities, enabling users to build high-accuracy models with minimal coding. The platform supports a wide range of algorithms and deployment options, including on-premises, air-gapped, VPC, and managed cloud environments. H2O.ai's platform empowers organizations to democratize AI, allowing both expert data scientists and business users to leverage AI for data-driven decision-making across various industries and use cases.

Target Audience

The primary target audience includes enterprises across various industries such as financial services, healthcare, insurance, manufacturing, marketing, retail, and telecommunications, as well as government entities, non-profit organizations, and academic institutions.

Features

End-to-end Generative AI (GenAI) platform with control over data and prompts
Enterprise h2oGPTe for connecting to any LLM/embedding model, scalable with Kubernetes, and includes guardrails and cost controls
Open Source h2oGPT for customizing and deploying open source AI models
H2O Danube and H2OVL Mississippi series of open weight Small Language Models (SLMs) for on-device and offline applications, OCR, and Document AI
H2O Eval Studio for assessing the performance, reliability, safety, and effectiveness of RAG and LLM-based applications
H2O LLM Studio for no-code fine-tuning of custom enterprise-grade LLMs
H2O Driverless AI for automated machine learning
H2O AI Feature Store for infusing data with intelligence
H2O MLOps for model hosting, monitoring, and deployment
H2O Wave open-source low-code AI AppDev Framework
H2O Document AI for extracting data with intelligence
H2O AI AppStore for industry and use case AI applications
Multi-modal Document AI for converting data to JSON and extracting answers from various file types
H2O Model Validation for LLMs, providing automated testing, human calibration, and bias detection
Integration with open-source ecosystems and proprietary LLMs