Motivus and H2O.ai announce strategic alliance to drive enterprise AI adoption and accelerate measurable business impact. Digital engineering firm Motivus has entered into a strategic partnership with H2O.ai, the developer behind H2O AI Cloud, to bring scalable, sovereign artificial intelligence infrastructure into core enterprise operations. This partnership integrates H2O.ai's sovereign AI with Motivus' human-driven engineering approach. Through this combination of platform architecture with delivery services, this partnership assists organizations in integrating AI in their business process, enhancing operational decision-making, and ensuring that they get their ROI out of their technology investments. Busting the governance & execution challenges of enterprise AI adoption. As interest grows in generative and agentic AI among enterprises, there is a big challenge in getting pilot projects implemented in the real world. This is especially true for sectors like financial services and healthcare that have compliance requirements, security standards, and data residency considerations which prohibit them from embracing a "one size fits all" public cloud offering. In addition, there is difficulty in reconciling the technical gap between software procurement and full integration into the process workflow. To implement large-scale AI solutions, one needs domain knowledge, data pipeline overhaul, and constant model governance to ensure the consistency of automated processes with the risk standards of the company. The collaboration between Motivus and H2O.ai addresses all these challenges associated with deployment. The H2O AI Cloud that can be implemented in a hybrid environment, on premises or air-gapped sovereign environment gives the organizations full ownership of their intellectual property, predictions and the data on their customers. On the other hand, Motivus offers the expertise in system integration and engineering needed for implementation. Key operational capabilities and technical advantages delivered through the partnership include: Sovereign, Air-Gapped Deployment Flexibility: Empowers organizations with stringent data sovereignty and compliance requirements to run AI models on-premises, in air-gapped data centers, or across private and public clouds. Unified Predictive and Agentic Capabilities: Integrates predictive machine learning, generative intelligence, and agentic workflows such as H2O AI Super Agent and h2oGPTe into a single, consolidated platform environment. Human-Led Digital Engineering: Combines advanced algorithmic automation with Motivus' hands-on systems architecture and delivery teams, eliminating operational blind spots during enterprise integration. Capital and Operational Risk Reduction: Maximizes legacy IT and data investments by configuring custom AI models directly on internal data stores, preventing vendor lock-in. "Our partnership with H2O.ai combines their H2O AI Cloud platform with Motivus' industry expertise and engineering strengths. Together, we will help enterprises adopt AI securely, responsibly, and at scale, while maximizing their existing technology investments. Our focus is on delivering measurable business outcomes improving productivity, accelerating decisions, reducing costs, managing risk, and driving growth." Uma Sankar Kopalle, Chief Growth Officer of Motivus "We're excited to have Motivus as a partner, bringing H2O AI Cloud to more banks and healthcare organizations that want to own their AI," said Tom Stenger, Vice President, Partnerships at H2O.ai. Scaling of AI applications in key verticals. As a result of focusing on data-driven industries like commercial banking, healthcare, and life sciences, Motivus and H2O.ai provide organizations with a proven model for moving beyond proof-of-concept trials to more concrete enterprise use of AI solutions. The collaborative offering guarantees that any enterprise efforts in AI are business-oriented and deterministically controlled.

H2O.ai launches TimeH2O-v1 forecasting model, tops fev-bench. Thu, 29th Oct 2026 (Today) H2O.ai has released TimeH2O-v1, a time-series foundation model for forecasting, and says it ranks first on the fev-bench leaderboard by win rate. According to H2O.ai, TimeH2O-v1 placed first among 32 models in the benchmark's default view. It posted a 91.9% win rate and a skill score of 50.8 across 100 forecasting tasks, ahead of Timestral-209M, t0-gamma, TimesFM-3 and Chronos-2. The release puts H2O.ai into direct competition with larger technology groups and specialist model developers in a part of the artificial intelligence market focused on operational prediction rather than text generation. Time-series forecasting is used in demand planning, inventory management, cash forecasting, energy load management and patient volume planning. Unlike forecasting systems trained separately for each dataset, TimeH2O-v1 works in zero-shot mode, H2O.ai said. This means it reads the history of a time series and generates a forecast without training on that specific dataset at the point of use. H2O.ai said TimeH2O-v1 was not trained on any fev-bench dataset, and that no training or fine-tuning takes place during forecasting, leaving the model's weights unchanged. The benchmark measures probabilistic accuracy on real-world time series across different domains and frequencies against a seasonal-naive baseline. Benchmark results On the leaderboard view cited by H2O.ai, TimeH2O-v1 beat rival models in 91.9% of head-to-head comparisons. Timestral-209M followed at 89.6%, while t0-gamma recorded 88.1%. H2O.ai said the model's skill score of 50.8 means forecast error was 50.8% lower than the seasonal-naive baseline when aggregated across all 100 tasks. TimeH2O-v1 has 108 million parameters, according to H2O.ai, and can forecast both univariate series and series that include covariates such as price, promotions or weather. H2O.ai said the model performs across datasets from finance, healthcare, supply chain and energy. It added that organisations can fine-tune it on their own infrastructure using their own data to improve results for specific domains. Business focus The launch reflects a growing effort among AI developers to build models for narrower business tasks, where prediction accuracy can be measured more directly than in general-purpose chatbots. Forecasting has long been a core use case for machine learning in large organisations, but many companies still maintain separate models for separate operational problems and retrain them as conditions change. H2O.ai is positioning TimeH2O-v1 as a general model that can serve as a starting point before adaptation to a customer's data. The approach mirrors a wider industry shift towards foundation models tailored to specific data types, including time series, code, biology and industrial processes. "Building your business with accurate forecasting and prediction is the superpower of AI. TimeH2O gives organizations a generalized, pre-trained model for time series that can then be fine-tuned into a domain-specific foundation model for each customer's use case," said Sri Ambati, Chief Executive Officer and Founder of H2O.ai. TimeH2O-v1 is available under a non-commercial licence for internal benchmarking and testing. Organisations with commercial forecasting requirements would need separate arrangements, H2O.ai said. H2O.ai said it is used by more than 20,000 organisations, including half of the Fortune 500. It cited customers including AT&T, Commonwealth Bank of Australia, Wells Fargo, Bank of America, Workday, Progressive Insurance and the NIH.

H2O.ai launches TimeH2O-v1 forecasting model, tops fev-bench. Thu, 29th Oct 2026 (Today) H2O.ai has released TimeH2O-v1, a time-series foundation model for forecasting, and says it ranks first on the fev-bench leaderboard by win rate. According to H2O.ai, TimeH2O-v1 placed first among 32 models in the benchmark's default view. It posted a 91.9% win rate and a skill score of 50.8 across 100 forecasting tasks, ahead of Timestral-209M, t0-gamma, TimesFM-3 and Chronos-2. The release puts H2O.ai into direct competition with larger technology groups and specialist model developers in a part of the artificial intelligence market focused on operational prediction rather than text generation. Time-series forecasting is used in demand planning, inventory management, cash forecasting, energy load management and patient volume planning. Unlike forecasting systems trained separately for each dataset, TimeH2O-v1 works in zero-shot mode, H2O.ai said. This means it reads the history of a time series and generates a forecast without training on that specific dataset at the point of use. H2O.ai said TimeH2O-v1 was not trained on any fev-bench dataset, and that no training or fine-tuning takes place during forecasting, leaving the model's weights unchanged. The benchmark measures probabilistic accuracy on real-world time series across different domains and frequencies against a seasonal-naive baseline. Benchmark results On the leaderboard view cited by H2O.ai, TimeH2O-v1 beat rival models in 91.9% of head-to-head comparisons. Timestral-209M followed at 89.6%, while t0-gamma recorded 88.1%. H2O.ai said the model's skill score of 50.8 means forecast error was 50.8% lower than the seasonal-naive baseline when aggregated across all 100 tasks. TimeH2O-v1 has 108 million parameters, according to H2O.ai, and can forecast both univariate series and series that include covariates such as price, promotions or weather. H2O.ai said the model performs across datasets from finance, healthcare, supply chain and energy. It added that organisations can fine-tune it on their own infrastructure using their own data to improve results for specific domains. Business focus The launch reflects a growing effort among AI developers to build models for narrower business tasks, where prediction accuracy can be measured more directly than in general-purpose chatbots. Forecasting has long been a core use case for machine learning in large organisations, but many companies still maintain separate models for separate operational problems and retrain them as conditions change. H2O.ai is positioning TimeH2O-v1 as a general model that can serve as a starting point before adaptation to a customer's data. The approach mirrors a wider industry shift towards foundation models tailored to specific data types, including time series, code, biology and industrial processes. "Building your business with accurate forecasting and prediction is the superpower of AI. TimeH2O gives organizations a generalized, pre-trained model for time series that can then be fine-tuned into a domain-specific foundation model for each customer's use case," said Sri Ambati, Chief Executive Officer and Founder of H2O.ai. TimeH2O-v1 is available under a non-commercial licence for internal benchmarking and testing. Organisations with commercial forecasting requirements would need separate arrangements, H2O.ai said. H2O.ai said it is used by more than 20,000 organisations, including half of the Fortune 500. It cited customers including AT&T, Commonwealth Bank of Australia, Wells Fargo, Bank of America, Workday, Progressive Insurance and the NIH.