Continuous Intelligence develops AI models that learn directly from irregular, asynchronous, and multiscale streaming data in continuous time, eliminating the need for resampling or binning. Its models maintain a continuously updated internal state as observations arrive, supporting prediction, querying, and action on real-world signals like clinical readings and machine telemetry. The company is building toward AI that learns, predicts, reasons, and acts through a continuously evolving representation of the world.
Funding
Funding not disclosed
Founders
Product
Problem
Current frontier AI models are built for discrete tokens and regular grids, but real-world data arrives as irregular, asynchronous, and multiscale streams of measurements, events, and signals. Applying existing AI to time-series data requires resampling, binning, interpolation, or patching, which discards the timing, sparsity, missingness, and cross-scale structure that make observations meaningful.
Solution
Continuous Intelligence develops machine learning models that learn directly from streaming data as it evolves in continuous time. The models' internal state updates as each observation arrives, providing a continuously updated representation that supports prediction, querying, and action without preprocessing steps that destroy information. This approach natively handles irregular, asynchronous, and multiscale time-series data across domains such as clinical readings and machine telemetry. The long-term goal is AI that learns, predicts, reasons, and acts through a continuously evolving representation of the world, making it practical for real-time decision-making on live data streams.
Target Audience
Primary customers are enterprises and research organizations with high-volume streaming data needs, including clinical healthcare providers, industrial machine telemetry operators, and AI research labs requiring real-time predictive analytics on irregular time-series data.
Features
- Models that process irregular, asynchronous, and multiscale time-series data natively without resampling, binning, or interpolation
- Continuous-time internal state representation that updates with each incoming observation
- Support for prediction, querying, and action on live streaming data
- Foundational work using Neural CDEs for online prediction and Permutation Equivariant Neural Graph CDEs for graphs with changing structure
- Research on SLiCEs, a NeurIPS 2025 Spotlight paper, advancing time-series models for irregular data