ContinualIST provides a plug‑and‑play forecasting platform built around Credence, a Time Series Foundation Model that delivers zero‑shot, domain‑adaptive predictions via API or SDK.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations relying on time‑series forecasts often spend weeks or months on data preparation, model training, and integration, causing decision windows to close before predictions are available. Traditional models also struggle with data drift, changing horizons, and new asset rollouts, leading to frequent re‑training and operational downtime.
Solution
ContinualIST offers a plug‑and‑play forecasting platform built around Credence, a Time Series Foundation Model trained on large cross‑domain datasets. The model provides zero‑shot, domain‑adaptive forecasts that can be accessed instantly via API or SDK, eliminating the need for extensive historical data collection or lengthy training cycles. Continuous adaptation mechanisms keep predictions accurate as market rules, data patterns, and forecasting horizons evolve, reducing drift‑related rework. The platform integrates natively with agentic workflows and existing data pipelines, enabling rapid deployment with minimal integration effort. Users receive AI‑driven predictions on demand, supporting timely decision‑making across diverse business functions.
Target Audience
Primary customers are enterprises in sectors such as supply chain, energy, finance, and manufacturing that require accurate, real‑time time‑series forecasts integrated into their operational workflows.
Features
- Zero‑shot forecasting that works out‑of‑the‑box for new assets without historical data
- Continuous self‑maintenance to handle data drift, horizon changes, and regulatory shifts
- API and SDK interfaces for fast integration into existing workflows and agentic pipelines
- Domain‑adaptive Time Series Foundation Model (Credence) trained on large, cross‑industry datasets
- Plug‑and‑play deployment with minimal configuration and no separate training infrastructure