Tangent AI provides a cloud‑native platform that automatically engineers features and rebuilds time‑series models in real time, delivering forecasts and anomaly alerts without manual preprocessing. The service integrates with major cloud data pipelines, scales horizontally, and reduces compute usage by up to 20×, helping data scientists and ML engineers accelerate insight generation across energy, finance, IT, and manufacturing use cases.
Funding
$4.3M 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.
2OMGSIFounders
Product
Problem
Many organizations generate large volumes of time‑series data but lack efficient tools to transform it into accurate forecasts and anomaly alerts. Traditional pipelines require extensive manual feature engineering, frequent model retraining, and high compute resources, leading to slow time‑to‑insight and elevated costs.
Solution
Tangent AI offers a cloud‑native predictive platform that acts as an AI copilot for time‑series data. It automatically engineers relevant features and rebuilds models in real time as data streams evolve, eliminating manual preprocessing and reducing the need for repeated model tuning. The service integrates with existing cloud data pipelines (e.g., Azure, Databricks) and scales infinitely, delivering forecasts and anomaly detections with up to 20× lower compute consumption. By generating models instantly, Tangent accelerates time‑to‑market by 10–20× and boosts data‑science productivity, enabling businesses to derive actionable insights for asset health, load forecasting, cloud waste detection, and commodity price prediction.
Target Audience
Primary users are data scientists, ML engineers, and cloud/AI architects in enterprises that require large‑scale time‑series forecasting and anomaly detection, particularly in energy, finance, IT operations, and manufacturing sectors.
Features
- Automated feature engineering for each time‑series column, producing millions of features monthly
- Real‑time model (re)building that adapts instantly to data schema changes
- Scalable cloud deployment with rapid provisioning and infinite horizontal scaling
- Seamless integration via flexible connectors to major cloud platforms and data warehouses
- Forecasting and anomaly detection APIs that deliver predictions with explainable outputs
- High computational efficiency, achieving up to 20× reduction in processing power
- Support for diverse domains such as energy grid load, financial price, IT resource usage, and raw material costs