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About Actable AI

Actable AI is an AI-powered analytics platform from Actable AI that allows users to analyze data and obtain deep insights in minutes. It combines capable AutoML and Causal ML-based analytics, a full suite of AI, statistics, and visualizations, and exportable models and insights to provide flexibility and actionable insights. The platform supports easy usability akin to popular tools like Tableau, ensuring that users can quickly adapt and use its capabilities. Actable AI is designed to help users take actual actions from causal insights rather than just predictions, keeping them aligned with the latest best practices and algorithms. Key capabilities: AutoML Causal ML Statistical analysis Data visualizations Model export Best for: Data analysts and business professionals that need actionable insights from complex data sets.

Actable AI Details

Vendor
Actable AI
Year Launched
2020
Location
London, GB
Deployment
cloud, on premise, windows, linux
Training Options
documentation, videos, live online
Countries Served
All Countries
Languages
English, Spanish, French, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean
Users
Data Analysts, Data Scientists, Statisticians, Business Analysts, Researchers
Industries Served
Healthcare, Education, Finance, Retail
Tags
No-code AI, Causal inference, Predictive analytics, Time series forecasting, Spreadsheet AI, AutoML, Explainable AI (XAI), Deep clustering, Sentiment analysis, Business intelligence, Machine learning automation, A/B testing

Actable AI's In-App Market Place

Does Actable AI have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

0

Mini Apps

Pricing Options

Free trial
Free version
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Promo Offer

Accepted Payment Currencies

USD ($), GBP (£), EUR (€), JPY (¥), AUD (A$), CAD (C$), CHF (CHF), CNY (¥), SEK (kr), NZD (NZ$), INR (₹), SGD (S$)

Pros & Cons

  • Causal AI capabilities for business users with no-code setup
  • Transparent modeling with SHAP explanations and counterfactuals
  • Versatile toolset: from forecasting to segmentation to statistical testing
  • Deployable models with REST APIs
  • Enterprise-ready with on-premise and white-labeling options
  • Advanced causal features may be unfamiliar to some business analysts
  • Limited flexibility compared to full programming environments (e.g., Python/R)
  • Forecasting and deep learning require understanding of data patterns and assumptions
  • Free plan is restricted to public data and basic model training hours
  • Some expert users may seek more granular control or customization options

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