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By 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Gartner Predicts 2020

Automated evidence-based decision making for customer-facing digital services

Continuous Intelligence is a design pattern in which real-time analytics are integrated into business operations, processing current and historical data to prescribe actions in response to business moments and other events.

Overview

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Atmosphere helps our clients build digital services for their customers.

Client services call the Atmosphere API to get recommended actions that best serve their customers.

The system learns continuously from customer responses using closed-loop machine learning.

The system performance is evaluated within a rigorous experimental framework, letting our clients know if and how they affect customer behaviour.

The system can learn from real-time data as well as historical data held by the client.

Our user interface allows digital teams, data scientists and data engineers to collaborate and drive intelligent digital services.

Benefits

Atmosphere enables your experimentation journey, facilitates personalisation of your customer interactions and increases your overall digital velocity.

Cause-and-effect

Experiment to uncover cause-and-effect for better customer understanding.

Return-on-data

Unlock your internal data in customer decisioning for better return on your data investment

Business Agility

Deploy new customer interactions quickly for better business agility

Market Responsiveness

React to changes that affect your customers faster for better market responsiveness

Features

Enable digital experimentation with machine learning for your organisation

Real-Time Decisioning

Empower customer-facing systems with real-time recommendations from an AI system that learns from every interaction in real-time

Infrastructure Flexibility

Deploy on your cloud or on your premises to enable activation of your internal customer data, never having to expose it externally

ML Model Deployments

Productionise your own machine learning models in a modern, scalable deployment infrastructure

Performance Monitoring

Monitor how models are performing live, comparing methods, evaluating lift, and understand factors driving performance

Experimentation Management

Manage multiple interacting experiments on the same groups of customers

Team Collaboration

User Interface designed to allow digital, analytics and data teams to collaborate effectively on AI driven experiments

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