AI-enabled next-best-actions for personalising customer-facing digital services
Introduction to Atmosphere
Introduction to Atmosphere
Atmosphere Overview
Atmosphere is a personalisation platform that helps organisations build ethical next-best-action solutions
Client digital services can call the Atmosphere API to get the recommended actions that best serve their customers
The system learns continuously from customer responses using closed-loop machine learning that adapts automatically
Atmosphere has been designed with the Australian AI Ethics Framework in mind in order to enable ethical safeguards
Atmosphere enables your experimentation journey, facilitates personalisation of your customer interactions and increases your overall digital velocity through the use of ethical AI and ML
Atmosphere Capabilities
ML Model Deployment
- Deploy bespoke models using your preferred ML framework and productionise on modern and scalable infrastructure
- Realtime scoring of the model using realtime context
- Monitor your models and track versioning and ownership
- Create API endpoints to serve models to digital channels
Experimentation Platform
- Create scientifically-valid experiments to uncover cause-and-effect relationships to truly understand customer behaviour
- Manage multiple interacting experiments on the same groups of customers within a rigorous experimental framework
- Monitor model performance live, compare methods, evaluate uplift and understand which factors drive performance
Continuous Intelligence
- Atmosphere continuously learns and adapts from the customer responses to its next-best-action recommendations
- The system can learn from real-time data as well as the historical data that is already held in the client environment
- React to changes that affect your customers faster for better market responsiveness
Ethics Monitoring
- Designed with the Australian AI Ethics Framework in mind
- Potential discrimination against sub-groups from automated-decisions can be tracked using bias monitoring features
- Atmosphere allows responsible owners to specify which variables should be monitored for bias, define thresholds and be alerted with notifications if any have been exceeded
Deployment Flexibility
Integrates with your architecture
- A modular design means that you can utilise your existing solutions, or use Atmosphere modules to integrate with your digital services to achieve your decisioning outcomes
- Use your own Data Science platform to deploy your ML models, or use Atmosphere's Model Deployment features
- Use your own MarTech solution to create and manage experiments on live traffic and monitor performance, or use Atmosphere's Experimentation Platform features
Retain control of your data
- Atmosphere gives you full control as its deployment on Kubernetes infrastructure can be done either on-premise, or in your cloud accounts, that you own and administer
- This means that your sensitive data never leaves your control at any point during the full machine learning lifecycle, from building and training models on internal data, through to model deployment and integration with your digital services
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