Enterprise AI Pain Point
85% of AI and machine learning projects fail to deliver.
53% of these projects make it from prototype to production.
Blockers to successful AI adoption:
- Lack of Expertise
- Time Intensive
- Complex Development
- Difficulties in Data Integration
Key Components for a Successful AI Application Implementation
- End-to-End Lifecycle From devices to automation
- No-Code Solution Easy to develop and integrate
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From Data to Automation with No-Code Technology
Complete AI Lifecycle with Sway AI & Interactor
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No-Code Automation Platform enabling fast API development and integration | |
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No-Code Automation Platform enabling fast AI application development and deployment |
We Solve Real-World Enterprise Problems
Telco - Customer Story in Action
- AI-Enabled Intelligent traffic management
- Collect (tag, process, and store) network data for inference to the AI layer
- Prevent lowering of network performance due to unexpected events way in advance
- Create insights on the types of network traffic with any granularity required by the operator
- Automate the actions for improved network performance
What's Unique?
- Fully no-code Minimal learning curve for anyone in the company to quickly leap the benefits.
- Easy Deploy Deployment in minutes. Runs on most common OSes. No other SW dependency.
- Lifecycle Management Minimal Effort. Non or simple reconfiguration when APIs change.
- A large number of connectors API AutoFlow: 3,000+
- Full development features API AutoFlow provides full development features needed to create complex solutions.
- High performance and scalable Extremely low latency and high TPS through proprietary technology.
- Business Logic: The user defines flexible business logic and automatically acts based on AI predictions.
- Drag & Drop User Interface: Intuitive user interface, no coding required, built-in expertise, easy to use.
- User Collaboration: The Business User and Technical User work together to build workflows and experience rapid experimentation and deployment.
- Best-in-Class Models: Automatically updates models and can pick/test different models prior to deployment.
- Open Architecture: Leveraging open-source AI models allows customers to utilize their own models and integrate with best-in-class vendors.
- Multi-Cloud: Platform agnostic with freedom and reduced costs.
