Narus
Manage Generative AI securely without data risks
Target Audience
- Business teams
- Marketing professionals
- IT managers
- Data security officers
Hashtags
Overview
Narus is a secure chat platform that allows teams to access generative AI models safely. It helps businesses optimize their AI investments while ensuring data security and compliance. With Narus, employees can harness the power of AI without the fear of data breaches or unexpected costs.
Key Features
Secure Access
Safely connect to AI models with data protection
Cost Visibility
Real-time insights into AI usage and expenses
Model Management
Easily manage multiple AI models in one platform
Data Privacy
Your data is not used for model training
Granular Permissions
Control user access to AI tools based on roles
Use Cases
Draft emails with AI assistance
Analyze marketing strategies using AI insights
Ensure compliance with data security regulations
Manage AI model integrations seamlessly
Enhance team productivity with AI tools
Pros & Cons
Pros
- Strong focus on data security and compliance
- Multiple AI model access in one platform
- Real-time cost management features
- User-friendly interface for non-technical teams
Cons
- Limited to business applications, may not suit individual users
- Requires an API key for model integration, which could be a barrier
Frequently Asked Questions
What is Narus?
Narus is a generative AI chat portal that helps your teams work smarter, offering multiple large language model connections like GPT-4o, Claude, and Gemini.
Who is Narus for?
Narus has been built for businesses that want to exploit the competitive advantage that AI offers without compromising security.
Where do we store our data?
Narus uses a dedicated AWS instance for data storage, ensuring confidentiality and compliance with data privacy standards.
Is my data used for model training?
No, your data is not used to train the model unless you opt-in for it.
How does Narus calculate my company’s AI cost?
Narus calculates AI costs based on the number of tokens used for input and output interactions with each LLM.
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