DeepAdvisor provides a low-code platform that enables businesses to develop and implement generative AI use cases across various functions, such as sales and finance, without requiring programming skills. The platform enhances productivity and decision-making by allowing users to interact with both structured and unstructured data while ensuring security and compliance.
Funding
Funding not disclosed
Founders
Product
Problem
Many businesses struggle to effectively implement and scale generative AI (GenAI) use cases due to the complexity of integrating structured and unstructured data, the need for specialized programming skills, and concerns around data security and compliance. This often leads to delayed deployments, underutilization of AI capabilities, and increased reliance on IT resources.
Solution
DeepAdvisor offers a low-code platform designed to simplify the development and deployment of GenAI applications across various business functions. The platform enables users to interact with both structured and unstructured data, leveraging pre-built use case templates and a visual development environment to accelerate the creation of AI-powered solutions. By providing a user-friendly interface and multi-layered security features, DeepAdvisor empowers business users to implement GenAI without extensive programming knowledge, while ensuring data privacy and adherence to organizational policies. The platform also supports integration with multiple Large Language Models (LLMs), offering flexibility and adaptability to different AI models.
Target Audience
DeepAdvisor targets business professionals across various departments, such as sales, finance, and marketing, who seek to leverage GenAI to improve productivity, automate tasks, and enhance decision-making.
Features
- Low-code IDE for building GenAI use cases across sales, marketing, finance, procurement, and HR.
- Pre-designed, customizable use case templates based on common business scenarios.
- Ability to combine company-specific data with global LLM information for contextually relevant outputs.
- Multi-LLM architecture supporting integration with various AI models.
- Role-based access control and custom access management for data security.
- Option for private model deployment to ensure data confidentiality and compliance.
- Performance management tools to plan and measure the impact of AI initiatives.
- Usage tracker to monitor platform engagement and optimize efficiency.