Provides a user analytics platform for language model (LLM) interactions, leveraging real-time conversation analysis to extract implicit feedback, user intents, and sentiment from text-based interactions. This enables businesses to identify LLM performance issues, conduct A/B testing, and generate user-rated evaluation datasets, improving user experience and aligning AI products with customer needs.
Funding
Funding not disclosed
Founders
Product
Problem
Businesses struggle to understand user behavior and extract actionable insights from interactions with Language Model (LLM) powered products. Traditional analytics tools are not designed for conversational AI, making it difficult to identify user intents, sentiment, and areas for improvement in LLM performance. This lack of user understanding hinders the optimization of LLM applications and limits their alignment with customer needs.
Solution
Nebuly provides a user experience platform specifically designed for LLMs, capturing and analyzing user interactions to provide deep insights into user behavior. The platform extracts implicit feedback, user intents, and sentiment from conversations, enabling businesses to understand user needs and optimize LLM performance. Nebuly facilitates A/B testing of different LLM configurations, allowing businesses to identify the most effective approaches for improving user engagement. The platform also automatically generates user-rated datasets from implicit feedback, which can be used to evaluate and refine LLM models.
Target Audience
The primary target audience includes AI product managers, data scientists, and developers building LLM-powered applications who need to understand user behavior and optimize LLM performance.
Features
- Real-time conversation analysis to extract implicit user feedback and intents.
- A/B testing capabilities to compare different LLM configurations in production.
- Automated generation of user-rated datasets for LLM evaluation pipelines.
- Customizable reports and dashboards for sharing insights across the organization.
- Support for a wide range of language models, including OpenAI, Azure, Anthropic, Hugging Face, AWS Bedrock, and Google.
- Flexible deployment options, including SaaS and self-hosted solutions on Azure, AWS, and GCP.
- SDKs and APIs for easy integration with existing LLM applications.
- Data security measures compliant with SOC2 Type I and GDPR standards.