Phospho provides a data analytics platform for large language model (LLM) applications, enabling product managers to quantify user engagement, application quality, and usage metrics through real-time data integration and analysis. The platform addresses the challenge of understanding user interactions and feedback, allowing businesses to make informed decisions and reduce churn.
Funding
$2.4M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Understanding user interactions and feedback within large language model (LLM) applications is challenging, making it difficult for product managers to quantify user engagement, application quality, and usage metrics. This lack of insight hinders informed decision-making and can lead to increased churn.
Solution
Phospho provides a text analytics platform designed to help product managers analyze user messages within LLM applications. The platform enables users to connect to various data sources, including APIs, CSV files, and SDKs, to import data in real time. Phospho offers features such as clustering, A/B testing, data labeling, and user analytics to help users discover use cases, detect misuse, assess ticket resolution, and segment user cohorts. The platform's data visualization tools and multi-user experience facilitate collaboration and provide insights into user behavior and preferences, allowing businesses to iterate faster, reduce churn, and improve their conversational experiences.
Target Audience
The primary target audience includes product managers and AI builders who need to analyze user interactions within LLM applications to improve user engagement and application quality.
Features
- Connects to various data sources, including Phospho API, CSV, JS SDK, LangSmith, Langfuse, and OpenAI.
- Offers no-code analytics for quick insights.
- Facilitates A/B testing to compare different versions of LLM applications across multiple KPIs.
- Provides tools to classify, tag, score, and annotate data for model improvement.
- Identifies power users, computes churn and activation rates, and calculates cost per user (tokens).
- Discovers use cases, detects outliers or misuse, and segments user cohorts.
- Includes clustering to group similar conversations and identify patterns.
- Offers data labeling capabilities for efficient data categorization and annotation.