Feedback Intelligence is a product analytics tool designed for LLM-based applications such as chatbots, voice agents, conversational AI. Think of it as Mixpanel or Amplitude, but tailored specifically for LLM applications.

Feedback Intelligence gathers and processes user interactions, converting implicit feedback into actionable insights. It uses NLP-driven analysis to detect patterns, uncover issues, and make recommendations, helping AI teams continuously optimize their LLM applications.

Feedback Intelligence helps developers, data scientists, and product managers capture and analyze implicit feedback—user interactions, intent, and behavior — to diagnose and optimize app performance.

Explicit feedback is directly provided by users, such as ratings or written comments.
‍Implicit feedback is inferred from user actions, like interactions, navigation paths, or repeated queries.
Both types are essential for a comprehensive understanding of how users engage with an app.

Implicit feedback includes user actions and engagement patterns that provide indirect insights into user satisfaction, needs, intents, and ethics. Feedback Intelligence captures and analyzes these patterns to detect emerging issues and optimize the app based on real-world use without needing direct user input.

Feedback Intelligence uses synthetic data generation and augmentation techniques with implicit feedback analytics insights to enhance model training when additional data is needed. By replicating real-world scenarios and variations, we ensure the model can perform well under diverse conditions, even with limited initial data.

Feedback Intelligence uses pattern recognition and clustering algorithms to identify common themes or anomalies in feedback. It traces these patterns to their root causes, such as LLM apps limitations (eg., knowledge holes, bad propmts) or user misunderstanding, providing clear recommendations to address and prioritize fixes.

Root cause analysis is a method Feedback Intelligence uses to pinpoint the reason of underlying issues that affect LLM performance. By identifying the source of a problem, it helps teams resolve issues at their origin, improving overall app reliability and user experience.

Feedback Intelligence is adaptable; it can start with predefined issue labels but also dynamically identify and cluster emerging issues over time. This allows the platform to adjust as new patterns and user behaviors appear, ensuring continuous and comprehensive optimization.

Feedback Intelligence continuously monitors and analyzes output quality based on both implicit and explicit feedback signals. It detects inconsistencies, accuracy issues, or misalignments in responses, allowing the team to address these issues proactively.

User Satisfaction Score: Measures overall user contentment with app responses, derived from feedback and engagement patterns.
‍User Intent Score: Evaluates how well the app’s responses match user intent, helping identify misinterpretations or misalignments that need improvement.

Feedback Intelligence is used across applications such as virtual assistants, customer service chatbots, content generation tools, and other LLM-powered solutions in sectors like healthcare, finance, and e-commerce, where user alignment and reliability are essential.

Feedback Intelligence supports a range of LLM-based applications, including conversational AI, virtual assistants, and customer support bots, and integrates easily with most platforms using APIs.

Yes, Feedback Intelligence captures and processes feedback in real time, allowing teams to respond to emerging issues promptly and continuously optimize the user experience.

Check out our docs https://docs.feedbackintelligence.ai/.

Feedback Intelligence provides APIs and documentation to make integration seamless. It connects directly with your LLM app to capture and process feedback, offering a user-friendly dashboard for monitoring insights and recommendations.