What Thunder Does
Thunder ingests your conversation data and provides insights through:- Topic Detection - Automatically categorizes conversations into topics
- Satisfaction Signals - Detects user satisfaction (CSAT) and dissatisfaction (DSAT) signals
- Gap Detection - Identifies knowledge gaps and missing tool capabilities
- Repeated Requests - Finds patterns where users have to ask the same thing multiple times
- Usage Metrics - Tracks sessions, users, and message volumes over time
Core Concepts
Instances
An instance represents a distinct product or deployment within your AI application. For example, if you’re building AI clones or agents, each clone would be its own instance. If your product is a single conversational experience, you can use the default instance. All analytics in Thunder are scoped to instances.Sessions
A session is a single conversation between your AI and a user. Sessions belong to instances and contain one or more messages. Each session is analyzed to extract topics, detect satisfaction signals, identify gaps, and find repeated requests.End Users
End users are the people interacting with your AI. Thunder tracks user activity across sessions to help you understand engagement patterns and identify users who may be having trouble.Topics
Thunder analyzes message content to identify topics - the subjects your users are discussing. Topics help you understand what users care about and where to focus improvements.Satisfaction Signals (CSAT/DSAT)
Thunder automatically detects signals of user satisfaction (CSAT) and dissatisfaction (DSAT) within conversations. These signals help you measure the quality of your AI’s responses and identify areas for improvement.Gaps
Gaps represent missing capabilities in your AI. Thunder identifies two types:- Knowledge Gaps - Information the AI doesn’t know but should
- Tool Gaps - Actions the AI can’t perform but users expect