LiteLedger analyzes the conversations captured by your LiteLLM proxy to show what customers are trying to achieve, where they get stuck, and what helps them succeed. Follow the path from their messages to a customer report and shared product priorities.
See the sample report →The standard conversation-analysis path builds context in five stages. Each step turns the previous output into a broader view, from a human message to patterns across analyzed customers.
Separate customer messages from automated calls, then group the human messages into sessions. This gives the analysis a view of what the person said and tried to do.
Read human messages for sentiment, stated goals, frustration, confusion, success, and delight. Quoted signals provide context for the session analysis that follows.
Bring the turn signals together: what was the customer trying to achieve, what happened along the way, and did they make progress? Session summaries describe recurring friction, recovery, and features adopted.
Combine the sessions in the selected analysis window into a customer journey, health score, churn-risk tier, behavioral profile, and recommended actions. Use the report to decide which accounts need a closer look and how to follow up.
Bring completed customer reports together to identify shared themes, recurring friction, positive signals, and product priorities. The Cohort Report covers the customers analyzed and suggests hypotheses for your team to investigate.
One customer message can trigger planning, tool calls, and retries. LiteLedger separates those automated calls from human messages before conversation analysis, so customer sentiment is grounded in what the person expressed.
Inspect classifications and correct individual turns. Editable rules determine which messages count as human; unrecognized traffic defaults to machine.
Use optional AI suggestions to draft rules from your corrections, then review and accept the ones that fit your traffic.
Interaction views connect human messages with related automated calls, helping you understand the cost of serving a customer request.
AI identifies qualitative signals and writes the narrative. Code calculates the health score and churn-risk tier from structured analysis inputs, including session health, engagement trend, and activity recency. The report connects those indicators to the customer’s sessions and proposed next steps.
A churn-risk tier helps prioritize investigation; it is not a measured probability that the customer will leave. Read it alongside the session story and your team’s knowledge of the account.
Bring usage, spend, and customer-analysis results into a configurable dashboard. Review friction themes, customer quotes, health, and segments to decide which accounts or product experiences deserve attention.
Quote views bring together the frustration, confusion, success, and delight signals identified in analyzed conversations.
Read health and risk alongside spend and usage to prioritize follow-up with context.
Use the Cohort Report’s themes and recommendations to frame a product question, then explore the customer reports behind it.
Qualitative views reflect completed analyses. Choose the customers and analysis scope that fit the question you want to answer.
The analysis console shows stage progress and session status during a run. You can see which sessions are queued, running, or complete while the report is being built.
Ask retrieves workspace metrics, Cohort Reports, customer reports, session summaries, and synced conversation text to answer questions in plain English. Start with a broad question, then follow up on a specific customer or session.
“What friction appears across the analyzed customers?”
“Summarize this customer’s recent sessions and suggested actions.”
“Find conversations mentioning the export workflow.”
Add product documentation to Primer to give the analysis and Ask your product terminology and context.
A LiteLLM proxy with conversation logs, a URL LiteLedger can reach, and a valid proxy credential. Connect and test it before syncing, then configure your supported AI provider. See setup requirements →
Choose customers and an analysis window from your synced history. Recognized account-key families are grouped into one customer. Reports describe the selected scope; the Cohort Report brings together available completed customer reports.
Configure your own provider credentials. Reports record an estimated analysis cost so you can review usage alongside the output. Explore provider choices and cost →
Workspace members share access to workspace data, with membership and settings managed by admins. Review data handling and customer controls →
The illustrative sample brings health, behavior, session history, and recommended actions together. See how the output could support your team’s next decision.