Call Analysis

After a call ends, Osvi can run the transcript through an LLM and turn the conversation into structured results: a summary, the specific data points you asked for, tags, and callback detection — then deliver all of it to your webhook or a Google Sheet. Everything here is configured per agent on the Call Analysis tab. Turn Post-Call Analysis on at the top of the tab, then work through its sections: LLM Model, Callback, Analysis Prompt, Data Extraction, Integrations, Call Tags, and Do Not Disturb.

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Analysis model and prompt

• LLM Model — the provider and model used for analysis, independent of the model the agent uses live on the call. Enable reasoning lets the model think before answering: better results on complex calls, but slower and more expensive.
• Analysis Prompt — instructions for the analysis pass: what the summary should focus on, what counts as a successful call, anything domain-specific the model should pay attention to.

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Data extraction

Define the fields you want pulled out of every conversation. For each field you set: Extracted values appear on each call’s Analysis tab in Conversations, in webhook payloads under data_capture, and in CSV exports.

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Output schema

Beyond flat fields, you can define a custom output schema — a JSON shape the analysis must produce. Use simple mode for a fixed structure, or conditional mode when the shape depends on how the call went (e.g. an appointment object only when one was booked). The result is delivered as structured_output in webhooks.

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Call tags

Add Tag gives each tag a name, a colour, and a description telling the model when it applies — “Apply when the caller successfully verified their identity…”. The analysis pass then tags each call. Tags show up as a filter in Conversations, so they’re the fastest way to slice call outcomes without reading transcripts.

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Callbacks

Turn on Enable Callback and the analysis pass watches for moments where the caller asked to be called back (“call me after 6pm”), then schedules the follow-up.

• Incomplete Call Retries — how many times to call back when a call ends incomplete, from 0 to 10. Counted separately from campaign retries; 0 turns them off.
• Callback Prompt — how the agent should decide the callback time. It returns a delay in seconds, so the prompt is where you set the rules: how long to wait after an incomplete call, how to read “in ten minutes” against a specific time of day, and what to do outside working hours.

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Do Not Disturb

Turn on Enable DND Detection and numbers are added automatically when a caller asks not to be contacted again. Campaigns skip every number on the list, and you can search it from this tab.

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Integrations — getting results out

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Webhooks

Send analysis results to any HTTP endpoint you control. Add Integration creates one; each agent can have several, and each fires independently after every analysed call. You control the method, URL (with {{placeholder}} support), headers, and body — either auto mode, where the chips on each row show which data sections are included (call info, call summary, data capture, call trigger data, structured output), or a fully custom JSON template. Each row has a switch to turn the webhook off without deleting it, and a send button that posts a sample payload so you can check your endpoint before going live. The complete payload contract, template expression reference, and delivery semantics are documented in the post-call webhook reference.

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Google Sheets

Append a row to a spreadsheet after every analysed call — connect your Google account, pick the spreadsheet and sheet, and map analysis fields to columns. Useful when the destination is a human reviewing outcomes rather than a system.

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Integration logs

Every delivery attempt — payload, response status, response body — is recorded under the Logs tab next to Webhooks. Failed deliveries can be retried from here, individually or in bulk.