Post-call analysis

Post-call analysis

Post-call analysis turns every finished call into structured data — a summary, sentiment, an outcome (disposition), action items, objections, your own custom fields, a QA rubric score, and an LLM-judge scorecard — without anyone listening to a recording. It runs automatically in the background after a call ends and never blocks the call itself.

It is configured per agent (not per organization): each agent has its own enable toggle, custom fields, and QA rubric, saved on the agent as metadata.postCallAnalysis. The analysis runs through Telenow's managed LLM layer, and the token cost is metered and billed to your organization like any other model usage (see Usage & billing).

Analysis results appear on the call's detail page — alongside AI insights, the transcript, and the cost breakdown.

Enable it

In the agent builder, open the Analysis step and turn on Enable post-call analysis for this agent. Doing so adds an Analysis tab to the agent and starts analyzing every call it finishes from then on. Two things are configurable below the toggle:

Which calls get analyzed

Not every finished call is worth an AI pass. A voicemail beep the agent talked over, a hangup one word into the greeting, an IVR that answered and punted — each of those bills a full analysis and returns nothing you would act on. Two thresholds sit directly under the toggle:

Set either to 0 to analyze everything.

Text conversations are measured in replies rather than seconds on purpose. A chat's duration is the wall-clock gap between its first and last message, so it records how long the customer took to answer, not how much was said — a chat resolved in fifteen seconds would be thrown away while one left idle for ten minutes with two messages would be kept.

A skipped call produces no analysis at all — no row, no cost, and no entry in the disposition, objection or QA rollups on the agent's Analysis tab. That last part is the one to weigh before raising the threshold: if a typical "not interested" on your campaign runs 25 seconds and you set the minimum to 45, the rollup will report that you meet almost no objections. 20 seconds is the default because it clears voicemail and instant hangups while still keeping short real outcomes — a confirmation, or a refusal.

Both thresholds are per agent, saved as metadata.postCallAnalysis.minDurationSec and metadata.postCallAnalysis.minCustomerTurns, and are read from the config the call actually ran under — so changing one today never re-grades or retroactively skips calls that already happened.

Custom fields

Your own extraction targets — facts you want pulled out of every call. Click Add field and fill three columns:

Up to 30 custom fields per agent. When a field can't be determined from the transcript the model sets it to null rather than guessing, and (where possible) records a verbatim supporting quote in the call's evidence. Extracted values land in the call's customData, keyed by your key. Entries with a blank key are dropped on save.

QA rubric (agent scorecard)

A list of yes/no criteria the model grades the agent against. Click Add criterion and give each:

• key — a short identifier, e.g. verified_identity, read_disclosure.
• criterion — the yes/no question, e.g. "Did the agent verify the caller's identity before sharing account info?"

Each criterion is scored met / not-met with a supporting verbatim quote, returned in the call's qa array as { key, met, evidence }. The rubric is also capped at 30 entries; keyless rows are dropped.

The config (fields + rubric) is preserved even if you toggle analysis off, so re-enabling later restores your setup.

What you get

Each analyzed call produces:

The talk ratio is reported as those four raw counts (computed deterministically from the transcript, no LLM). The familiar agent-vs-customer percentage is derived from them on the client — so you can compute it however you like.

The LLM-judge passes (score, coaching, hallucinations, cx) are grounded strictly on the transcript and the agent's own system prompt — the judge is told not to invent external facts, which is what makes the hallucination flags meaningful.

When it runs

A background worker claims finished calls roughly every 30 seconds and only looks back a couple of hours, so analysis lands shortly after a call ends. It never blocks the call, survives restarts, and retries on failure. Turning analysis on affects calls going forward — it does not back-fill old calls. Analysis needs a non-empty transcript; calls with no speech are marked failed.

Where to see it

• Call detail — open any call in Calls; the analysis appears as a card once it's ready.
• Agent → Analysis tab — rollups across a date range: average score, sentiment trend, disposition breakdown, top objections, top topics/keywords, talk ratio, and how many calls had hallucination flags. See Analytics.

API & webhook

These are dashboard routes — they return { success, data } and use your normal session auth within an org. When analysis completes, Telenow also fires the call.analyzed webhook carrying the full analysis object (scoped to the call's agent) — the easiest way to push results into your CRM or warehouse without polling. The call.analyzed payload's talkRatio carries the same four counts described above.

Tips

• Write custom-field descriptions like instructions to a junior analyst — the clearer the description, the more reliable the extraction.
• Make QA criteria genuinely yes/no. "Was the call good?" grades poorly; "Did the agent state the cancellation policy?" grades well.
• Use disposition for funnel reporting and customData for the specifics — both aggregate on the Analysis tab.

Troubleshooting

• No analysis card on a call — confirm the agent's Analysis toggle is on, give the worker up to a minute, and check the call actually has a transcript (a call with no caller speech can't be analyzed).
• A custom field is always null — the model couldn't find it in the transcript (by design it won't guess). Tighten the description, or check the information was actually said on the call.
• Old calls have no analysis — analysis only applies going forward; it doesn't back-fill calls from before you enabled it.

Related

• Caller memory — remember individual callers across calls (configured on the same Analysis step).
• Analytics — the agent Analysis-tab rollups in depth.
• Calls, recordings & insights — where analyzed calls surface.
• Webhook events — the call.analyzed payload.

Getting the results out

Everything analysis extracts — summary, sentiment, and your own custom fields — can be written straight into a Google Sheet or Airtable table as each call ends. See Call destinations.