#AI call summary
An automatic written recap of a finished call: what was said, what was asked, what happens next.
Full definition and sources
An AI call summary is the post-call write-up generated from the transcript: the reason for the call, what the prospect said, objections raised, commitments made, and the agreed next step. It attacks wrap-up time directly, since the agent edits a draft instead of composing notes from memory. The quality bar is specific: a summary that is fluent but wrong is worse than no summary, because the next person to touch the lead trusts it. Good setups constrain the model to the transcript, structure the output into fixed fields, outcome, next action, follow-up date, and keep the audio for verification. DialBreeze drafts a summary after each recorded call; missing or unusable audio can mean no summary.
Related: Call transcription, Wrap-up time, CRM sync, Disposition
#Call scoring
Grading calls against a rubric, by a reviewer or automatically, to measure script adherence and quality.
Full definition and sources
Call scoring grades a recorded call against a rubric: did the agent identify themselves, deliver required disclosures, follow the script, handle the objection, ask for the appointment. Traditionally a supervisor listened to a sample, maybe two calls per agent per week, and scored by hand. Automated scoring runs a model across every call, flagging the misses and ranking where a human should listen. The value is coverage and consistency, not judgment: rubric items that are objectively checkable, like disclosure presence, automate well, while nuance stays with the reviewer. Floors get the most from scoring when results tie to coaching within the week, not to a monthly report nobody reads.
Related: Conversation intelligence, Call transcription, Whisper / coaching, Sentiment analysis
#Call transcription
Turning the recorded audio of a call into written text, timed and labeled by speaker where the tool supports it.
Full definition and sources
Call transcription converts recorded audio into text, usually with speaker labels so you can see who said what. It is the substrate for everything downstream: searchable call history, QA scoring, AI summaries, sentiment reading, and dispute records. Accuracy varies with audio quality, accents, industry jargon, and cross-talk, so floors treat transcripts as reliable for search and review and verify anything that matters, like a verbal commitment or a compliance disclosure, against the recording itself. Transcripts also inherit the call's privacy weight: they contain everything said, so access control and retention rules should apply to the text exactly as to the audio.
Related: Speech-to-text, AI call summary, Call scoring, Call recording disclosure
#Conversation intelligence
The analytics layer over recorded calls: searchable transcripts, topic and question tracking, and patterns across thousands of conversations.
Full definition and sources
Conversation intelligence is the umbrella for analytics over call recordings at scale: transcribed, searchable, tagged by topic, question, competitor mention, objection, and outcome. It answers floor questions that used to require listening: which objections show up most on this list, what do the top closers ask that others do not, how often does the compliance disclosure actually get delivered, where in the script do prospects drop. The tooling matters less than the questions, since a searchable transcript library with honest outcome data answers most of them. The pattern to want is specific: conversation intelligence should change the script and the list, or it is decoration.
Related: Call scoring, Call transcription, Speech-to-text, Sentiment analysis
#Sentiment analysis
Estimating the emotional tone of a caller from their words and voice, per moment or across the whole call.
Full definition and sources
Sentiment analysis reads a call's tone: frustration, interest, confusion, anger, enthusiasm, estimated from word choice and, in some engines, from pitch and pace. On the floor it works best as a signal, not a verdict: flag the calls where sentiment dropped hard at the pricing line, or the ones that ended hot, and let a human decide what actually happened. Per-moment sentiment is more useful than a whole-call average, which mostly tells you the call was a call. The honest limits: people are polite while refusing, motivated while complaining, and models misread regional dialect and cross-talk. Use it to find the ten calls worth reviewing, not to grade the customer's mood.
Related: Conversation intelligence, Call scoring, Call transcription
#Speech-to-text
The underlying engine, often a speech model, that converts spoken audio into words, powering transcription and live agent aids.
Full definition and sources
Speech to text is the engine underneath transcription: an acoustic and language model that maps audio to words. The same engine increasingly runs live, not just after the call, which is what makes real-time agent assist possible: the model hears the prospect, surfaces the matching script section, flags a missing disclosure, or suggests an answer while the conversation is still going. Engine quality is measured by word error rate, and word error rate rises fast with noisy headsets, cheap audio paths, and domain vocabulary, so floors testing tools should score them on their own recorded calls, not vendor demo clips. Words matter here, because every downstream AI output inherits the mistakes.
Related: Call transcription, Conversation intelligence, AI call summary
#Whisper / coaching
A manager or trainer speaking into the agent's ear during a live call without the customer hearing.
Full definition and sources
Whisper coaching is the floor's live assist channel: the coach selects the agent, talks, and the agent hears it in one earpiece while the customer hears nothing. It is how new agents survive their first weeks, getting the next line fed mid-call, and how managers correct a call going sideways in real time instead of debriefing the wreckage afterward. The mechanics ride the conference bridge the platform already runs. Its close cousins are barge, where the coach joins audibly, and listen, silent monitoring; the three together are the standard supervision trio on calling floors. Use is a management style question: constant whispering builds dependence, occasional whispering builds skill.
Related: Softphone, Call scoring, Handle time