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Tonality Analysis

How the agent sounded, how the customer sounded, and one word for the pair of them together. Chosen words rather than a fixed list, which is what lets them describe a call that closed politely and grudgingly.

How the call sounded

per analysed call
agent tonality
Marcus Hale
warm
customer tonality
Ben Osei
clipped
Overall
brisk
Sentiment, same pass
positive
Notes, one sentence, grounded in the transcript

The customer agreed to the renewal inside a minute and mentioned twice that they were between meetings.

The words are chosen, not picked from a list, so they read rather than chart · the record column keeps the overall one
This one closed, so the sentiment is positive and a dashboard built on sentiment alone would stop there. The tonality is what says the customer was in a hurry the whole way through.
0
pieces the model is asked for
0
of them kept on the record
0
characters the column holds

Product figures from the platform’s own defaults - not customer averages

The mechanism

How it works.

01

Four pieces come back and one is kept

The request asks for the agent's dominant manner, the customer's, a single word for the conversation, and a sentence of evidence. Only the conversation word is validated, and only it is written down, into a fifty character column. The other three are read and dropped.

02

No vocabulary is handed over to choose from

Nothing restricts the answer to an enumerated set, so a call can come back clipped, resigned or apologetic without anyone having predicted those in advance. The cost is that these words read rather than compute.

03

The evidence sentence has to point at something

The instruction asks for a concrete moment rather than a restatement of the label, and nothing enforces it: the sentence is neither validated nor stored. Presets sharpen what to watch for by vertical: frustration and de-escalation on a support line, stress and empathy where a claim is being made.

04

Tallied, because it cannot be averaged

The overnight rollup counts how often each word appears for an agent and stores the whole spread as counts. Since no list exists in advance, a word nobody expected simply turns up in the tally rather than being swept into an other bucket that explains nothing.

Where it sits

One moment in every read.

Every read passes through the same seven. Tonality Analysis is the lit one, and everything either side of it is a different page in this category.

  1. 01
    Source

    the call or the thread it reads

  2. 02
    Transcribe

    audio into words, with speakers

  3. 03
    Read

    the pass over the whole of it

  4. 04
    Judge

    the score, the sentiment, the intent

  5. 05
    Extract

    the fields and follow-ups pulled out

  6. 06
    Write

    what lands back on the record

  7. 07
    Review

    a person checking the machine

The specifics.

7 facts
Asked for
Agent manner, customer manner, one word for the conversation, one sentence of evidence
Kept
That single conversation word, in a fifty character column on the analysis
Vocabulary
Open. No enumeration, no fixed list, and no number attached to any of it
Rollup
Counted per agent per day as a spread, because averaging a word would mean nothing. Written nightly and read as counts
Per speaker on the record
Not persisted. The pair is produced by the model and written to no column
Not the same as
Sentiment is one enum about the outcome. This is the manner of both parties
On mail
Not asked for. Manner of speech is a call idea and the mail path leaves it out

More in Intelligence

12 capabilities

AI that proposes edits to the record - an insight becomes a field once you accept it.

Call Analysis

One reading of the transcript returns the summary, the sentiment, the tone, what each side intended and a set of scores, in a shape fixed in code.

AI CRM Writeback

The model proposes record updates with its confidence and the quote it heard them in, shown as a before-and-after you accept or reject row by row.

Call Summaries

One to six sentences on the record covering why the call happened and what was agreed, so nobody reading the list has to open the audio to find out.

Compliance Scoring

Compliance scored nought to ten against what the vertical says matters, because no phrase list exists to recite. A missing reading is excluded, never passed.

Intent Detection

What the customer was actually asking for and what the agent committed to, extracted as structured intents rather than left inside the transcript.

Follow-Up Extraction

Callbacks and meetings pulled out with their times, a confidence figure and the words they came from, ready to accept in one click or reject in one.

Industry Presets

Collections, healthcare, insurance, admissions and eleven more - each shaping what the analysis looks for without letting anyone break the output shape.

AI Email Analysis

Threads summarised and read for intent and urgency, but only when a participant matches a record, so the model is never called on mail that is not yours.

AI Email Writing

Draft from an instruction or rework a message you have, landing in the composer with a suggested subject and going nowhere at all until you send it.

AI Field Generation

Describe what you track or paste a spreadsheet's columns, and get a named field group back with types, options and placeholders to approve one at a time.

Natural-Language Filtering

A sentence becomes a grid filter and a sort order with the reading explained back, on the toolbar of every grid that carries a column menu of its own.

Bring Your Own Model

OpenAI, Azure, Anthropic, Gemini, Bedrock and ten more, bound per purpose with an ordered fallback chain, a circuit breaker and a cost line on every attempt.

Intelligence

See tonality analysis on your own floor.

Thirty minutes, your numbers and your data. We will set tonality analysis up live and you can decide from the thing itself rather than from this page.

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