Skip to content
Adoptiv

Intent Detection

What the customer asked for and what the agent promised, returned as two separate lists because they are two different obligations and only one of them is yours to keep.

What each side meant

reading the transcript
Transcript
Customer intents
  • Invoice reissued at the agreed rateprimary
  • Credit rather than a carry-over
Agent commitments
  • Reissue the invoice today
  • Raise a credit note
Suggested disposition
waiting for the end of the call
interestednot_interestedcallback_requestedappointment_setvoicemail_leftwrong_numberdo_not_callno_answerfollow_up_neededcompletedother
Two lists rather than one, because what the customer asked for and what the agent promised are different obligations, and only the second one is yours to keep.
0
disposition categories in the closed set
0
lists pulled from one conversation
0
dominant intent named on its own

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

The mechanism

How it works.

01

Two lists, and they are never merged

Customer intents on one side, agent commitments on the other, both as short labels rather than paragraphs. A single dominant intent is named separately and stored beside them, so a call can also be counted by the one thing it was mostly about.

02

A disposition is proposed from a closed set

Eleven codes: interested, not interested, callback requested, appointment set, voicemail left, wrong number, do not call, no answer, follow up needed, completed, other. A readable phrase comes with it and that phrase is not constrained at all.

03

The proposal stays a hint and does not click

It appears only while no disposition has been set, and setting one hides it. Your own catalogue is what a rep picks from, because those eleven codes are the model's vocabulary rather than your reporting categories.

04

This same block carries the writeback

Proposed column edits and proposed follow-ups arrive inside this dimension rather than beside it, which is why leaving it out of a run produces no review queue at all. A sentence of notes sits underneath the lists.

Where it sits

One moment in every read.

Every read passes through the same seven. Intent Detection 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
Returns
A list of customer intents, a list of agent commitments, and one dominant intent
Disposition
One of eleven categories plus a free phrase. Neither is written onto the call
Applying it
By hand. Nothing maps those eleven codes onto the codes your floor uses
Shown
Only while the call carries no disposition. Choosing one removes the hint
Also carries
The proposed column edits and the proposed follow-ups. They are fields of this block
Shape
Free JSON on the analysis row. Its presence is checked, its interior is not
Not the same as
Labels and a code. The summary answers the same call in readable prose

More in Intelligence

12 capabilities

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

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.

Sentiment Analysis

Positive, neutral or negative on every analysed call, stored in a column of its own so the ones that went wrong are a filter rather than a listening exercise.

Tonality Analysis

Chosen words rather than a fixed list for how the agent sounded, how the customer did, and the conversation overall - the manner behind the sentiment.

Call Scoring & QA

Communication, resolution, satisfaction and compliance scored nought to ten beside a separately judged overall, on every call that clears the analysis gates.

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.

Diarized Transcripts

Four of six speech paths return the channel each side was recorded on, so the transcript knows who spoke rather than guessing, and one control swaps a pair.

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 intent detection on your own floor.

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

14-day trial · no card · migration included