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Natural-Language Filtering

Type the list you want and it builds the filter and the sort for you, then says in one sentence what it understood. That last part is the point: a filter nobody typed is only worth trusting when it says what it did.

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Reading the sentence

parsed
What you asked for

hot leads in California, newest first

Where
ratingishot
stateisCA
Order
created, newest first
And what it says it read

Two conditions, both of which must hold, and no date range because you named none.

The filter model and the sort model are handed to the grid together, so the rows narrow and reorder in one paint rather than settling twice. The sentence back is how you tell a filter that answered your question from one that answered a nearby question convincingly.
0
kinds of condition it can build
0
lists you can ask this way
0
sentence of explanation with every answer

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

The mechanism

How it works.

01

The columns travel with the question

The request carries the grid's own column definitions, so the answer names fields that exist rather than fields it imagined. A grid asking on behalf of leads and one asking on behalf of deals therefore get different answers out of the same sentence.

02

Four kinds of condition come back

Text with contains, equals, starts with and their negatives. Numbers with the comparisons and a range. Sets with a list of values. Dates with a from and a to. A sort order arrives beside it as a column and a direction.

03

An explanation travels with the answer

It states what was understood, in one sentence, and that sentence is shown with the result. Reading it is how you tell a filter that answered your question from one that answered a nearby question convincingly.

04

The rows narrow and reorder in one go

The filter and the sort are handed to the grid together, so nothing settles twice while you watch it. Once they land they are ordinary grid state, which is why the ordinary clear control takes them off again.

Where it sits

One moment in every read.

Every read passes through the same seven. Natural-Language Filtering 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.

8 facts
Endpoint
Takes a sentence, a record kind, and the column definitions of the grid that asked
Returns
A filter model, a sort model, and one sentence explaining the reading
Condition families
Text, number, set and date, each with the operators that kind allows
Mounted
On the toolbar of all nineteen grids that carry one
What it applies
Both halves. The conditions narrow the rows and the sort reorders them together
Clearing it
The grid's own clear control, because what arrived is ordinary filter state
Shown back
The explanation sits with the result, so a filter nobody typed still accounts for itself
Not the same as
This writes the filter from a sentence. Keeping one you built by hand is Saved Views

More in Intelligence

13 capabilities

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

Call Analysis

A supervisor who used to open the audio reads it instead: what the call was about, how it went, what was asked for, what it scored. One reading, one answer.

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

Finding yesterday's bad calls is a filter, not an afternoon. Positive, neutral or negative on every analyzed call, kept in a field of its own you can sort on.

Tonality Analysis

How the agent sounded, how the customer sounded, one word for the pair. Chosen words rather than a fixed list, so a call can close politely and grudgingly.

Call Scoring & QA

A month of calls gets reviewed by whoever had time. Every call carries five numbers instead, and the overall is judged in its own right rather than averaged.

Compliance Scoring

Nobody re-listens to check the disclosure went out. Every call is scored against what compliance means on your floor, with a missing one excluded, not passed.

Intent Detection

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

Follow-Up Extraction

A promise made out loud is worth nothing until it sits on a day. Callbacks and meetings come off the call with the time said, real the moment you accept.

Diarized Transcripts

A coaching note never quotes the customer back at a rep who did not say it. Four of six engines return the channel each side was on, one control swaps them.

Industry Presets

A collections call and an admissions call are not judged the same way. Fifteen verticals ship knowing the difference. What comes back still reads the same.

AI Email Analysis

A thread is read only when somebody on it is one of your leads. No match and nothing is sent anywhere, so mail that is not about a customer is left alone.

AI Email Writing

A draft or a rewrite that lands in the composer and stops there. The subject is a suggestion, every word editable, and nothing leaves until a person sends it.

Bring Your Own Model

OpenAI, Azure, Anthropic, Gemini, Bedrock and ten more. When one starts timing out mid-afternoon the work moves down the chain rather than stopping on you.

Intelligence

See natural-language filtering on your own floor.

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

See pricing

14-day trial · no card · migration included