Call Transcript Classifiers

Bootstrap sparsely labeled datasets into fully-trained models. 

Custom classifiers require tens if not hundreds of thousands of labeled examples for training and take human-powered shops months to produce. Eliminate the data labeling bottleneck with Jaxon:

Label your real examples

From a small seed of human-labeled examples, Jaxon autonomously labels the rest. Using weak supervision, transfer learning, and unsupervised data augmentation, Jaxon labels as many examples as you feed him in machine time.


Synthetically create new examples based off real examples by swapping:
– Synonyms
– Random words
– Frequently used words

Synthetic Data from gpt3

Discover gaps in coverage and send a few examples from the underrepresented class to GPT3; synthetically created new examples come back, balancing the training set.

3-Component Neural Model

Most neural networks only analyze the first 512 tokens in a dialog and throw away the rest, wasting time and potentially valuable resources. Compounding the issue further is the additional complexity around dissecting and recombining the dialogs. Jaxon boasts a 3-component neural model that utilizes the entirety of the dialog while strategically separating and recombining the utterances:
– Component 1: Text representation
– Component 2: Attention layer (coordinate different utterances in a single dialog)
– Component 3: Classification

See how Jaxon has helped data scientists and engineers like you build AI faster:

Because of COVID, the vocabulary and the way we are talking about subjects has changed. With Jaxon, we can retrain our model every week or even every day.
Top 15 gaming company
Before Jaxon, we had to create a mini-Jaxon for each use case, cobbling together bespoke training pipelines. Those models often got wasted and it was typically just throwaway code. There was no tooling around automating this process until Jaxon.
Top 10 retailer

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Jaxon is an AI platform that trains other AI to more accurately understand natural language from raw text.

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