Question 1

What kind of text work can Levity actually automate?

Accepted Answer

Levity automates manual text processing tasks: classifying incoming messages or documents into categories, summarizing long text into brief overviews, extracting specific data points from unstructured content, and generating new text based on templates or patterns. For example, you can route incoming records based on content type, pull key details from form submissions automatically, or create standardized summaries without human review. The work that gets automated is the repeated reading, categorization, or data entry that currently requires someone to process text by hand.

Question 2

How does data move between Levity and my other tools?

Accepted Answer

Levity connects to other platforms through API integrations and automation workflows. When you connect Levity to another tool, incoming data (like form responses, emails, or database records) flows to Levity for processing, and the results flow back out to your connected systems. For instance, a record can arrive from a form platform, Levity can classify or extract data from it, and the enriched record can then be sent to your CRM or database. The data pathway depends on how you configure the connection in your automation platform.

Question 3

What are common problems when using Levity in automation?

Accepted Answer

Levity can miss edge cases if the text patterns differ from what it was trained on, leading to misclassifications or incomplete extractions. Training it with too few or unrepresentative examples reduces accuracy. Typos, formatting inconsistencies, or language variations in incoming data can also cause failures. Additionally, if Levity is misconfigured in your workflow (wrong input field mapped, incorrect output format), data may not flow correctly to downstream tools. Testing with real, messy data before full deployment helps catch these issues early.

Question 4

How should I set up Levity models to keep automations reliable?

Accepted Answer

Train your Levity models with diverse, high-quality examples that represent the actual data you receive, including variations in wording and format. Be specific about what you want classified, extracted, or generated, and define categories or outputs clearly upfront. Map input fields correctly to ensure Levity receives complete text, and validate output formats match what your downstream tools expect. Monitor the model's performance after launch by spot-checking results, and retrain periodically if your incoming data patterns shift or accuracy drops.

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