Levity connector
Automate AI Classification and Tagging with Levity Integrations
Connect Levity's no-code AI workflows to your tech stack and stop manually labeling, routing, and categorizing data at scale.
What can you do with the Levity connector?
Levity lets teams build custom AI models that classify text, images, and documents without writing a single line of code — but that's only useful if those models are wired into your actual workflows. Integrating Levity with tray.ai means every email, support ticket, document upload, or form submission gets automatically analyzed, tagged, and routed in real time. Triaging customer feedback, sorting product images, qualifying inbound leads — whatever the job, connecting Levity to the rest of your stack removes the human bottleneck entirely.
Automate & integrate Levity
Automating Levity business process or integrating Levity data is made easy with tray.ai
Use case
Automated Customer Support Ticket Triage
Feed incoming support tickets from Zendesk, Intercom, or Freshdesk directly into a Levity classifier that categorizes them by topic, urgency, and sentiment. tray.ai then routes each ticket to the right team, sets priority levels, and triggers SLA timers automatically. No more manual queue reviews slowing down first-response times.
Use case
Intelligent Lead Qualification and CRM Enrichment
When new leads arrive via web forms, email, or chat, Levity can classify their intent, industry, and fit against your ideal customer profile before the data ever touches your CRM. tray.ai passes the enriched classification results into Salesforce, HubSpot, or Pipedrive so sales reps only spend time on high-confidence leads. Custom AI models trained on your historical win/loss data make the scoring specific to your business, not a generic template.
Use case
E-commerce Product Image Classification and Tagging
Retailers and marketplaces dealing with high volumes of product uploads can use Levity to automatically classify images by category, detect quality issues, or flag policy violations before they go live. tray.ai triggers the classification workflow when new images land in Shopify, Cloudinary, or an S3 bucket, then writes tags back to the product record and notifies reviewers of flagged content. Catalog management gets faster without adding headcount.
Use case
Document Routing and Approval Workflow Automation
Businesses processing high volumes of contracts, invoices, or intake forms can use Levity to classify document types and extract intent, then have tray.ai route them to the right approver, archive, or downstream system. Connect Levity to DocuSign, Google Drive, or SharePoint and documents land where they belong without anyone reading them first. Pair with Slack or email notifications to keep stakeholders in the loop.
Use case
Social Media and Review Sentiment Monitoring
Pull brand mentions, app store reviews, or social media comments into a Levity model trained to detect sentiment, product feedback themes, and escalation signals. tray.ai handles data collection from Sprout Social, Brandwatch, or Google Play and pushes classified insights into your product team's Jira board or a Slack channel for immediate action. You replace the manual monitoring dashboard with alerts that fire when something actually needs attention.
Use case
HR Resume and Application Screening
Recruiting teams can train Levity models on the characteristics of successful past hires to automatically screen and classify inbound applications from Greenhouse, Lever, or email. tray.ai picks up new applications, runs them through the Levity classifier, and updates the ATS with a fit score and classification tags so recruiters focus on the strongest candidates. Custom models mean the screening criteria actually reflect your specific role requirements.
Use case
Content Moderation at Scale
Platforms handling user-generated content can deploy Levity classifiers to detect policy-violating text or images and trigger automated moderation actions via tray.ai. When Levity flags content, tray.ai can hide it, notify a human reviewer, log the decision, and update the user record — all within seconds of submission. Moderation queues stay manageable without a proportional increase in trust-and-safety headcount.
Build Levity Agents
Give agents secure and governed access to Levity through Agent Builder and Agent Gateway for MCP.
Agent Tool
Classify Text with AI Models
An agent can submit text to Levity's custom AI models for classification, automating the categorization of emails, support tickets, feedback, or any unstructured text without manual review.
Agent Tool
Classify Images with AI Models
An agent can send images to Levity for AI-powered classification, automating the tagging, sorting, or routing of visual content like product photos, documents, or scanned files.
Data Source
Retrieve Classification Results
An agent can pull classification results and confidence scores from Levity to inform downstream decisions, like routing a ticket to the right team or triggering a specific workflow branch.
Data Source
List Available AI Models
An agent can query Levity to see all configured AI models and their associated tasks, so it can pick the right model for a given classification job on the fly.
Data Source
Fetch Model Performance Metrics
An agent can retrieve accuracy and performance stats for Levity AI models, letting it flag underperforming models or recommend retraining when confidence scores drop below a threshold.
Agent Tool
Submit Training Samples
An agent can push new labeled examples to Levity to improve model accuracy over time, closing the feedback loop by capturing edge cases or misclassifications from live workflows.
Agent Tool
Trigger Model Predictions in Bulk
An agent can batch-submit large sets of records to Levity for classification in a single operation, making it practical to work through backlogs of historical emails or product catalog entries.
Agent Tool
Route Content Based on Predictions
An agent can use Levity prediction outputs to automatically route content to the right destination — assigning a support ticket category, tagging a CRM record, or moving a file to the correct folder.
Data Source
Monitor Low-Confidence Predictions
An agent can continuously check Levity for predictions that fall below a confidence threshold and surface them for human review, keeping quality control intact in automated classification pipelines.
Data Source
Retrieve Task and Label Configurations
An agent can fetch the label sets and task definitions configured in Levity, then use that information to validate inputs, build dynamic forms, or explain classification logic to downstream users.
Get started with our Levity connector today
If you would like to get started with the tray.ai Levity connector today then speak to one of our team.
Levity Challenges
What challenges are there when working with Levity and how will using Tray.ai help?
Challenge
Keeping AI Models in Sync with Changing Business Logic
As products evolve and customer language shifts, AI classification models trained on older data can drift and produce inaccurate outputs. Most teams have no systematic way to catch when accuracy degrades or to kick off a retraining cycle before the damage compounds.
How Tray.ai Can Help:
tray.ai can monitor Levity model confidence scores on every classification event and automatically flag low-confidence outputs to a human reviewer queue in Airtable or a Slack channel. Uncertain predictions get reviewed, corrected, and fed back into Levity for retraining — no separate monitoring tool required.
Challenge
Handling High-Volume Data Throughput Without Rate Limit Errors
Sending thousands of documents, tickets, or images through a Levity model in a short burst causes API rate limiting, dropped requests, and incomplete processing — especially during batch migrations or peak traffic events. It's the kind of problem that's invisible until something goes missing.
How Tray.ai Can Help:
tray.ai's built-in queue management and retry logic handles Levity API rate limits by spacing out requests, batching payloads where possible, and automatically retrying failed calls with exponential backoff. Every item gets processed without manual intervention or data loss.
Challenge
Mapping Levity Classification Outputs to Downstream System Fields
Different systems use different field schemas, picklist values, and data types for categories and tags. Translating Levity's output labels into the exact format your CRM, helpdesk, or database expects usually means custom transformation logic that breaks whenever something changes.
How Tray.ai Can Help:
tray.ai's data mapping and transformation tools let you build reusable translation layers that convert Levity classification labels to the exact field values each downstream system needs. When Levity labels change or new classes are added, you update the mapping in one place rather than hunting through multiple scripts.
Challenge
Orchestrating Multi-Step Workflows Triggered by Classification Results
A single Levity classification result often needs to trigger several actions across multiple systems — update a CRM, send a notification, create a task, log to a data warehouse — all conditionally based on which class was assigned. That branching logic is hard to build and harder to maintain without a proper orchestration layer.
How Tray.ai Can Help:
tray.ai's conditional branching and multi-connector orchestration lets you define exactly which downstream actions fire for each Levity classification output. You can build complex if-this-then-that chains across Salesforce, Slack, Jira, and any other tool without custom code, and update the logic visually as your workflows change.
Challenge
Securing Sensitive Data Passed Through AI Classification Pipelines
Documents, support messages, and resumes often contain PII, financial data, or confidential business information. Sending that material to an external AI service raises legitimate questions about what gets retained and whether it feeds back into model training.
How Tray.ai Can Help:
tray.ai lets you build pre-processing steps that scrub or mask sensitive fields before data reaches Levity, so only the relevant text or image content goes to the classifier. Combined with tray.ai's enterprise-grade security controls and audit logging, you get full visibility over every data flow in your AI pipeline.
Talk to our team to learn how to connect Levity with your stack
Find the tray.ai connector with one of the 700+ other connectors in the tray.ai connector library to integrate your stack.
Integrate Levity With Your Stack
The Tray.ai connector library can help you integrate Levity with the rest of your stack. See what Tray.ai can help you integrate Levity with.
Start using our pre-built Levity templates today
Start from scratch or use one of our pre-built Levity templates to quickly solve your most common use cases.
Template
Zendesk Ticket Auto-Triage with Levity
Automatically classifies new Zendesk tickets by category and urgency using a Levity AI model, then sets ticket priority, assigns the correct group, and sends a Slack notification to the on-call team lead.
Steps:
- Trigger on new Zendesk ticket creation via webhook
- Send ticket subject and body text to Levity classification model
- Write Levity classification output back to Zendesk as tags and priority level
- Post a formatted Slack message to the relevant team channel with classification details
Connectors Used: Zendesk, Levity, Slack
Template
HubSpot Lead Scoring with Levity AI
Classifies new HubSpot form submissions through a Levity intent and fit model, then updates the contact record with a lead score property and enrolls high-fit leads in a targeted sales sequence.
Steps:
- Trigger when a new HubSpot contact is created from a form submission
- Send contact properties and message content to Levity for intent classification
- Update HubSpot contact with Levity score and classification labels
- Enroll contacts above a score threshold into a HubSpot sales sequence automatically
Connectors Used: HubSpot, Levity
Template
S3 Product Image Tagging Pipeline
Watches an S3 bucket for new product image uploads, runs each image through a Levity image classification model, and writes category tags and quality flags back to an Airtable product database.
Steps:
- Trigger on new object creation event in the designated S3 bucket
- Pass the image URL to the Levity image classification model
- Receive category, subcategory, and quality score from Levity
- Create or update the corresponding Airtable product record with all classification data
Connectors Used: AWS S3, Levity, Airtable
Template
Google Drive Document Router with Levity
Monitors a Google Drive intake folder for new file uploads, uses Levity to classify document type and urgency, then moves the file to the correct subfolder and creates a Jira ticket for the responsible team.
Steps:
- Trigger on new file added to a specified Google Drive intake folder
- Extract document text and send to Levity document classifier
- Move file to the correct Google Drive subfolder based on Levity output
- Create a Jira issue with document metadata and classification result
- Send Slack notification to the assigned team with a direct link to the document
Connectors Used: Google Drive, Levity, Jira, Slack
Template
App Store Review Sentiment to Jira
Pulls new app store reviews on a scheduled basis, classifies each review's sentiment and product theme with Levity, then creates Jira issues for negative reviews and aggregates positive feedback into a weekly digest email.
Steps:
- Schedule trigger to fetch new app store reviews every few hours
- Send each review body to Levity for sentiment and theme classification
- Create a Jira bug or feedback ticket for reviews classified as negative or critical
- Aggregate positive and neutral reviews into a digest payload
- Send a weekly summary email via SendGrid to the product team
Connectors Used: Levity, Jira, SendGrid
Template
Greenhouse Applicant Pre-Screening with Levity
Automatically scores new job applications in Greenhouse using a custom Levity model trained on successful past hires, then tags candidates in Greenhouse and notifies recruiters via Slack when a high-fit applicant comes through.
Steps:
- Trigger on new application submitted in Greenhouse
- Send resume text and application answers to the Levity fit classifier
- Write fit score and classification tags back to the Greenhouse candidate profile
- Post a Slack alert to the recruiting channel for any candidate scoring above the fit threshold
Connectors Used: Greenhouse, Levity, Slack