# Levity integrations > Connect Levity's no-code AI workflows to your tech stack and stop manually labeling, routing, and categorizing data at scale. **Canonical page:** https://tray.ai/connectors/levity-integrations/ **Categories:** LLMs, General automation services **Documentation:** https://tray.ai/documentation/connectors/service/levity ## Overview 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. ## Use cases ### 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. - Route tickets to the correct team instantly, cutting average first-response time - Eliminate manual triage queues and free up support managers for escalations - Apply priority labels consistently, without human fatigue skewing the results ### 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. - Pre-qualify leads before CRM entry so sales teams work the right pipeline - Train Levity on your own deal history for accurate fit scoring - Sync enriched lead data to any CRM without manual data entry ### 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. - Auto-tag thousands of product images with consistent category labels - Flag low-quality or policy-violating images before they reach customers - Speed up catalog go-live times for new product lines ### 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. - Route invoices, contracts, and intake forms to the right team automatically - Cut document processing backlogs with real-time classification - Maintain a full audit trail of how each document was classified and routed ### 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. - Detect emerging product issues or PR risks in real time - Categorize feedback by theme to inform product roadmap decisions - Spend less time manually reviewing social listening dashboards ### 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. - Cut time-to-shortlist by pre-screening applications automatically - Apply consistent screening criteria across every applicant - Push fit scores and tags directly back into your ATS of choice ### 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. - Catch policy violations in real time before content reaches other users - Build a consistent moderation paper trail for compliance reporting - Scale content moderation capacity without proportional headcount growth ## Templates ### 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. Connectors used: Zendesk, Levity, Slack ### 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. Connectors used: HubSpot, Levity ### 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. Connectors used: AWS S3, Levity, Airtable ### 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. Connectors used: Google Drive, Levity, Jira, Slack ### 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. Connectors used: Levity, Jira, SendGrid ### 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. Connectors used: Greenhouse, Levity, Slack ## Challenges Tray.ai solves ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ### 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 helps:** 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. ## Agent features ### Classify Text with AI Models (Agent Tool) 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. ### Classify Images with AI Models (Agent Tool) 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. ### Retrieve Classification Results (Data Source) 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. ### List Available AI Models (Data Source) 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. ### Fetch Model Performance Metrics (Data Source) 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. ### Submit Training Samples (Agent Tool) 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. ### Trigger Model Predictions in Bulk (Agent Tool) 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. ### Route Content Based on Predictions (Agent Tool) 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. ### Monitor Low-Confidence Predictions (Data Source) 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. ### Retrieve Task and Label Configurations (Data Source) 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. ## Learn more - Intelligent Integration: https://tray.ai/platform/intelligent-ipaas/ - Merlin Agent Builder: https://tray.ai/platform/merlin-agent-builder/ - Agent Gateway for MCP: https://tray.ai/platform/agent-gateway/ - Book a demo: https://tray.ai/contact/