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What is LLMOps?

LLMOps without the glue code

Version prompts, test across models, deploy to production, and monitor usage. One platform replaces fragmented tooling.

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app.aisle.sh

Customer Feedback Analyzer

v12 · Last edited 2 hours ago

System Message
You are a customer feedback analyst. Analyze the provided feedback data and extract actionable insights. Always structure your response with: - Themes with frequency percentages - Overall sentiment breakdown - Recommended next actions
User Message
Analyze the following customer feedback and identify the top themes. Focus on churn risk signals and areas requiring immediate attention. Feedback to analyze: {{feedback_data}}
Recent Activity
Chat[email protected]2 min ago
APIsystem14 min ago
Workflowfeedback-pipeline1 hr ago
MODEL
Claude Sonnet 4.5
CONNECTED TOOLS
Slack
Jira
Add connector
DEPLOY
ChatLive
Workflows3 active
APIEndpoint active
Structured Output
MODEL SETTINGS
Max Tokens20000
Temperature0.4
Thinking

Works with every model

  • Anthropic
  • OpenAI
  • Gemini
  • xAI
  • OpenRouter
  • Amazon
  • Perplexity
  • MoonshotAI
  • Meta
  • Qwen
  • DeepSeek

What is LLMOps?

LLMOps (Large Language Model Operations) is the set of practices for managing the full lifecycle of LLM-powered applications: prompt engineering, version control, testing, deployment, and monitoring. It's MLOps for the age of foundation models.

Build

Author prompts with variables, structured outputs, and model settings in a visual editor.

Test

Compare prompt performance across models side-by-side in Playgrounds.

Deploy

Ship to chat, API endpoint, webhook, or scheduled trigger with one click.

Monitor

Track usage, costs, and team adoption across every prompt and workflow.

Every stage of the LLMOps lifecycle, in one platform

Prompt versioning and rollback

Every prompt edit creates a version. Diff any two versions side-by-side, see who changed what, and roll back with one click. No more grepping through git history to find what changed.

Model-agnostic testing

Compare the same prompt across GPT-4, Claude, and Gemini simultaneously in Playgrounds. Find the best model for each task before you ship to production.

Workflow automation

Chain prompts, integrations, and logic into multi-step workflows. Trigger from webhooks, Slack, or schedules. Every workflow step can reference versioned prompts. Change a prompt once and every workflow using it gets the update.

Governance and monitoring

Role-based access control, audit logs, usage tracking, and model selection per team. Track costs, monitor adoption, and control who can do what.

Automate real work, not just demos

Build workflows that combine AI reasoning with your existing tools. Trigger from Slack, webhooks, or schedules.

Workflow Builder
Visual workflow builder with connected nodes

Classify and route incoming email

A prompt classifies email by intent and urgency. A condition node routes it to the right queue. The whole team can run it.

Connects to the tools you already use

Pull data from Slack, Google Drive, GitHub, and dozens more. Push results out and let AI coordinate across your stack.

SlackGitHubJiraGoogle DriveGmailOutlook MailPostgreSQLAWS BedrockAzure SQLAsanaAirtableGongPipedriveAffinitySlackGitHubJiraGoogle DriveGmailOutlook MailPostgreSQLAWS BedrockAzure SQLAsanaAirtableGongPipedriveAffinity
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LLMOps is simpler when everything is in one place.

Stop stitching together Langfuse for monitoring, LangSmith for testing, and custom scripts for deployment.

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