Intent Moderation in AI+ Studio helps you classify, filter, and act on incoming messages based on their intent and relevance. It combines moderation and intent detection into a single configurable solution. You can define how messages should be interpreted, categorized, and routed to human agents across your workflows.

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What is Intent Moderation

Intent Moderation is an AI-based message classification framework that identifies both:

• Whether a message is relevant or actionable (engageable)
• What the message is about (intent)

You can:

• Create custom intent taxonomies aligned to your business
• Customize prompts using AI+ Studio
• Select which taxonomy to apply within a specific rule engine
• Use outputs in reporting and workflows

Intent Moderation helps classify messages based on their purpose or meaning rather than just keywords. 

Why it Matters

Organizations receive high volumes of inbound and listening messages. Traditional keyword-based filtering is often noisy and requires manual cleanup.

Intent Moderation addresses key challenges:

1. High volume of irrelevant messages

Large volumes of noise, or off-topic mentions reduce data quality and increase manual effort. 

2. Inefficient routing and delayed response

Without intent-level classification, messages are routed incorrectly or require manual triaging. 

3. Limited context awareness

Basic classification models fail to consider parent post context, leading to inaccurate labeling and low trust. 

4. Manual effort in dataset cleanup

Teams spend significant time filtering and organizing data instead of analyzing insights.

How Intent Moderation Works

Intent Moderation uses a two-layer pipeline to improve accuracy, speed, and decision-making.

Stage 1: Moderation (Engageability detection)

• Classifies messages into engageable vs non-engageable
• Filters out noise or irrelevant content
• Ensures only meaningful messages proceed for deeper analysis

Stage 2: Intent classification

• Identifies the purpose of the message

• Assigns:

  • Intent label
  • Confidence score

Routing and Action

• The Rule Engine uses classification results to:

  • Route messages to the right teams
  • Trigger workflows
  • Assign cases to agents

This layered approach improves accuracy and reduces unnecessary processing. 

Key Capabilities

Intent Moderation provides a flexible and configurable framework:

• Custom intent taxonomy

• Define your own intents based on business needs

• Align classification with teams, workflows, and reporting structures

• Prompt customization

• Customize AI prompts to improve intent detection accuracy

• Adapt models to industry-specific terminology

• Rule Engine integration

• Use classification outputs to trigger routing and automation

• Maintain control over final decisions

• Reporting and insights

• Analyze message trends by:

  • Intent category
  • Engageability

• Use insights to improve decision making and operations

Business Use Cases

Customer support routing

Automatically route messages such as complaints, billing issues, or service requests to the correct support queues.

Social media moderation

Filter out spam, abusive, or irrelevant comments before they reach moderation teams.

Listening data enrichment

Classify large volumes of listening data into actionable categories to improve analysis and reporting.

Case creation and prioritization

• Identify high-priority issues
• Create cases only for relevant messages
• Reduce noise in case management workflows

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Smart assignment

Assign messages to the right agent or team based on detected intent and category. 

Relevant Personas

Analysts

• Automatically filter irrelevant and low-value messages
• Reduce manual effort in dataset cleanup
• Focus on meaningful insights and trends

Brand and Moderation Managers

• Classify messages with full context, including conversation history
• Improve routing accuracy and operational efficiency
• Build trust in AI-driven decisions with structured outputs

Example Scenarios

Scenario 1: Customer complaint routing
A customer posts about a billing issue.

• Stage 1: Message marked as engageable
• Stage 2: Classified as “Billing Complaint”
• Result: Routed to the billing support team

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Scenario 2: Spam filtering in listening
A social mention contains promotional spam.

• Stage 1: Classified as non-engageable
• Result: Filtered out and not processed further

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Scenario 3: Multi-intent categorization
A message includes both complaint and refund request.

• Stage 2: Detects intent with confidence score
• Result: Routed to appropriate escalation workflow

When to Use Intent Moderation

Use Intent Moderation when you need to:

• Reduce noise in large datasets
• Improve routing accuracy and speed
• Replace keyword-based ML classification with contextual AI application
• Standardize message categorization across teams
• Enable scalable, automated workflows

Enablement and Access

Intent Moderation is currently available through controlled enablement.

To request access:

1. Contact your Success Manager or Account Executive
2. Share your use case and expected workflows
3. The Sprinklr team will assist with enablement and initial configuration

With Intent Moderation enabled, you can automatically filter and classify messages at scale, ensuring that large volumes of data are processed efficiently. It helps route messages accurately to the appropriate teams, reducing delays and improving response time. By minimizing manual effort in moderation and analysis, your teams can focus on higher-value tasks. Overall, this leads to improved operational efficiency and more informed decision-making.