What is Intent Recognition?

Intent recognition is an AI capability that determines what a user is trying to accomplish from their message. It goes beyond keywords to understand the underlying purpose, enabling appropriate responses regardless of how the request is phrased.

How Intent Recognition Works

1. Input processing: User message is analyzed
2. Feature extraction: Key signals identified
3. Classification: Message mapped to known intents
4. Confidence scoring: Certainty level calculated
5. Action routing: Appropriate response triggered

Examples of Intent Recognition

Same intent, different phrasings:

• Where is my order?
• I have not received my package
• Can you track shipment 12345?
• When will my stuff arrive?

All map to: Order Tracking Intent

Intent vs Entity

• Intent: What the user wants to do (track order, get refund)
• Entity: Specific details needed (order number, product name)

Both are needed for complete understanding.

Intent Recognition Accuracy

Modern systems achieve:

• High-frequency intents: 95%+ accuracy
• Medium-frequency: 85-95% accuracy
• Long-tail intents: 70-85% accuracy

Challenges

• Ambiguity: Messages with unclear purpose
• Multi-intent: Users asking multiple things
• Context dependency: Meaning changes with context
• New intents: Requests not in training data

Beyond Classification

Modern AI agents do not just recognize intents - they resolve them:

• Understand intent
• Extract required entities
• Take appropriate action
• Confirm resolution

Maven AGI Difference: Our intent recognition combines LLM understanding with your domain knowledge. We do not just classify - we resolve. This powers the 90%+ resolution rates customers like Mastermind and K1x achieve.

Book a demo to see intent-driven resolution.