Chatbot Qualification & Routing Playbook
Analyses inbound chat conversations to ensure correct intent detection, qualification accuracy, and routing decisions. Supports improved lead conversion and operational efficiency.
Start building
Deploy this kit stack into your workspace. Customize bricks, scoring, and outputs to match your team.
Without this playbook
Most teams handle chatbot qualification & routing through scattered call reviews, manager opinion, and isolated examples. Without a shared operational definition, the signals stay inconsistent and difficult to act on across volume.
With this playbook
A shared, repeatable lens for chatbot qualification & routing - with structured outputs you can route into coaching, reporting, and workflow automation. Every conversation produces evidence, not just opinions.
Built for
AI product managers, ML engineers, and trust & safety teams
When teams use it
- Model evaluation and release gates
- Governance review and policy enforcement
- Safety and accuracy monitoring
The operational stack
1 kit behind this playbook
A chatbot that qualifies leads incorrectly or routes them to the wrong team wastes pipeline and frustrates prospects. This stack evaluates the three stages of inbound chat handling: intent detection to determine whether the bot correctly understood what the visitor wanted, qualification accuracy to check whether it extracted the right signals, and routing decisions to verify the lead ended up with the right team. Each stage is a separate failure point with a separate fix.
Chat Intent Detection Kit
3 bricks
Classifies chat intent and accuracy for digital channels.
Included bricks
Customize this kitChat Intent Type
CategoryClassifies chat intent lead support info or complaint
Intent Confidence Score
ScoreScores the confidence of intent classification
Chat Entities Detected
String listExtracts key entities tied to chat intent for routing
Knowledge base
Supporting materials
The kits in this playbook work best when backed by reference materials that ground the evaluation. Upload these into your workspace knowledge base to improve accuracy and relevance.
Learn more about Knowledge BasesChat intent taxonomy and classification criteria
Lead qualification criteria and scoring thresholds
Routing rules and team assignment logic
Chatbot conversation flows and decision trees
Lead conversion benchmarks and routing performance data
Structured output
What you get back
Every conversation processed through this stack produces a structured JSON object. Each brick contributes a typed field - booleans, scores, categories, or string lists - that you can route, aggregate, and report on.
Example output shape
{
"chat_intent_type": "Strong",
"intent_confidence_score": 7,
"chat_entities_detected": [
"signal 1",
"signal 2"
]
}In practice
How teams use these outputs
The structured outputs from this stack integrate into your existing workflows. Use them wherever you need repeatable, evidence-based signal from conversations.
Model evaluation and release gates
Governance review and policy enforcement
Safety and accuracy monitoring
AI agent performance benchmarking
Get started
Deploy this playbook in your workspace
Customizing creates a workspace-owned draft with this playbook's full kit stack. Adjust bricks, scoring, and outputs to fit your team, then publish when ready.