customer_support · workflow
Lessons learned from building over 50 chatbots on 5 continents
Most chatbots fail due to poor implementation: bots trap users in 'not understood' loops for out-of-scope questions, generate false positives that mislead users, and respond unhelpfully rather than routing them toward a solution.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Define scope and train NLP
A chatbot project starts by agreeing on information architecture, defining knowledge and responses, and training the NLP model.
Tools used
NLPCampfire AI
Outcome
(not stated)
What failed first
Common failure modes include bots asking for information users already provided, rephrasing loops that can't resolve out-of-scope questions, and dead-end 'Sorry, I don't know' responses that add frustration rather than helping.
Results
Volumemore than 50
Grounding & classification
Source type: listicle or blog summary
16 fields verified against source quotes.
chatbotconversational aifailure mode describedmetric backedtools describedworkflow describedaccuracy improvementlisticle or blog summarycustomer supportescalation workflowintake to triage