CASE STUDY / 03
Conversational support engine
Resolve common support requests at volume while preserving a route for complex questions.
Responsibility
The résumé describes building a customer-support chatbot with Rasa and transformer models during the Cognizant role.
Architecture at a glance
- Customer request
- Intent routing
- Response or fallback
- Support handoff
Reported outcome
50K+ reported daily interactions
Interaction volume describes workload, not unique users or resolved conversations. The PDF separately reports an 85% resolution rate.
The counting unit, observation window, resolution definition and baseline are not published. Volume and resolution should not be combined into an inferred count of successful cases.
Read the source résumé (PDF)Technical deep dive
Design decision
Use intent routing and fallbacks to keep common requests efficient and make unresolved conversations actionable.
Alternatives and tradeoffs
A generative-only assistant can handle flexible language, but needs grounding and escalation safeguards. Structured flows trade conversational flexibility for predictable handling.
Engineering takeaway
Evaluate handoff quality and repeated contacts as well as resolution rate. A completed conversation does not automatically mean the customer problem was solved.