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Conversational AI
2025–Present

Virtual Assistant

Customer-service conversational agents deployed for retail brands including Natura and Avon, resolving customer questions and issues to cut down on human-agent interaction.

PythonDialogflowFastAPILangChain
01

Problem

Customer-support volume across multiple retail brands doesn't scale with human agents alone — most incoming questions are repetitive, but customers still expect an immediate, accurate answer.

02

Approach

Built brand-specific conversational agents trained on each brand's product and policy knowledge, handling common questions and issue resolution end-to-end, with handoff to a human agent reserved for cases the bot can't resolve confidently.

03

Architecture

Brand-specific knowledge base → conversational agent (Dialogflow/LangChain) → FastAPI backend for business-logic integration → confidence-based handoff to human agents.

04

Results

  • Deployed across multiple retail brands, including Natura and Avon.
  • Reduced human-agent interaction for common customer questions and issues.
05

Future work

Extending the same architecture to additional brands and further reducing handoff rate through better confidence calibration.