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AI & Automation

Multi-Channel AI Support Chatbot

An LLM-backed chatbot handling customer support and order queries across business websites, built for real ByteForce clients.

Difficulty — advanced Complexity — 7/10 Category — AI & Automation

Overview

A conversational AI support system deployed across multiple client websites to automate first-line customer interaction — answering FAQs, checking order status, and escalating complex queries to a human agent instead of leaving visitors waiting on email. Built to plug into each client's existing site and backend without a rebuild.

Architecture

A webhook-driven pipeline: incoming messages from a website widget or messaging channel hit an n8n workflow, which routes the query to an LLM with business-specific context (product catalog, FAQ data, order lookup), then returns a response through the same channel — with an escalation path back to a human when confidence is low.

Key Features

  • Context-aware responses grounded in each client's product catalog and FAQ content, not generic answers.
  • Order-status and account lookups via API calls back into the client's store/CRM.
  • Automatic handoff to a human agent when the bot detects a query it can't confidently resolve.
  • Reusable workflow template adapted per client rather than rebuilt from scratch each time.

Development Process

  • Mapped each client's most common support queries before designing the conversation flow.
  • Built the core n8n workflow once, then parameterized it per client (catalog, tone, escalation rules).
  • Tested against real customer questions pulled from existing support inboxes.
  • Iterated on prompt structure to reduce incorrect or overconfident answers.

Challenges & Solutions

Challenge

Generic LLM responses ignored client-specific policies (returns, shipping, stock), producing answers that sounded right but were wrong.

Solution

Grounded every response in retrieved business-specific data before generation, rather than relying on the model's general knowledge.

Challenge

Knowing when the bot should hand off to a human, instead of confidently guessing, was as important as the answers themselves.

Solution

Added explicit confidence and escalation logic so uncertain queries route to a human agent instead of a bad answer.

Results & Impact

Deployed across multiple client websites as part of ongoing work at ByteForce IT Solutions, reducing first-response time on common support queries and cutting down repetitive manual replies for client teams.

Future Improvements

  • Add multilingual support for broader client bases.
  • Layer in analytics on deflection rate vs. human escalation.
  • Extend to WhatsApp/Messenger as additional channels.