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AI · Conversational AI

AI Chatbot

A large share of support volume consisted of the same handful of questions, but a generic chatbot had previously damaged trust by confidently inventing answers that were wrong.

Project type
Conversational AI
Industry
Customer support
Domain
AI
Delivery
Web channel first, then messaging channels

Technologies

  • Python
  • FastAPI
  • LLM APIs
  • PostgreSQL
  • Next.js
  • WhatsApp API
Grounding
Approved content
Fallback
Human handover
Channels
Web, app, WhatsApp

These describe scope and architecture. We do not publish performance figures we cannot substantiate.

Business problem

A large share of support volume consisted of the same handful of questions, but a generic chatbot had previously damaged trust by confidently inventing answers that were wrong.

Our approach

The assistant only answers from an approved content set, and every response is checked against a confidence threshold. Below that threshold it says it does not know and offers a handover rather than guessing. Unanswered questions feed a content backlog.

Result

Routine questions are resolved without an agent while genuinely novel ones reach a person quickly with the conversation attached. Gaps in documentation surface as data instead of as complaints.

What was built

Key features

The capabilities that mattered most to the outcome.

  • Answers grounded in approved company content
  • Clean escalation to human agents with full context
  • Web, in-app and WhatsApp channels from one core
  • Confidence thresholds with safe fallback replies
  • Conversation analytics on unanswered questions
Engineering

Technologies used

Chosen for the requirement rather than by habit — we explain the trade-offs before building.

Python
FastAPI
LLM APIs
PostgreSQL
Next.js
WhatsApp API

Your project would not necessarily use the same stack. We choose per requirement, and we will explain the reasoning before anything is committed.

Next step

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Seyffluo Technologies

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