How does live handoff work?

When the bot escalates, it sends the full conversation transcript to the configured live chat system (Zendesk, Intercom, Freshdesk). The agent sees the complete context and can continue the conversation seamlessly.

How does multilingual support work?

The system auto-detects the user's language at the start of the conversation. The LLM is instructed to respond in the detected language. Separate knowledge base collections can be maintained per language for highest accuracy.

How is the deflection rate calculated?

Deflection rate is the percentage of chatbot sessions that were resolved without human intervention. It's tracked per intent category and overall, helping you identify which topics still need bot improvement.

Technology & SaaS

Build a AI Chatbot Platform with AI - in Hours

ProjectCode generates a complete, production-ready AI Chatbot Platform from a single natural language prompt. In hours instead of months, you get a React frontend, NestJS REST API, PostgreSQL database, and authentication - fully integrated, live-previewed, and deployed to a URL you own.

Estimated build time: 2–4 hours

Build my AI Chatbot now View pricing

Free to start · No credit card required

The Challenge

Why building a AI Chatbot from scratch is hard

Building a AI Chatbot Platform from scratch requires months of development time and significant engineering resources. Teams struggle to coordinate frontend, backend, database, and authentication while managing ai chatbot-specific business logic, integrations, and role-based access rules.

What's Included

Everything your AI Chatbot needs

ProjectCode generates a production-ready AI Chatbot with all the modules your team needs from day one.

No-code chatbot builder with intent and flow designer

Knowledge base integration with semantic search

Multi-channel deployment: website, WhatsApp, Slack, Teams

Live handoff to human agent with context transfer

Multilingual conversation support with auto-detect

Conversation analytics: deflection rate, CSAT, unresolved intents

A/B testing for chatbot responses

Webhook and API integration for backend actions

Architecture

How it's built

The generated AI Chatbot follows production-grade architecture patterns.

React 18 + TypeScript frontend with role-based dashboards tailored for ai chatbot workflows

NestJS REST API with dedicated ai chatbot modules, DTOs, validators, and service layer

PostgreSQL relational schema with normalized tables for all ai chatbot entities

JWT + Google OAuth authentication with granular permission gates per role

Real-time Socket.IO events for live ai chatbot status updates and notifications

Managed hosting with one-click deploy, custom domain, and auto-SSL

Database

PostgreSQL schema designed for AI Chatbot

ProjectCode generates a normalized, indexed PostgreSQL schema with the right tables and relationships for your use case. You own the schema and can evolve it freely.

chatbots (id, workspace_id, name, model, language, persona jsonb, knowledge_base_id, handoff_config jsonb)
sessions (id, chatbot_id, channel, user_identifier, started_at, ended_at, deflected, csat_score, transferred_to_human)
messages (id, session_id, role, content, intent, confidence, tokens, created_at)
intents (id, chatbot_id, name, examples jsonb, response_type, response_content, follow_up jsonb, escalation_trigger)

schema.sql (preview)

-- Generated by ProjectCode
-- AI Chatbot Platform schema

CREATE TABLE chatbots__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

CREATE TABLE sessions__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

CREATE TABLE messages__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

CREATE TABLE intents__id (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW()
  -- ... auto-generated columns
);

Tech Stack

Production-ready from day one

Every generated app uses a battle-tested stack you can deploy anywhere and own completely.

Frontend

React 18 + TypeScript + Tailwind CSS

Backend

NestJS 10 + Node.js + TypeScript

Database

PostgreSQL (managed or Supabase)

Auth

Email OTP · Google OAuth · GitHub OAuth

Deployment

Managed hosting · Custom domain · Auto-SSL

AI Models

Claude Sonnet · Claude Opus · Kimi

Try It Now

Example prompt to get started

Copy this prompt into ProjectCode and get a working app in minutes.

ProjectCode prompt

Build an AI chatbot platform for a customer service team handling 10,000 chats per month. Include a no-code flow designer for common intents (order status, returns, billing questions), knowledge base integration using semantic search over support docs, multi-channel deployment (website widget and WhatsApp), live handoff to Zendesk when the bot can't resolve, multilingual auto-detection for English, Spanish, and French, a deflection rate dashboard, A/B testing for bot responses, and a webhook to query the order management system in real time.

Build this now - it's free

Deployment

Deploy your AI Chatbot anywhere

Managed hosting

One-click deploy to a live URL managed by ProjectCode.

Custom domain

Connect your own domain with auto-provisioned SSL via Cloudflare.

GitHub export

Push to your GitHub repo and self-host or deploy via Vercel, Railway, or AWS.

Docker ready

Export a Dockerfile and docker-compose for any containerized environment.

Pricing

Start free, scale as you grow

No per-seat fees. No vendor lock-in. Token-based pricing that scales with your usage.

Free

Start building your first app. Live preview, managed hosting, and full code export included.

Token Packs

From $5

Purchase token packs that roll over every month. Build more, pay only for what you use.

Enterprise

Custom

Custom models, SLA, SSO, and private cloud deployment for large teams.

See full pricing details

FAQ

Common questions about building a AI Chatbot

Support documents are chunked and embedded as vectors in a pgvector database. When a user asks a question, the query is embedded and matched against the knowledge base using cosine similarity. The top-matching chunks are used to generate the answer.

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Start free. No credit card. Your first app is live in minutes.

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