How are vitiv.ai chatbots different from generic chatbot builders?

vitiv.ai chatbots are trained on your specific business data — products, policies, FAQs, pricing, and processes — so they give accurate, context-aware answers rather than generic responses. They understand your customers's actual questions and know exactly when to escalate to a human agent rather than guessing.

Which platforms can vitiv.ai chatbots be deployed on?

vitiv.ai deploys chatbots on websites, WhatsApp Business, Slack, Microsoft Teams, Instagram Direct, Facebook Messenger, SMS, and custom API integrations. One AI model, consistently trained, delivers the same accurate experience across every channel simultaneously.

Can a chatbot handle sales and lead generation, not just support?

Yes. vitiv.ai builds sales-focused chatbots that qualify leads, answer product questions, book meetings, and collect contact details — generating pipeline 24/7 without requiring your sales team to be online. Leads are logged and routed to your CRM automatically.

How long does it take to build and deploy a chatbot?

A basic chatbot trained on your documentation and deployed on your website can be live in 1–2 weeks. A full-featured, multi-channel assistant with CRM integration and escalation flows typically takes 3–4 weeks, including training, testing, and deployment across all channels.

What happens when the chatbot does not know the answer?

vitiv.ai builds intelligent escalation paths — the chatbot recognizes when a query exceeds its knowledge, collects the customer's contact details and issue summary, and routes the conversation to the right human team member via your CRM, email, or messaging platform.

What is RAG and why is it used in chatbots?

RAG (Retrieval-Augmented Generation) is the architecture that enables a chatbot to answer questions about your specific business accurately. Instead of relying on an LLM's general training data (which does not know your products or policies), RAG retrieves relevant excerpts from your knowledge base in real-time and passes them to the LLM as context before generating a response. The result is accurate, grounded answers with references to your actual documentation — eliminating hallucinations about your specific product details, prices, or policies.

06Service

Chatbots & Assistants

Conversational interfaces trained on your data, deployed across every channel.

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Overview

Generic chatbots frustrate users. Ours are trained on your actual business data — your products, your policies, your FAQ, your processes. They understand context, handle complex queries, and know when to escalate to a human.

Deploy them everywhere your customers are: your website, WhatsApp, Slack, Microsoft Teams, SMS, or custom integrations. One AI brain, every channel, consistent experience.

Key Capabilities

→Custom-trained on your knowledge base and documentation

→Multi-channel deployment — web, WhatsApp, Slack, Teams

→Seamless handoff to human agents when needed

→Continuous learning from conversations and feedback

See it liveRelaytiv — our AI front desk product for WhatsApp, Instagram, Messenger, and web chat.

Why choose us

What you get

Your Data, Your Bot

Trained on your specific knowledge base, products, and processes for accurate responses.

Everywhere At Once

Deploy across web, WhatsApp, Slack, Teams, and SMS from a single AI brain.

Smart Handoff

Automatically escalates to human agents when the conversation needs a personal touch.

Always Improving

Learns from every conversation and feedback loop to get smarter over time.

How it works

Our process

Knowledge Base Preparation

3–5 days

We ingest and structure your knowledge sources — product documentation, pricing PDFs, FAQ documents, policy manuals, past support tickets, and website content — into a vector database with chunking and embedding optimized for your query patterns. Any gaps in coverage are identified and flagged for content creation.

Conversation Design

4–6 days

Design the conversation architecture: intent classification, entity extraction, escalation triggers, handoff conditions, and the response tone calibrated to your brand. We map the top 50 query types your customers actually ask and test response accuracy against each.

Build & Channel Integration

7–14 days

Implement the RAG-powered conversational AI with your preferred channels: web widget (WhatsApp Business API, Slack, Microsoft Teams, or custom integration). Connect to CRM for lead capture and context lookup. Escalation routes established: conversation summary and contact details routed to your team via CRM or email.

Training & Continuous Improvement

3–5 days + ongoing

Launch monitoring tracks resolution rate, escalation rate, user satisfaction, and coverage gaps. Monthly knowledge base refresh from new content. Quarterly full review of conversation patterns to retrain intent detection and expand coverage of emerging query types.

Measurable impact

Typical outcomes

80%+ of routine queries resolved without human agent involvement

24/7 availability across all deployed channels from day 1

60% reduction in average support ticket resolution time

Technology stack

OpenAI GPT-4oAnthropic Claude 3.7LangChainPineconeSupabaseWhatsApp Business APISlack APIMicrosoft TeamsTwilioReact (web widget)

FAQ

Common questions

Related comparisons

Chatbot vs AI Agent

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Let's discuss how chatbots & assistants can transform your business.

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