How do you prevent the chatbot from hallucinating?

We use RAG (retrieval-augmented generation) to ground responses in your actual documentation. We also implement guardrails that detect when the model is uncertain and trigger a "I don't know" response or human handoff instead of guessing.

How much does it cost to run an AI chatbot?

LLM API costs depend on usage volume and model selection. For a typical support chatbot handling 1,000 conversations per day, expect $200-500/month in API costs. We optimize with prompt caching, model routing, and token management.

Can the chatbot take actions like creating tickets?

Yes. We implement tool calling so the chatbot can query databases, create support tickets, look up order status, update account settings, and perform other actions during the conversation.

Deploy an AI Chatbot That Actually Helps Your Users

Bookuvai builds context-aware AI chatbots with RAG knowledge retrieval, tool calling, and streaming responses that resolve queries instead of deflecting them.

Feature: AI Chatbot

AI chatbots powered by large language models can handle customer support, product guidance, and internal knowledge queries with human-like understanding. Unlike rule-based chatbots, LLM-powered bots understand natural language, retrieve relevant context from your documentation, and perform actions through tool calling. Bookuvai builds production chatbots with proper guardrails, cost management, and continuous quality improvement.

Benefits

Instant Customer Support: Resolve 60-80% of support queries instantly with AI that understands your product, documentation, and common issues without human intervention.
Context-Aware Responses: RAG-powered retrieval grounds responses in your actual documentation, ensuring accurate answers instead of hallucinated information.
Actionable Conversations: Tool calling enables the chatbot to perform real actions: look up orders, create tickets, update settings, and process requests during the conversation.
Continuous Learning: Analytics and feedback loops identify gaps in knowledge and response quality, enabling continuous improvement of the chatbot over time.

How It Works

Knowledge Base Ingestion: We ingest your documentation, FAQs, help articles, and product data into a vector database for semantic retrieval during conversations.
Chatbot Architecture Design: We design the chatbot with appropriate LLM selection, prompt engineering, guardrails, and tool definitions for your specific use case.
Streaming Chat UI: We build a polished chat interface with streaming responses, message history, suggested questions, and conversation threading.
Guardrails and Quality Assurance: We implement content moderation, topic boundaries, hallucination detection, and human handoff triggers for queries the AI cannot handle.

Technology Options

Vercel AI SDK + OpenAI: Production-ready AI chat with streaming responses, tool calling, and structured output using the Vercel AI SDK with OpenAI models. (Best for: Next.js applications needing a polished chatbot with minimal setup)
LangChain + Custom RAG: Advanced chatbot with custom retrieval pipelines, multi-step reasoning, and agent capabilities using LangChain with any LLM provider. (Best for: Complex use cases needing multi-step reasoning, agents, or custom retrieval)
Custom Pipeline + Anthropic Claude: A custom chatbot pipeline using Anthropic Claude models with tool use, long-context windows, and safety-focused responses. (Best for: Applications requiring long conversations, safety focus, or Claude-specific features)

Estimated Hours

Simple: 30-50 hrs | Moderate: 50-100 hrs | Complex: 100-200 hrs

Frequently Asked Questions

How do you prevent the chatbot from hallucinating?: We use RAG (retrieval-augmented generation) to ground responses in your actual documentation. We also implement guardrails that detect when the model is uncertain and trigger a "I don't know" response or human handoff instead of guessing.
How much does it cost to run an AI chatbot?: LLM API costs depend on usage volume and model selection. For a typical support chatbot handling 1,000 conversations per day, expect $200-500/month in API costs. We optimize with prompt caching, model routing, and token management.
Can the chatbot take actions like creating tickets?: Yes. We implement tool calling so the chatbot can query databases, create support tickets, look up order status, update account settings, and perform other actions during the conversation.