Customer support
Resolve questions using documentation and support tools. Provide a route to human support when a query needs further attention.
Use cases / Text and agents
Chatbots and assistants
Build AI chatbots and assistants that know your business.
Answer customer and team questions with context from your products, services and documentation. Build an AI chatbot that helps people find information and take the next step.
With QDivZero, you can integrate language models into your website or application using an OpenAI-compatible API where supported. Add RAG with your data and choose the right LLM for each experience.
Conversations with context
Give your assistant the knowledge to help.
Create conversations connected to your sources and tools. Tailor the assistant to questions from customers, teams and users.
Team assistants
Query procedures and internal knowledge through conversation. Filter sources by user and retain references to current documentation.
In-product copilots
Guide users within the feature they are using. Provide the context needed to explain options and help them progress.
Specialist assistants
Scope instructions and sources to a specific domain. Define how the assistant responds to questions outside its documentation.
Build it with QDivZero
Connect the conversation to your context and models.
Supply instructions, relevant history and sources through Retrieval. Connect tools when the conversation needs current data; your application manages permissions, turns and handover to the team.
Your application flow
Message
Your application receives the question and relevant history.
Context
Add instructions and Retrieval information when needed.
Model
The LLM generates a response using available context.
Conversation
Display the response and keep state in your application.
Open-weight / Hugging Face
You can start with…
Choose an LLM for your languages, instruction following and conversation context. Compare answers with support questions and add tools or RAG when the assistant needs company information. An isolated correct answer does not describe the whole experience. Compare conversations pursuing the same objective, including incomplete questions and requests requiring another support channel.
Text and vision
Qwen3.8-27B
For conversation, code, and tasks combining text, images, and your own context.
View model on Hugging FaceMultimodal reasoning
DeepSeek V4.1 Flash
Explore reasoning and understanding of text and images in multi-step tasks.
View model on Hugging FaceText and vision
GLM-5.3 Flash
Combine conversation, visual understanding, and work with tools.
View model on Hugging FaceChatbots and assistants
Chatbots and assistants: frequently asked questions
How do I build an AI chatbot for my business?
Define the questions it should answer, prepare documentation, and choose a language model. Connect your interface to the model endpoint and add Retrieval for business context. Your application manages conversation history and system integrations.
Can I build a customer support chatbot with my own data?
Yes. Index product, service, and support documentation to retrieve context for each query. A RAG chatbot can generate answers using that content. Connect tools from your application for order queries or issue creation.
How do I integrate an AI assistant into a website or app?
Your website or app sends messages to the model endpoint and displays responses in a chat interface. Use OpenAI-compatible API clients for compatible models. Keep credentials on your application server and select context for each request.
How does a RAG chatbot differ from one without company data?
A RAG chatbot retrieves your document content before answering. An assistant without that retrieval relies on instructions, conversation history, and model knowledge. Retrieval adds company-specific information and current content.
Can I use an open-weight model for a private assistant?
Deploy a compatible Hugging Face LLM or your own model on QDivZero and connect it to your assistant. Your application manages users, conversations, and knowledge access. Choose the model and deployment configuration for your project requirements.
How much does an AI chatbot or assistant cost?
Cost depends on the model, capacity, conversation volume, and context per request. Retrieval or tools also change the system workload. Compare a representative configuration and review QDivZero pricing before sizing your assistant.
Can I build a multilingual customer service chatbot?
Yes, with a model that supports your languages and handles your business vocabulary well. Connect support documentation and evaluate real questions to refine instructions and context retrieval. Your application can hand a conversation to a person when the assistant lacks enough information.
How do I keep a business AI assistant up to date?
Update the content used by RAG when prices, features or procedures change, and review assistant instructions. Separate documentation from conversation logic so you can maintain each component. This lets you add knowledge without retraining the LLM or rebuilding the entire interface.
Ready to build an assistant that knows your business?
Create a chatbot with your data and bring it to your website or application. Choose a language model, connect your knowledge with Retrieval and build a support or information experience with QDivZero.