How to Deploy a Custom OpenAI Assistant to your Website or Messaging Channel

Customer Support

Nobody likes waiting on hold. AI assistants provide instant responses, reducing ticket volumes and improving resolution times. They handle FAQs, troubleshoot issues, and escalate complex queries to human agents when needed.

Education

AI assistants can answer questions, explain concepts, and provide real-time feedback, acting as on-demand tutors for students. Platforms use AI to offer interactive lessons and quizzes, making learning more engaging.

Healthcare

Patients need quick access to information about symptoms, appointments, and medications. AI-powered assistants help schedule appointments, provide health advice (within guidelines), and offer mental health support through chat-based counseling.

Sales and Lead Generation

For tech companies, AI chatbots act as sales assistants, answering product questions, qualifying leads, and scheduling demos. They also assist existing users by troubleshooting software issues and guiding them through features.

1. Build it Directly with OpenAI

OpenAI previously introduced the Assistants API, but as of April 2024, its development has stalled, and it remains in beta with no clear roadmap. Instead, OpenAI has shifted its focus to GPTs, allowing users to create tailored AI assistants inside the ChatGPT platform.

However, GPTs aren't deployable on external websites—they stay within OpenAI’s ecosystem. If you want a chatbot embedded in your site, you’ll need to use the OpenAI API and build your interface.

2. Use a No-Code AI Assistant Builder

For those who want a quick and scalable solution without heavy coding, no-code AI assistant platforms offer an easy way to deploy chatbots. These platforms provide:

• Pre-built integrations with OpenAI and other AI models.
• Multi-channel deployment on websites, messaging apps, and customer support tools.
• Built-in workflows for managing user interactions, memory, and automation.

Some examples include Botpress, Voiceflow, and Chatbot.com, among others. These tools are ideal for businesses that need a functional AI chatbot without deep development work.

3. Build a Custom AI Assistant from Scratch

For full control, developers can integrate OpenAI’s API with AI agent frameworks like LangChain, vector databases, and custom UI components. This allows advanced features like:

• Retrieval-augmented generation (RAG) for personalized responses.
• Custom integrations with databases, CRMs, and internal tools.
• Advanced memory handling beyond OpenAI’s built-in capabilities.

This method has a learning curve but can often be outsourced to development teams.

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Each option has trade-offs, but if you want an AI assistant fully embedded in your website, using OpenAI’s API or a no-code AI assistant builder is the way to go.

Next, let’s walk through how to build and deploy your OpenAI assistant step by step.

Step 1: Set up your Project

Create a new project in Botpress. Once logged in, select 'Start from Scratch' for full customization and flexibility.

Step 2: Write Clear Instructions

Instructions are crucial for your AI assistant’s behavior, use this opportunity to change the following aspects of how the chatbot interacts with the user and the information.

1. Open the "Instructions" section in Studio.
2. Define how the assistant should behave:

• Tone and format – Should it be formal, casual, concise, or detailed?
• Knowledge access – What tools, APIs, or data sources should it use?
• Prohibited behavior – What responses should it avoid?

3. Save the instructions to apply them to your chatbot.

Step 3: Choose an OpenAI Model for AI Assistant

Botpress Studio allows you to select from different OpenAI models based on your needs:

1. Open Bot Settings from the main menu.
2. Scroll down to LLM options.
3. Select a GPT model based on your needs (e.g., GPT-4o mini for speed and cost efficiency).

Different models offer varying trade-offs between cost, speed, and response quality, so choose one that fits your needs. Botpress doesn’t add extra fees on top of LLM provider pricing—check GPT model pricing here.

Step 4: Add Knowledge Sources

To make your AI assistant effective, connect it to relevant external knowledge, such as PDF documents, website URLs, or APIs. Here's how you can do it:

1. Go to the “Knowledge Base” section from the menu.
2. Upload documents or add URLs of websites as sources.
3. The platform should then index the content automatically, segmenting it into smaller, retrievable knowledge chunks.

This ensures that your assistant can pull accurate, up-to-date information when responding to users.

Step 5: Deploy your Chatbot

Once your assistant is configured, it’s time to publish and embed it on your website:

1. Click 'Publish' in the top-right corner.
2. Go to "Customize Webchat" and then select "Share".

1. Copy the embed code and paste it into your website’s HTML.

Your OpenAI assistant is now live, ready to assist users in real-time.

1. Experiment with Different Model Offerings

Not all AI models perform the same across different tasks. Try different options to balance token usage, user experience, and cost efficiency:

• Switch to a more cost-effective model – If your assistant primarily handles basic queries, consider using a cheaper conversational model like DeepSeek-V3 instead of GPT-4o.
• Use a reasoning-optimized model for complex tasks – Models like OpenAI o1 or o3 provide better logical processing if your assistant needs advanced reasoning or long-context understanding.

2. Enhance Workflows with Autonomous Nodes and AI Transitions

A single prompt-response setup limits what your AI can do. Instead, deepen the assistant’s workflow by:

• Using autonomous nodes to let the AI handle multi-step interactions without manual triggers.
• Implementing AI transitions to guide conversations dynamically based on user input.
• Integrating tool access and APIs to fetch real-time data and enhance responses.

3. Optimize AI Spend and Token Usage

• Monitor AI usage trends to see where tokens are being spent inefficiently.
• Adjust response length and verbosity to reduce token consumption without losing clarity.
• Implement caching or memory features to avoid redundant API calls for repetitive queries.

Fine-tuning these elements helps reduce costs, improve user experience, and make your OpenAI assistant smarter over time.