Choose the content
Review your website pages and support documents. Identify the authoritative version of each answer and separate public information from material that requires authentication.
The right information
Useful answers start
with usable sources.
We begin with the questions visitors actually ask and the information your team approves for answering them. That defines the chatbot's knowledge and the gaps that need a human response.
Prepare document search
Structure the approved content so the assistant can find relevant passages. Where required, include source links that let a visitor or staff member check an answer.
Handle missing answers
Define the response when sources do not answer a question. The assistant can ask for useful context or direct the visitor to the agreed support route.
Keep content current
Agree on who updates the sources and how changes reach the chatbot. Recheck important questions when policies or product information change.
Website integration
Build the conversation
into the visitor's journey.
A chatbot is part of the product interface. Its controls and support route need as much attention as its answers.
A chat experience that fits
Use your established colors and typography. Check the launcher and conversation panel on narrow screens. Make the input and controls usable with a keyboard as well as a pointer.
The integration should make sending and waiting states clear. It also needs a usable error state so a failed request does not leave the visitor wondering what happened.
A defined support handoff
Decide when a conversation should move to a person. Agree on the contact route and the information that needs to accompany the request.
A handoff may be a contact link or an integration with your support system. The scope depends on the tools your team uses and their available APIs. We confirm that connection before promising automated ticket creation or live staff transfer.
Implementation walkthrough
The assistant on
the oSyntax website.
Our website assistant is a working example of the chat integration. Open it on this page to inspect the interface and its conversation flow.
From a website question to the next step
Open and askVisitors can choose a suggested question or write their own. The assistant opens within the site and adapts to a narrow screen, so the conversation fits the page they are using.
Send the requestThe message is sent with the current page context. The interface shows that a response is being prepared and keeps the conversation visible while the visitor waits.
Show the resultThe reply can link to relevant site content. Visitors can request a team handoff with the conversation attached. If sending fails, the message returns to the input for another attempt. This walkthrough shows our website implementation; each customer's knowledge sources and support connections are agreed during discovery.
Delivery and evaluation
Review the answers.
Test the complete journey.
The acceptance criteria should cover content quality and the visitor experience. Your team needs a clear way to review both before launch.
Collect real questions
Choose representative enquiries with expected answers and source references. Include questions the chatbot should not answer from the available content.
Build and connect
Implement the website widget and the agreed knowledge source. Keep server credentials outside browser code and enforce any required access boundaries.
Evaluate and refine
Compare answers with the approved material. Check source references and missing information. Exercise the support route and the experience after a failed request.
Prepare handover
Agree on deployment and account ownership. Document the source update process and responsibility for usage costs, support and future changes.
Common questions
Planning a website chatbot.
Can the chatbot use our documents?
Yes, the project can include search across approved source material. We review its format and access requirements first. The scope should specify which sources are included and how their updates become available to the assistant.
Does this require training a new model?
Often the first step is connecting an existing model to relevant content. That lets the application retrieve current information for a question. Model training is a separate decision based on the task and evaluation results.
What happens when it cannot answer?
We define that behavior with your team. It may ask a clarifying question or direct the visitor to support. Tests should include missing sources and questions outside the chatbot's scope.
Can it create tickets or change records?
Those actions need a supported API and explicit permission rules. A chat interface can be connected to an action workflow when that is part of the scope. See AI agent implementation for how actions and approvals are handled.
Can you add it to an existing website?
We review the website's framework and available access. The widget can then be scoped around its layout and deployment process. We check the affected pages on desktop and narrow screens as part of the integration.
What should we send for an estimate?
Share the website address and a sample of the source material. Include typical questions and your intended support route. Tell us whether visitors need authentication or access to account information.
Your website
What do your visitors need to know?
Send your website and a few common questions. We will use them to define the knowledge sources and support behavior your chatbot needs.