01
Customer support
Answer common questions from approved material and make it clear when a conversation needs a human.
AI chatbot development
Give people useful answers. Give your team control.
An AI chatbot needs more than a chat window. It needs the right information, useful integrations, and a clear way to handle questions it cannot answer.
Discuss your project Concept illustration
Where we help
Chatbots for customer support, knowledge and product workflows.
We develop AI chatbots for customer support, internal knowledge, and product workflows. We start with the questions the assistant should handle, the sources it can rely on, and when it should involve a person.
02
Internal knowledge search
Help teams find information across selected documents and systems, with access rules carried through to retrieval.
03
Product assistance
Guide users through a product or collect the context needed for the next step in a workflow.
04
Business integrations
Connect the assistant to relevant tools. Separate looking up information from taking an action on someone’s behalf.
Engineering decisions
Decide what a good answer looks like before launch.
Define the source material
We identify what the assistant may use, who maintains it, and how updates reach the chatbot.
Choose the model and retrieval approach
We weigh answer quality, response time, cost, and data-handling requirements. Retrieval is useful when answers need current or private information.
Check answers and handoffs
Representative questions, missing information, and failed integrations help define the evaluation and fallback behavior.
Relevant experience
Built on our AI and application delivery experience.
Stax3’s documented work includes AI chat and customer-engagement products, alongside the web applications and backend integrations they depend on. Client names and detailed product information stay private.
See our wider AI servicesUseful answers
Before we start.
Can the chatbot use our documents?
Yes, where the documents are suitable and access is authorized. We review their structure, update process, and permissions before choosing how to retrieve relevant information.
Which AI provider would you use?
The choice follows the use case. We consider model quality, latency, cost, deployment constraints, and the provider’s current data-handling terms rather than selecting a model only by its brand.
Can it take actions in our systems?
That can be part of the scope. Actions need explicit permissions, validation, and a way to handle failures. Higher-impact steps may also need human approval.
Is this the same as a custom ChatGPT integration?
No. A chatbot can run in your website or application. A ChatGPT integration brings your tools or workflows into ChatGPT. The delivery surface and access model are different.
Start with the context
Tell us what needs to work better.
A goal, an existing product, or a difficult workflow is enough to start a useful conversation.
Start a conversation→