Knowledge preparation
Choose the approved pages, documents or structured content the assistant is allowed to use and define how that content will be refreshed.
Create a site assistant that answers from approved content, captures useful context and hands off when it should. We build chat experiences around retrieval, guardrails and clear scope rather than an ungrounded chatbot that tries to answer everything.
We treat AI as one component in a normal software system. The WordPress architecture, source data, permissions, user experience, business rules and fallback behaviour are defined alongside the model so the feature can be supported after launch.
Exact model providers and supporting services are selected around the use case, data requirements and the existing WordPress stack.
These are practical implementation areas for this service. Final scope depends on the systems, data and permissions involved.
Choose the approved pages, documents or structured content the assistant is allowed to use and define how that content will be refreshed.
Find the most relevant source material for each question before the model generates a response.
Build a responsive WordPress interface with clear loading, error, reset and escalation states.
Limit the assistant’s scope, add refusal or fallback behaviour and avoid presenting unsupported answers as facts.
Collect useful context and route the conversation to a form, email, CRM or support workflow when a person should take over.
Review common questions, failed retrievals and user feedback so the knowledge base and prompts improve over time.
A good use case has a defined user, trusted input, measurable output and an owner for review or exceptions.
Help visitors understand services, processes and next steps using the site’s approved information.
Answer pre-sales questions from structured product or documentation content and direct users to the right page.
Handle common documented questions while giving users an obvious path to human support for unresolved issues.
Ask a small number of relevant questions, summarise the requirement and pass it into the normal enquiry workflow.
The implementation moves from a bounded first version to broader automation only after real cases have been tested.
Define the current workflow, data sources, user and desired result.
Set the model task, system boundaries, permissions, validation and fallback behaviour.
Implement the WordPress code, integrations, interface and supporting services.
Run real examples, edge cases, failed requests and review scenarios in staging.
Release with monitoring, usage limits and clear ownership for exceptions.
Use real outcomes to improve retrieval, prompts, rules and the surrounding user experience.
The exact controls depend on the risk of the action, but these are normal considerations before a workflow is trusted in production.
Use the monthly AI Automation plan for WordPress AI Chatbot work, ongoing WordPress development and the integrations needed to keep the workflow useful after launch.
Designed for agencies that need recurring AI implementation capacity rather than a one-off prototype. Your developer can handle the WordPress or WooCommerce code, model and API integrations, workflow logic, testing, fixes and ongoing improvements across your client queue.
No long-term contract. White-label ready. One developer can support recurring work across multiple agency client projects.
Clear answers before the work starts.
Still unsure? Send us your current backlog.
Yes. A retrieval-based chatbot can search approved website or knowledge content and use the relevant passages as context for its answer.
It can be designed to link users back to relevant pages or source material, which is useful when the goal is to help visitors verify information or continue reading.
Yes. It can collect agreed fields, prepare a conversation summary and pass the information into a WordPress form, CRM or another supported handoff workflow.
The experience should have a clear fallback such as saying the information is not available, suggesting relevant pages or escalating to a person instead of inventing an answer.
Conversation logging and analytics can be added where appropriate, subject to the site’s privacy approach and the type of data being collected.
Send a real workflow, example input and desired result so the implementation can be scoped around what the system actually needs to do.