A business owner asks an AI assistant to find a website company that can handle a particular integration. They also want relevant examples before booking a call.
What would the assistant find on your site?
If the answer is a slogan, unlabeled screenshots and “Contact us to learn more,” it may have little to work with. A human visitor would face the same problem.
Start by making the business easy to understand. Then consider whether there is a useful action an assistant could help someone complete.
Put important facts where they can be read
Explain which services you offer and the projects they fit. Describe your actual work in portfolio examples.
If you can publish pricing guidance, make the conditions clear. If you cannot, explain what determines the scope. Give someone enough information to know what a first conversation involves.
Important details should appear in readable page text. Do not make a visitor watch an animation to discover the service area. Do not expect software to infer a capability from an image filename.
Descriptive headings and stable links help people find the answer again. They also give systems clearer material to retrieve.
Structured data can describe facts in a machine-readable format. Keep it consistent with the visible page. It cannot supply evidence that the business has never provided.
Search access and action access are different
An assistant may use a search index, read a page directly or connect to a tool. Those routes have different requirements.
Google's AI features guidance explains the role of ordinary SEO practices in its search features. OpenAI's crawler documentation describes different types of crawler access.
Check the documentation for the systems you intend to support. Do not assume one setting covers every use.
Allowing a crawler does not guarantee a recommendation. A page that can be found is also not automatically a page an agent can use to book an appointment.
Keep those goals separate when planning the work. It will make the implementation easier to test.
Choose an action that would actually help
Suppose the assistant identifies a suitable provider. The user then asks whether a consultation time is available.
A page explaining office hours may not answer that. A reliable availability tool could.
Other possible tasks include:
- Returning current product specifications.
- Preparing a requirements summary for human review.
- Producing a preliminary estimate with stated assumptions.
- Checking which service options fit supplied requirements.
These are design possibilities. They are not claims that RCL currently exposes all of these functions to outside agents.
Start with a task customers already need. Define the information required and what result the system can responsibly return.
Keep the underlying process dependable
Exposing a broken estimator through an API will not improve its calculations. A calendar connection cannot fix availability that nobody maintains.
Test the process with people first. Make sure it returns accurate results and handles missing information.
Then decide whether another application has a real reason to use it.
A documented API can support a defined software interaction. The Model Context Protocol describes a way compatible AI applications can connect with tools and data.
That does not mean every small business needs an MCP server. Build an integration when a supported application will use it and someone will maintain it. A technical interface without a useful task behind it adds little.
Separate a lookup from a commitment
Checking an appointment time is different from booking it. Preparing a quote request is different from accepting an offer.
Make those distinctions explicit in the interface. An important commitment should require the appropriate authorization.
The result should say what actually happened. If a booking failed, return a failure. If a price could not be calculated, return that limitation instead of a guess.
Define a route to a person when the task falls outside the system's scope. Keeping that route can make the service more useful, especially for unusual requests.
Test a real question and a bounded action
Choose a buying question your team hears regularly. Check whether a person can answer it from the public site without contacting you.
Then choose an action, such as preparing a request. Identify the smallest reliable process that would help someone complete it.
Test an incomplete input as well as a correct one. Check how the result distinguishes a draft from a confirmed action.
RCL's article on agents doing work explains the difference between answering and acting. Your website needs to support the other side of that exchange with accurate information and dependable processes.
You can begin with clearer service content today. Add tool access when you have a specific use for it, not because someone promises universal “AI readiness.”
