AI chatbot for Schema Pro: answers grounded in your structured data
SleekAI reads the same fields Schema Pro uses to build JSON-LD, so the bot answers with the entities, properties, and types you already maintain. Bring your own OpenAI, Anthropic, Google, or OpenRouter key.
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A chatbot that reads the same structured data Schema Pro outputs
Schema Pro maps page fields, custom fields, and ACF values to schema properties like Article author, Product price, Review rating, or Event start date. Those mappings live in postmeta and plugin options. SleekAI's Wizard reads them through data sources, so the chatbot's system message includes the same property-value pairs Schema Pro pushes into your JSON-LD.
That gives the bot a structured view of the site rather than a flat dump of HTML. The model can answer that an article was written by a specific author, that a product is priced at a specific amount, or that an event runs on a specific date, all using the schema mappings you already maintain. When you tweak a mapping inside Schema Pro, the next chat reply picks up the change without a sync.
For sites with many schema types, multibot lets you scope a different chatbot to articles, products, and events, each with its own subset of fields. Display conditions handle the routing, and conversation logging captures every question, the model name, and the page URL so the team can audit how visitors actually use the structured data the site emits.
Workflow
How SleekAI plugs into Schema Pro
Mirror the field map
Scope per type
Bring your own key
Audit and refine
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A typical Schema Pro conversation
Comparison
Generic chatbot vs SleekAI for Schema Pro
Generic chatbot
- Has no view of your schema mappings
- Can't answer with structured properties
- Doesn't know which posts are Article, Product, or Event
- Treats every field as free text
- No display conditions per schema type
SleekAI chatbot
- Reads the same fields Schema Pro maps to JSON-LD
- Answers with structured properties (author, rating, date)
- Multibot per schema type (Article, Product, Event)
- Display conditions per post type and taxonomy
- Conversation logs with model name and URL
Features
What SleekAI gives you for Schema Pro
Property-aware
Schema Pro tells you which postmeta or ACF field maps to which schema property. SleekAI reads the same map, so the bot can answer with the right property at request time.
Per-type bots
Run separate chatbots for Article, Product, Event, and other schema types using display conditions for post type, category, and URL pattern. Each bot sees only the properties relevant to its type.
Audit logs
Every conversation saves inside WordPress with model name and page URL. Use the logs to find structured-data questions visitors ask that your current mappings do not cover yet.
Use cases
Where teams use SleekAI for Schema Pro
Editorial sites
An article bot answers with the real author, publish date, and headline pulled from the same fields Schema Pro emits as JSON-LD, which keeps the chat answer aligned with what search engines see.
Product catalogues
A product bot answers with price, currency, availability, and review rating using the same property mappings Schema Pro pushes into Product schema, so the chat experience matches the rich result.
Event sites
An event bot answers with start date, end date, location, and organiser using Event schema fields, which keeps reservation and waitlist questions grounded in the actual event data.
The bigger picture
Why structured data is the right grounding for chat
Schema markup exists because search engines need a structured view of a page rather than a freeform paragraph. Chatbots have the same need. When the model knows that a value is the author, the price, the start date, or the rating, the answer it produces stops feeling like a paraphrase and starts feeling like a direct fact lookup.
Schema Pro already does the hard work of mapping site fields to schema properties for crawlers. Reusing that map for the chatbot means the team does not have to maintain two parallel definitions of what counts as the author, the price, or the event date. The chat layer inherits the same structure.
When the JSON-LD is updated, the chat replies update too. That is much easier to govern over time than two separate sources of truth, and it keeps the chatbot honest about which facts come from where, which matters for trust on review-heavy or pricing-heavy pages.
Questions
Common questions about SleekAI for Schema Pro
It reads the underlying fields that Schema Pro mapped. Schema Pro itself stores a mapping between schema properties and source fields, and the source fields live in wp_postmeta or ACF. SleekAI's Wizard maps those source fields into the chatbot's system message as named variables, so the bot answers using the same values Schema Pro uses to build JSON-LD.
Yes, if you map the schema-type column. Schema Pro tracks which posts use which schema type, and the Wizard can expose that as a variable. The bot can then route its reply based on whether a post is an Article, Product, Event, or any other type you maintain, which keeps the language relevant to each one.
Yes, if your reviews live in fields that Schema Pro reads, such as a postmeta key or ACF field. SleekAI can expose the same field, so the bot returns the average rating and review count for a product or course. The data path is identical to what Schema Pro uses for AggregateRating.
Yes. Schema Pro encourages ACF for custom property mappings, and ACF stores its data in wp_postmeta with predictable keys. SleekAI reads ACF either through direct postmeta keys or through the ACF helper, so the bot has clean access to the same fields Schema Pro reads.
Yes, when both read the same source field. There can be a difference if a property has a fallback chain inside Schema Pro that the chatbot's mapping does not mirror. The safest pattern is to point both at the same canonical field, so chat replies and rich results stay aligned by default.
Yes. Conversation logs include the page URL and the system prompt context, so you can see whether visitors are asking about articles, products, or events. That helps focus future schema work on the types people actually ask about, instead of marking everything up evenly.
Yes. The bot does not care which schema type a post is, it cares which fields it can read. Map the union of properties you need across types, let the bot use whichever ones are populated for a given post, and the same chatbot can answer Article, Product, and Event questions.
Push the product-relevant fields plus their schema properties into an OpenAI Files vector store of up to one gigabyte per file. The model retrieves only the rows that match a query at request time, so the chatbot scales without ballooning the prompt or the cost per chat.
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