AI chatbot for WP Realty: chat across MLS listings
SleekAI maps the WP Realty listings table, including price, bedrooms, bathrooms, square feet, and city, so visitors can ask plain English questions about your MLS inventory. Bring your own OpenAI, Anthropic, Google, or OpenRouter key.
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MLS data deserves a human-sounding front door
WP Realty stores listings in a custom table rather than the WordPress posts table. That is great for performance with thousands of MLS records, but it is the reason most generic chatbots cannot help your site. They read posts and pages, they do not query wp_realty_listingsdb, and they have no idea what listingprice or bedrooms mean in your schema.
SleekAI handles this with a custom-table variable mapping. A developer writes one PHP filter that exposes the WP Realty table as a named context variable and the bot can now ask "give me the three lowest-priced 4-bed homes in Reno listed this month" and get a real answer pulled directly from the table.
For brokerages with hundreds or thousands of feed-imported listings, the value compounds. The bot can answer school district questions, walk through the difference between two similar homes, and keep the conversation flowing without paging the buyer through twelve archive pages. Generic chatbots simply cannot reach into a custom table.
Workflow
How the WP Realty bot works
Map the listings table
Add neighborhood knowledge
Route to the right agent
Iterate from the logs
Try it now
A typical WP Realty conversation
Comparison
Generic chatbot vs SleekAI for WP Realty
Generic chatbot
- Cannot read WP Realty's custom listings database table
- Has no concept of MLS price, status, or property type fields
- Cannot intersect city, beds, baths, and feature filters in one turn
- Forces buyers back to the search form when criteria change
- Never knows when a listing moves from active to pending
SleekAI chatbot
-
Reads the
wp_realty_listingsdbcustom table directly - Maps price, beds, baths, sqft, city, and status as variables
- Supports MLS feed updates without re-importing the bot context
- Handles 1,000+ listing inventories without slowing chat replies
- Logs which MLS numbers buyers ask about most often
Features
What SleekAI gives you for WP Realty
Custom table support
WP Realty stores everything outside the posts table. SleekAI's variable mapping exposes that table to the model as a named context so the bot reads real prices and bed counts on every reply, not stale snapshots.
MLS feed friendly
Listings rotate fast. The bot queries on demand instead of preloading a snapshot, so price drops and status changes show up in the next chat reply within seconds of the importer running.
Neighborhood smart
The system prompt holds your local knowledge about school districts, commute times, and HOA quirks. The bot blends that with the live MLS data so answers feel like a junior agent who knows the city.
Use cases
Where MLS-driven brokerages benefit
Relocation specialists
Out-of-state buyers ask broad geography questions before they shortlist. The bot frames neighborhoods and price bands in one turn, then narrows to specific MLS numbers in the next.
High-volume brokerages
When your feed has 2,000 active listings, no one reads page three of search results. The bot finds the right three homes from the full set every time, not just the top of the archive.
Buyer agent teams
Each listing is tied to a buyer agent in the WP Realty schema. The bot can route the conversation to the right team member when the visitor commits, with the MLS number attached.
The bigger picture
Why MLS sites need conversational search
MLS feeds are unforgiving to UX. A new buyer arrives, sets bedrooms to 3, hits search, and gets 184 results. They pick the wrong sort order, scroll for two minutes, and bounce.
The data was there but the funnel asked the buyer to be an expert before showing the inventory. A chat that reads the same MLS table flips that experience. The buyer arrives, types one sentence, and gets three homes.
They ask about schools and get a real comparison. They ask about a backyard and get a real lot size. By the time they request a tour, they have done the work of a fifteen-minute phone call without occupying a single agent minute.
Most brokerages do not think of their site as an answer machine. They think of it as a brochure with a search form bolted on. WP Realty is a great engine for that brochure-plus-search model, but it leaves a gap between the buyer's natural language and the search form's required fields.
SleekAI closes that gap. The bot does not need to know everything in the MLS. It just needs to read the active rows and write a useful sentence about three of them.
The team behind the brokerage stays the deal-makers. The bot covers the work no one wants to do, which is repeating the same five answers about Somersett schools and dog-friendly HOAs to every visitor at 11 pm.
Questions
Common questions about SleekAI for WP Realty
A developer adds a PHP filter that exposes the wp_realty_listingsdb table as a named context variable. SleekAI passes the rows to the model on each turn so answers always match the live database. The setup is about ten lines of code.
 No. The bot only queries when a visitor asks a relevant question, and the query is filtered server-side before tokens ever hit the model. A 2,000-listing site adds about 40 ms per query, well below the model latency.
 Yes. WP Realty stores the listing agent ID per row. SleekAI can filter by that ID via a context variable so individual agents can run their own page bots with only their inventory.
 The variable mapping is a plain PHP array. Rename any field to match the WP Realty schema in your installation. The bot only sees the friendly names you assign, not the raw column names.
 Yes, if you store open house dates in a related table or column. Expose them as a variable and the bot can answer "any open houses this weekend in 89523" with the correct subset.
 Filter by the listing status column in your context query. Most brokerages limit the bot to active and coming-soon, hiding pending and sold from chat-driven search results.
 Indirectly. The bot can quote sample monthly payments using a simple formula with the listing price, the asked-about down payment, and a rate variable you set in PHP. It will not replace a licensed loan officer.
 Custom table queries pull only a few rows per turn, so token use stays modest. Expect 1,500 to 4,000 tokens per full conversation, which is around 1.5 cents on gpt-4o-mini. Logs show the exact per-message cost.
 Pricing
More than 1000+
happy customers
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