Yes. Each listing post can mark fields as public (asking, basic unit mix, year built) or restricted (T-12 NOI, rent roll, expense detail). The bot answers public fields openly and gates restricted answers behind an NDA flow, where it captures investor identity and routes the NDA link before disclosing underwriting detail. For deals where pricing is on-request, the bot can hold the ask back entirely until qualification completes.

The bot answers from structured fields on the listing post, not by parsing PDF offering memoranda directly. For best results, the underlying numbers (NOI, cap, expenses, rent roll summary) should be entered as named fields when you publish the deal, with the full OM PDF linked from the post. OpenAI Files vector store can index supplemental documents like market reports and comp sets for context, but the headline figures should live as structured fields.

The bot can describe the basic 1031 structure (held for investment, like-kind, 45-day identification, 180-day closing) and confirm whether a deal qualifies based on published criteria, but it does not give tax advice. For specific exchange questions, the bot routes the buyer to a qualified intermediary. The qualification flow captures exchange status, identification deadline, and target close to forward with the transcript so the broker can prioritize 1031 buyers correctly.

Yes, if you publish them. Existing loan UPB, interest rate, IO period, maturity, assumability, prepayment penalty, and lender consent requirements all read from named fields. The bot quotes the published terms and recommends the buyer's debt broker verify with the loan servicer before relying on the answer for closing. For assumable Fannie or Freddie loans, the bot can flag the rough timeline for lender approval if you have published it.

Yes. Multibot lets you scope chatbots per asset class. A multifamily chatbot emphasizes unit mix, loss-to-lease, and value-add thesis. A retail chatbot covers anchor co-tenancy and CAM. An industrial chatbot leads with clear heights and dock door count. A medical office chatbot covers tenant rosters and lease abstracts. Display conditions tied to asset-class taxonomy route each chatbot to the right deal pages.

Each deal post can store call-for-offers date, BOV deadline, best-and-final round, and award timing. The bot quotes the timeline, surfaces deals approaching the deadline, and captures preliminary interest with investor profile for the lead broker. For private negotiation deals without a public deadline, the bot frames the marketing as ongoing and prioritizes qualification over urgency.

If your WordPress site is the marketing source of truth for your investment listings, the bot reads each deal post directly. Most brokerages publish to CoStar, Crexi, and LoopNet via outbound feeds while keeping WordPress as the canonical content store, so the chatbot stays current with the brokerage's own marketing copy. The bot does not pull from third-party transaction databases like RCA or MSCI.

Yes. SleekAI uses your own API key with OpenAI, Anthropic, Google, or OpenRouter, and standard API terms exclude conversation data from model training. For brokerages handling institutional dispositions where seller confidentiality is critical, this keeps NDA flow, investor qualification, and deal interest inside your WordPress install. Conversation logs stay in your database, viewable in WP admin with model name, token use, and page URL.