Yes. SleekAI's custom-table data source maps wp_fc_subscribers, wp_fc_lists, wp_fc_tags, wp_fc_funnels, and wp_fc_campaigns into named variables in the system message. Variables resolve at request time, so a new subscriber or a fresh campaign send appears in the next answer without an additional sync layer.

Yes. Match the logged-in WordPress user to their fc_subscriber row by email and map that row into the prompt. The bot can then answer questions like which lists am I on, what tags do I have, and when did I last open a campaign, all without exposing data to other users.

You need an account with at least one supported provider. SleekAI works with OpenAI, Anthropic, Google, and OpenRouter, so the marketing team can pick a fast model for short metrics queries and a stronger one for funnel analysis under the same chatbot. Usage stays on your provider account.

Yes. Display conditions cover URL pattern and user role. The common setup is a marketer bot under the wp-admin URL path for the administrator and marketing manager roles, and a customer bot on the public preferences page for logged-in subscribers, both reading from the same fc_ tables.

Only the data you map into the system message reaches the model. For a customer-facing bot, scope variables to the current subscriber's row only. For an admin bot, you can map aggregate counts and the campaign metrics you choose, without exposing individual contact fields you do not want sent.

Yes. SleekAI supports OpenAI Files vector stores of up to one gigabyte per file, which lets you push a snapshot of contacts or campaign history into the model's retrieval index. The bot then retrieves only the rows it needs at query time rather than stuffing them all into the prompt.

Yes. With the marketer bot in wp-admin you can ground the model in the brand voice and recent top-performing campaigns from wp_fc_campaigns. The bot drafts subject lines and short copy variants for the marketer to edit, with awareness of which content has worked recently on this list.

Yes. Conversation logging stores each user message, the assistant reply, the model name, tokens used, and the originating page URL. For FluentCRM that URL is usually a specific report or contact screen, which gives leadership visibility into how the team is using the bot.